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Travel Demand Model ReportCity of Peterborough

Comprehensive Transportation Plan UpdateSupporting Document

Paradigm Transportation Solutions Limited43 Forest RoadCambridge ON

N1S 3B4

Prepared for:City of Peterborough

and Morrison Hershfield

June 2012

PROJECT SUMMARY

PROJECT NAME: .......................................................... CITY OF PETERBOROUGH COMPREHENSIVE TRANSPORTATION PLAN UPDATE SUPPORTING DOCUMENT TRAVEL DEMAND MODEL REPORT CLIENT: ......................................................................... MORRISON HERSHFIELD LIMITED 2440 DON REID DRIVE OTTAWA ON K1H 1E1 CLIENT PROJECT MANAGER: ................................................... BASSAM G. HAMWI, M.ENG., P.ENG.. PRINCIPAL & MANAGER TRANSPORTATION PLANNING PH: 613-739-3241 FAX: 613-739-4926 CONSULTANT: ..................................... PARADIGM TRANSPORTATION SOLUTIONS LIMITED 43 FOREST ROAD CAMBRIDGE ON N1S 3B4 PH: 519-896-3163 FAX: 1-866-722-5117 CONSULTANT PROJECT MANAGER ....................................... JAMES MALLETT, M.A.SC., P.ENG., PTOE REPORT DATE: .............................................................................. JUNE 2012 PROJECT NUMBER: ........................................................................... 081030

Peterborough Comprehensive Transportation Plan Update | Travel Demand Modelling Report | June 2012 | 081030

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EXECUTIVE SUMMARY

Paradigm Transportation Solutions Limited has prepared this Transportation Demand Modelling Report on behalf of Morrison Hershfield and the City of Peterborough. Paradigm Transportation solutions was part of the project team headed by Morrison Hershfield that was commissioned by the City of Peterborough to provide and update to the City’s 2002 Transportation Master Plan.

This report provides an overall review of the current model, its identified limitations, presents a comprehensive plan for addressing these shortcomings. In doing so, the inherent assumptions, procedures and processes used in this update are included.

In addition, the report provides information with respect to the model network performance in the planning horizon years establish for the study (2006, 2021 and 2031)



CONTENTS

1.0 EXISTING MODEL FRAMEWORK .................................................................................. 1

1.1 BACKGROUND .............................................................................................................. 1 1.2 STUDY GOALS AND OBJECTIVES ......................................................................................... 1 1.2.1 ESSENTIAL / HIGH PRIORITY ............................................................................................................................ 3 1.2.2 DESIRABLE / MEDIUM PRIORITY ....................................................................................................................... 3 1.2.3 POTENTIAL FOR FUTURE EXPANSION / LOW PRIORITY ............................................................................................ 3 1.3 MODEL OVERVIEW ASSESSMENT ........................................................................................ 4 1.4 KNOWN ISSUES AND CONSTRAINTS ..................................................................................... 4 1.5 ACTION ITEMS FOR UPDATE .............................................................................................. 5 1.5.1 MODELLING FRAMEWORK .............................................................................................................................. 5 1.5.2 TRIP GENERATION ........................................................................................................................................ 6 1.5.3 TRIP DISTRIBUTION AND MODE SPLIT ................................................................................................................ 6 1.5.4 TRIP ASSIGNMENT ....................................................................................................................................... 7 1.5.5 MODELLED SPEEDS ...................................................................................................................................... 7

2.0 MODEL FRAMEWORK ENHANCEMENTS ......................................................................... 8

2.1 DATA SOURCES ............................................................................................................ 8 2.2 TRAFFIC ANALYSIS ZONE RESOLUTION .................................................................................. 8 2.3 MODEL NETWORK RESOLUTION .......................................................................................... 8 2.4 CENTROID CONNECTORS ................................................................................................ 10 2.5 NETWORK ATTRIBUTES ................................................................................................. 13 2.5.1 PHYSICAL ATTRIBUTES (LENGTH, LANES, AND POSTED SPEED) ................................................................................ 13 2.5.2 FUNCTIONAL CLASSIFICATION AND PLANNING CAPACITY ....................................................................................... 13 2.5.3 LINK DELAY ESTIMATES ............................................................................................................................... 15

3.0 MODELLING PROCESS ............................................................................................ 17

3.1 TRANSPORTATION DEMAND MODELLING ............................................................................. 17 3.2 TRANSPORTATION MODEL OVERVIEW ................................................................................. 17

4.0 LAND USE .......................................................................................................... 20

4.1 DATA SOURCES .......................................................................................................... 20 4.2 LIMITATIONS AND ASSUMPTIONS ...................................................................................... 20 4.3 TAZ FRAMEWORK ....................................................................................................... 20

5.0 TRIP GENERATION ................................................................................................. 21

5.1 DATA SOURCES .......................................................................................................... 21 5.2 METHODOLOGY ........................................................................................................... 21 5.2.1 COMPARISON TO 2002 MODEL .................................................................................................................... 22 5.2.2 2006 TRIP GENERATION RATE COMPARISON .................................................................................................... 22 5.3 CALIBRATION SUMMARY ................................................................................................ 38

6.0 TRIP DISTRIBUTION AND MODE SPLIT ......................................................................... 39

6.1 DATA SOURCES .......................................................................................................... 39 6.2 METHODOLOGY ........................................................................................................... 39 6.3 GRAVITY MODEL CALIBRATION ......................................................................................... 41 6.4 MODE SPLIT AND AUTO OCCUPANCY ................................................................................. 42

7.0 EXTERNAL PASSENGER VEHICLE TRAFFIC .................................................................... 43

7.1 DATA SOURCES .......................................................................................................... 43 7.2 METHODOLOGY ........................................................................................................... 43


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8.0 MODEL SPEEDS AND VDF EQUATIONS ....................................................................... 44

8.1 DATA SOURCES .......................................................................................................... 44 8.2 METHODOLOGY ........................................................................................................... 44 8.3 VDF DEVELOPMENT ..................................................................................................... 47 8.4 VDF CALIBRATION AND VALIDATION .................................................................................. 47 8.4.1 COMPARISON TO EXISTING VDF FUNCTIONS ..................................................................................................... 49 8.4.2 EXISTING VDF CALIBRATION CONCLUSION ........................................................................................................ 49 8.4.3 ADOPTED VDF ENHANCEMENTS .................................................................................................................... 49 8.5 RESULTS .................................................................................................................. 49

9.0 ASSIGNMENT .................................................................................................... 512

9.1 METHODOLOGY ........................................................................................................... 52 9.2 VALIDATION ............................................................................................................... 53 9.2.1 SYSTEM-WIDE VEHICLE MILES OF TRAVEL (VMT) .............................................................................................. 53 9.2.2 SYSTEM-WIDE TRAFFIC VOLUMES .................................................................................................................. 54 9.2.3 CORRIDOR VOLUMES .................................................................................................................................. 57 9.2.4 LINK-SPECIFIC CALIBRATION ....................................................................................................................... 623

10.0 MODEL VALIDATION CONCLUSION AND FUTURE ENHANCEMENTS ..................................... 64

10.1 VALIDATION CONCLUSION ............................................................................................. 64 10.2 FUTURE MODEL ENHANCEMENTS .................................................................................... 64 10.2.1 TEMPORAL MODELS ...................................................................................................... 64 10.2.2 SPECIAL GENERATORS .............................................................................................................................. 64 10.2.3 EXTERNAL TRAVEL DEMANDS ..................................................................................................................... 64

11.0 BASE YEAR (2006) CONDITIONS ............................................................................ 65

11.1 DEFICIENCY DEFINITION ................................................................................................ 65 11.2 BASE YEAR (2006) NETWORK PERFORMANCE ................................................................... 66 11.3 BASE YEAR (2006) NETWORK LINK DEFICIENCIES ............................................................... 68

12.0 LAND USE FORECASTS ......................................................................................... 70

12.1 BACKGROUND .......................................................................................................... 70 12.2 POPULATION AND EMPLOYMENT PROJECTIONS .................................................................... 70

13.0 FORECAST CONDITIONS ........................................................................................ 77

13.1 TRAVEL DEMAND INCREASES ......................................................................................... 77 13.1.1 INTERNAL-BASED TRAVEL DEMANDS .......................................................................... ERROR! BOOKMARK NOT DEFINED. 13.1.2 EXTERNAL TRAVEL DEMANDS ..................................................................................................................... 81 13.1.3 TOTAL TRAVEL DEMANDS ....................................................................................... ERROR! BOOKMARK NOT DEFINED. 13.2 COMMITTED ROAD NETWORK IMPROVEMENTS ............................... ERROR! BOOKMARK NOT DEFINED. 13.3 FUTURE (2031) NETWORK PERFORMANCE ....................................................................... 83 13.4 FUTURE (2031) NETWORK PERFORMANCE ................................. ERROR! BOOKMARK NOT DEFINED. 13.5 PERFORMANCE TRENDS .............................................................................................. 89 13.5.1 Arterial and Collector Performance Trends ............................................................................................. 86

APPENDICES

Appendix A – Population and Employment Growth Projections

Appendix B – Travel Demand Matrices



FIGURES

FIGURE 1.1: CURRENT MODEL LIMITS ...................................................................................... 2 FIGURE 2.1: TRAFFIC ANALYSIS ZONES ..................................................................................... 9 FIGURE 2.2: MODEL NETWORK ............................................................................................ 11 FIGURE 2.3: CENTROID CONNECTOR EXAMPLE (DOWNTOWN PETERBOROUGH) ..................................... 12 FIGURE 3.1: CITY OF PETERBOROUGH MODELLING PROCEDURE ....................................................... 19 Figure 5.1: 2002 Model PM Peak Hour Auto Trip Generation Equations ................................ 24 FIGURE 5.2: 2002 MODEL AUTO TRIP GENERATION EQUATIONS VS. 2006 TTS OBSERVED TRIPS ............. 25 FIGURE 5.3: 2002 MODEL AUTO TRIP GENERATION EQUATIONS VS. 2006 TTS .................................. 26 FIGURE 5.4: COMPARATIVE DIFFERENCES FEBRUARY 2009 AND AUGUST 2009 DATA .......................... 27 FIGURE 5.5: 2006 REVISED TRIP GENERATION EQUATIONS VS. 2006 TTS OBSERVED TRIPS ................... 28 FIGURE 5.6: TOTAL PREDICTED TRIPS VS. TOTAL OBSERVED TRIPS (2006) ......................................... 30 FIGURE 5.7: TOTAL PREDICTED TRIPS VS. TOTAL OBSERVED TRIPS (PRODUCTIONS AND ATTRACTIONS) ......... 31 FIGURE 5.8: PM PEAK HOUR AUTO TRIP CALIBRATION HBW PRODUCTIONS PREDICTED VS. OBSERVED ....... 32 FIGURE 5.9: PM PEAK HOUR AUTO TRIP CALIBRATION HBW ATTRACTIONS PREDICTED VS. OBSERVED ........ 33 FIGURE 5.10: PM PEAK HOUR AUTO TRIP CALIBRATION HBO PRODUCTIONS PREDICTED VS. OBSERVED....... 34 FIGURE 5.11: PM PEAK HOUR AUTO TRIP CALIBRATION HBO ATTRACTIONS PREDICTED VS. OBSERVED ....... 35 FIGURE 5.12: PM PEAK HOUR AUTO TRIP CALIBRATION NHB PRODUCTIONS PREDICTED VS. OBSERVED ...... 36 FIGURE 5.13: PM PEAK HOUR AUTO TRIP CALIBRATION NHB ATTRACTIONS PREDICTED VS. OBSERVED ....... 37 FIGURE 6.1: SAMPLE TRAVEL IMPEDANCE FUNCTION ................................................................... 40 FIGURE 8.1: TRAVEL TIME SECTIONS STUDIED ........................................................................... 46 FIGURE 8.2: OBSERVED SPEED VS. V/C RATIO ......................................................................... 48 FIGURE 8.3: OBSERVED AVERAGE TRAVEL SPEED VS. V/C AND EXISTING VDF FUNCTIONS ....................... 50 FIGURE 8.4: OBSERVED AVERAGE TRAVEL SPEED VS. V/C AND VDF FUNCTIONS ................................... 51 FIGURE 9.1: PM PEAK HOUR AUTO TRIP CALIBRATION PREDICTED VS. OBSERVED .................................. 56 FIGURE 9.2: MAXIMUM ALLOWABLE DEVIATION ACROSS SCREENLINES .............................................. 58 FIGURE 9.3: SCREENLINES .................................................................................................. 59 FIGURE 9.4: NORTH-SOUTH SCREENLINE CALIBRATION VOLUMES (PM PEAK HOUR) ............................... 60 FIGURE 9.5: EAST-WEST SCREENLINE CALIBRATION .................................................................... 62 FIGURE 11.1: BASE YEAR PERFORMANCE MEASURES .................................................................. 67 FIGURE 11.2 2006 PM PEAK HOUR NETWORK LOS ................................................................ 69 FIGURE 12.1: SUPER ANALYSIS ZONE (SAZ) STRUCTURE ............................................................. 72 FIGURE 12.2: PROJECTED GROWTH IN POPULATION AND EMPLOYMENT (2006 TO 2031) ...................... 73 FIGURE 12.3: RELATIVE GROWTH IN POPULATION AND EMPLOYMENT (2006 TO 2031) ......................... 74 FIGURE 12.4: STUDY AREA POPULATION AND EMPLOYMENT TRENDS ............................................... 76 FIGURE 13.1: 2006 PM PEAK HOUR INTERNAL TRAVEL DEMANDS ................................................ 78 FIGURE 13.2: 2021 PM PEAK HOUR INTERNAL TRAVEL DEMANDS ................................................ 79 FIGURE 13.3: 2031 PM PEAK HOUR INTERNAL TRAVEL DEMANDS ................................................ 80 FIGURE 13.4: 2021 PERFORMANCE MEASURES ....................................................................... 84 FIGURE 13.5: 2021 PM PEAK HOUR NETWORK LOS ................................................................ 85 FIGURE 13.6: 2031 PERFORMANCE MEASURES ....................................................................... 87 FIGURE 13.7: 2031 PM PEAK HOUR NETWORK LOS ................................................................ 88 FIGURE 13.8: VKMT AND VHT GROWTH TRENDS ..................................................................... 90 FIGURE 13.9: ARTERIAL AND COLLECTOR SYSTEM ...................................................................... 91 FIGURE 13.10: ARTERIAL AND COLLECTOR PERFORMANCE (VKMT) ................................................. 92 FIGURE 13.11: ARTERIAL AND COLLECTOR PERFORMANCE (VHT).................................................... 93


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TABLES

TABLE 2.1: PLANNING CAPACITIES ........................................................................................ 14

TABLE 2.2: PLANNING CAPACITY ADJUSTMENTS MADE DURING CALIBRATION PROCESS .......................... 14

TABLE 2.3: BPR VARIABLES ............................................................................................... 16

TABLE 6.1: TRIP DISTRIBUTION CALIBRATION ............................................................................ 41

TABLE 6.2: MODE SHARE AND AUTO OCCUPANCY ...................................................................... 42

TABLE 8.1: AVERAGE LOADED SPEEDS VS. AVERAGE PEAK SPEED WITHIN FUNCTIONAL CLASSIFICATIONS ..... 47

TABLE 11.1: PLANNING CAPACITIES ...................................................................................... 65

TABLE 11.2: LEVEL-OF-SERVICE AND V/C RELATIONSHIP .............................................................. 66

TABLE 13.1: TOTAL TRAVEL DEMAND INCREASES ...................................................................... 81

TABLE 13.2: NETWORK ASSUMPTIONS WITHIN GROWTH AREAS ..................................................... 82


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1.0 EXISTING MODEL FRAMEWORK

1.1 Background

The City of Peterborough (COP) has maintained and continues to refine a transportation planning model which has been used to forecast future travel conditions along the City of Peterborough Roadway System. The model has undergone several revisions since its development in the mid-1980's. In 2003, a City of Peterborough model was developed using TransCAD software based on 1996 weekday PM peak hour conditions. Two horizon years (2011 and 2021) were developed for this model using land use forecasts provided by the City's Planning Department and officially endorsed by City of Peterborough Council.

Since the study completion in 2002, the original TransCAD model has undergone revisions and updates. The first revision involved changes to the base assumptions as well as refinement of the modeling procedures in the West-Side corridor Analysis Review in 2003. The model remained an automobile-based model. As a result of work performed by the County of Peterborough, refinements were made and the TransCAD model was revised to incorporate additional information related to the County of Peterborough. Finally, the model has been adjusted to reflect the most recent land use forecast prepared by the City’s Planning Department. Following this latest revision, travel demand scenarios were developed for 2011, 2021 and 2031. Traffic volumes were assigned to the horizon year networks and the resultant traffic volume forecasts were used to determine the travel demands on the Peterborough road network.

The City's model has two key components: a supply component (road network) representing the characteristics of all of the significant roadways in the area and a demand component (trip matrix) representing the typical weekday PM peak hour traffic volume that flows through the network.

The core area of the model update will be within the boundaries of the model area which extends into the County of Peterborough. However, significant transportation links to other municipalities (external links) are an important part of City's transportation infrastructure.

The existing City of Peterborough model coverage shown in Figure 1.1 covers the entire City of Peterborough and includes area municipalities such as Lakefield and Bridgenorth. These municipalities vary from the populated centres with urban intensive features, to the Townships of Ottonabee and South Monaghan with a more rural & pastoral area setting. Tourism, industry and farming, as well as all the natural resources including mineral resources and environmental resources, make up Peterborough's economic diversity.

1.2 Study Goals and Objectives

The primary goal of this study is to update the City of Peterborough model such that it can assist in variety of municipal decision making processes. These decision making processes were grouped into three categories based on their level of priority. The three categories are:

Essential;

Desirable; and

Potential for Future Expansion.



Figure 1.1: Current Model Limits

City of Peterborough Model Update Figure 1.1

Model LimitsParadigmwww.ptsl.com

City of Peterborough Limit



1.2.1 Essential / High Priority

The ongoing work in Peterborough requires a suitably rigorous travel demand forecasting model that is capable of providing forecasts to be used in the following types of studies:

Long Range Transportation Plans - For estimating major corridor deficiencies and- travel demand needs for new major facilities;

Land Use and Growth Management Plans - Far evaluating transportation impacts of major changes in future land use and development;

Area Municipal Transportation Plans - For determining municipal long range transportation plans; and

Transportation Corridor studies - For evaluating alternative solutions within the identified corridor and also for providing sufficient output to aid in design of preferred alternative.

1.2.2 Desirable / Medium Priority

In addition to the above minimum requirement, the ability to provide transportation planning input into the following types of studies is viewed as highly desirable:

Traffic lmpact Assessments - For evaluating the impacts of proposed development on existing transportation facilities and services, for identifying improvements to transportation facilities and accommodating travel demands associated with development proposals;

Travel Demand Management Strategies - For evaluating alternative methods of reducing or accommodating future travel demands within specified sub areas in Peterborough;

Traffic Operational Plans - For evaluating traffic impacts and alternative traffic improvements in sub areas of Peterborough; and

Setting of Development Charges - For identifying major improvements required to City of Peterborough road network and for assessing percentage increase in travel demand related to new development in the City and adjacent local municipalities.

1.2.3 Potential for Future Expansion / Low Priority

Consideration to developing modules that could provide information with regard to the following should be given:

Transit Plans - For identifying need for services between municipalities and within Peterborough and for providing ridership data for preliminary design of transit services;

Air Quality Improvement Strategies - For estimating air quality impacts associated with alternative development and transportation plans and for monitoring changes in traffic and related impacts on air quality; and

Emergency Response Planning - For planning alternative routing of traffic in instances where major roads or river crossings may be closed.



1.3 Model Overview Assessment

The City of Peterborough model has undergone a number of transformations/updates since it was first developed. The current model can be likened to a photocopy of a photocopy. That is to say, that with each change/evolution of the modelling framework, some integrity has been lost at each step along the way. Indeed the model has evolved from a rigorous four-step transportation planning model to essentially an assignment tool. Based on this, it is clear that a “fresh start” approach is required in terms of the modelling framework as the current model does not adequately meet the requirements outlined above.

1.4 Known Issues and Constraints

The City of Peterborough model was originally designed to prepare long range transportation plans for the Peterborough area utilizing travel demand forecasts to identify major transportation requirements over a 20-year horizon period. The City identified the following key issues and constraints:

Traffic Assignment - Intersection delay is ignored in the current City of Peterborough model. Most traffic assignment procedures assume that delay occurs on the links rather than at the intersections. This is a reasonable assumption for highways and freeways but not for road corridors with extensive signalized intersections. There are a number of signalized intersections within the City of Peterborough model that involve highly complex movements and signal systems. These have been highly simplified in the current model. The current traffic assignment process does not modify control systems in an attempt to reach equilibrium. The use of sophisticated traffic signal systems, freeway ramp metering or enhanced network traffic control is not easily analyzed with conventional traffic assignment procedures. In the past transportation planning model networks were populated with road attribute data manually. This information can now be managed electronically with the added GIS component.

Commercial Vehicle Travel - The City of Peterborough Count Program includes collection of detailed vehicle classification data which to date has not been fully utilized in the current City of Peterborough model. The project explored the need to more accurately and reasonably model commercial vehicle travel.

Extent of Road Network - The current model assumes that all trips begin and end at a single point in a zone (the centroids) and occurs only on the links included in the model network. Not all roads/streets have been included in the current network nor have all possible trip origins and destinations been included. The current zone/network system is an oversimplification of reality and excludes some travel most notably shorter trips.

Over-simplified Roadway Capacities - Determining the capacity of roadways requires a complex process of calculations that consider many factors. Travel forecasts in the City of Peterborough model have been oversimplified. For example, capacity is based only on the number of lanes of a roadway and its type (freeway or arterial).

Emphasis on Peak Hour Travel - As mentioned above, forecasts are done for the PM peak hour on a typical weekday. A forecast for the peak hour of the day does not provide any information on what is happening the other 23 hours of the day, in particular during the AM peak hour and the mid-day conditions. A measure of the duration of congestion beyond the peak hour such as "peak spreading" is not determined. Further, travel forecasts are made for an 'average weekday'. Variation in



travel by time of year (summer vs. typical) or day of the week (weekday vs. weekend) was not currently considered.

1.5 Action Items for Update

Based on the model review and the Terms of Reference for the study, the following key elements have been identified as requiring action in this update.

1.5.1 Modelling Framework

The current City of Peterborough model has a number of fundamental features that needed to be reviewed in the project including the Traffic Analysis Zones (TAZ) and network resolution. These are the fundamental building blocks of the model. A well-designed TAZ system and comprehensive network structure (including centroid connectors) can greatly assist in providing reliable and meaningful forecasts.

The TOR expressed concern with the current zone system and the state of the network including the coverage of the network and the network link characteristics. Based on the geographical size of City of Peterborough and the level of resolution necessary, it was determined that the TAZ’s and network required a critical review and update.

There were a number of important items that needed to be given consideration in this review:

Longitudinal conformity - It is essential that any new TAZ system that is developed be able to be linked geographically back through the current zone system to provide the City with the ability to monitor changes in travel patterns and demands over time as well as to utilize previous land use and travel demand data.

Road Network Complexity - There is an adage in transportation planning that states “Travel knows no boundaries”. This notion is important when considering the roadway network to be included in the model. In many ways the network that is developed should focus on the demands of the system rather than any self-imposed jurisdictional filter. That is, there may be “local” roads that are playing an important function and should be included. Similarly, there may be a series of local roads that feed together to load the City of Peterborough road network and should be included as a system rather than being represented by a centroid connector.

Road Network Use - Understanding the nature of the road system and how it is used is very important in terms of providing an accurate traffic assignment. As with the above, traffic does not necessarily adhere to an assumed functional classification. For example, there may be links in the network that are classed as “local” in Official Plans that are performing a “major collector” function. Also it is important to understand that “all arterials are not created equal” and the model network needs to reflect this.

Roadway Planning Capacity - Coupled with the above is the determination of roadway capacity. The TOR noted that there is perhaps an over-simplified view toward capacity in the current framework. While that may be accurate and should be thoroughly reviewed, there are also important features in the modelling framework such as the Volume-Delay Functions (VDF) and intersection delays that are very important in determining the true capacity of a corridor. In many cases, it is the intersections which govern the throughput of a corridor and it is incumbent upon the modelling framework to accurately reflect these to the maximum extent possible.



1.5.2 Trip Generation

The absence of Trip Generation rates and equations in the current model severely limit the ability of the tool to produce accurate long-term forecasts of demand. The current tool relies on the 1996 Transportation Tomorrow Survey Demand matrices that have been Fratar-balanced into the horizon years.

This approach has a number of limitations including:

Low Sample - The demand matrix is based on roughly a 4.5% sample and has a significant number of “no-observation” cells. The Fratar technique cannot create demand; rather it simply factors demand where it exists in the base table. This is reasonable for short-term planning horizons where travel patterns are not likely to change much. It cannot reflect long-term changes in demand resulting from new development patterns, where no demand was observed in the base year without an artificial intervention.

Fixed Demand Patterns - This technique is not responsive to reflect the changes in travel demand that might occur as result of infrastructure improvements. For example, the impact of a new arterial link, or improvements to the Highway 115, or other Highways that reduce travel times and therefore make commuting to the GTA from the area a shorter time trip, cannot be reflected by a Fratar-based model.

There are other issues that have been identified with travel demand in the TOR that need be given consideration in the model update:

Commercial Vehicle Travel: A significant amount of commercial travel demand relies on the Provincial Highway system through Peterborough area. In addition, there are a number of agricultural and industrial developments which generate commercial vehicle travel. Methods of estimating commercial travel demand needs to be considered in the update.

Temporal Variation: Understanding time-varying demand is fundamental to understanding how the transportation system is utilized. The role that the transportation planning model can have in this regard needs to be reviewed. Many agencies are now carrying forward both AM peak hour and PM peak hour trip generation functions to attempt to capture the unique issues associated with each peak hour. The current model prepares PM peak hour forecasts which tends to be the highest hour of demand for the majority of the system but may be less accurate in areas of high industrial employment (which tend to have AM peak hour based issues) and high tourist activity (which tend to have Saturday peak hour issues).

Tourism: The ability of the current model to accurately reflect peak travel demands in high tourist areas is an area of concern for the transportation planning in the area. The model update must review and consider means to determine whether the modelling tool is the appropriate method in forecasting this demand and secondly how it could get incorporated into the modelling structure.

1.5.3 Trip Distribution and Mode Split

The current model does not employ any gravity-model based trip distribution methods as it relies on the Fratar technique to forecast future demand. Development of trip distribution functions will be required for a full four-step transportation planning model.

The TOR also highlights the need to consider the role of mode split in the modelling framework. There are a number of issues that could potentially arise depending upon the approach ultimately undertaken. It is



recognized that there are specific areas which rely on public transport to provide effective transportation services. The degree to which this will play a role in the ultimate development of the model will need to be addressed in this effort.

1.5.4 Trip Assignment

The current model framework relies on the traditional link-based approach to traffic assignment. This approach uses parameters related to the ratio of volume to capacity to assign delay to the link. This has a number of limitations; the most significant of which is that it ignores the impacts of intersections on capacity and consequently it is difficult to accurately model areas such as downtown Peterborough.

The model update considered new and improved methods of estimating delay. This included a detailed review of the VDF functions available in the model. The model update also addresses the issues of advanced assignment techniques which give consideration to intersection delays and turn penalties.

1.5.5 Modelled Speeds

At a project steering committee meeting of January 14, 2010 it was identified by that without confirmed and validated model speed estimation, the full evaluation of some of the parameters proposed for the evaluation of the alternatives could not be included in the benefit-cost analysis proposed for the evaluation of alternatives.

At the meeting, it was identified that overall the model speed estimation that resulted from the calibration process to date had shown to well-represent typical conditions in Peterborough based on experience in developing models elsewhere.

Despite these findings, the following issues were nonetheless identified:

it was felt that the model VDF functions that were currently being used potentially underestimated the impacts of congestion in Peterborough, in particular for instances of high V/C ratios;

the VDF functions currently in use within the model and previously used in the development of the 2002 TMP and West Side Analysis were not supportable in that they had not been validated for use in the Peterborough context;

there was a desire to be able to forecast link speeds with a reasonable degree of precision to link-specific segments; and

there was a general lack of objective, observed speed data against which the VDF function and area-specific, and link specific speeds could be tested and validated.

It was agreed that in order to provide enhanced assessments of the relationship between speed and congestion in the context of the Peterborough network, it would be necessary to undertake a comprehensive data collection effort couple with detailed analyses of the average and free speed conditions.



2.0 MODEL FRAMEWORK ENHANCEMENTS

This section documents the work undertaken to update the current modelling framework given the goals and objectives identified above, along with the identified issues and constraints.

2.1 Data Sources

Within the modelling framework a number of data sources and methods were used in this update:

City of Peterborough GIS database – City of Peterborough maintains a single-line representation of the entire road network within the City. This is maintained within a Geographic Information System and is tied to a number of information sources which are important to transportation planning;

Traffic Count Data – The City of Peterborough conducts a comprehensive annual traffic counting program. The data are mapped to the road sections and intersections within the City of GIS structure and include automatic traffic recorder counts, intersection turning movement counts and classification counts; and

County of Peterborough Model – In cooperation with the City of Peterborough, the County of Peterborough provided its model network files to the project to ensure that the most recent changes completed in its TMP update were reflected in the City’s work.

2.2 Traffic Analysis Zone Resolution

The existing Traffic Analysis Zone System (TAZ) has been found in practice at City of Peterborough to be too coarse in many cases to provide reliable forecasts at the municipal level. Notable issues with the current TAZ structure included:

TAZ boundaries were found to span barriers. While in most cases, these areas were undeveloped and would not generate travel demand, it did not provide adequate flexibility for changes over the long term. Indeed identification of areas that produce little or no trips is as important as those that are high generators; and

Known growth areas were typically found to have large zones which would not be capable of reflecting potential future development scenarios without the development of a sub-area model.

A comprehensive review of the TAZ framework was undertaken and the structure was modified accordingly. Figure 2.1 illustrates the refined TAZ structure that resulted from this effort.

2.3 Model Network Resolution

The development of the TAZ structure described above was in part created through the addition of new roadways to the modelling framework. A thorough review of the entire City of Peterborough roadway infrastructure was undertaken. The City of Peterborough GIS formed that basis on which the model would be constructed. The model framework was developed as the locus of:



Figure 2.1: Traffic Analysis Zones


Traffic Analysis ZonesParadigmwww.ptsl.com



All County of Peterborough roads;

All City of Peterborough arterial roads;

All City of Peterborough collector roads;

All important local streets such as those that are effectively performing a function of collector roads;

All known future new roads; and

Potential roadway patterns in new growth areas.

Outside of City of Peterborough, with co-operation from the County of Peterborough the model network was incorporated in its entirety. This provides an important potential policy variable for the City and allows the City to test important provincial policy directives with respect to the impact of new potential transportation corridors on the City of Peterborough road network. For example, the Highway 7 corridor route has been incorporated into the modelling framework to provide the City with information on the potential diversion to the corridor to or from City of Peterborough roads.

Figure 2.2 illustrates the refined network structure that resulted from this effort.

2.4 Centroid Connectors

As a direct result of the development of the TAZ structure, additional centroid connectors were added to the modeling framework. Coincident with this work, a review of the centroid connectors was undertaken. The following process and generalized rules were applied to the placement of centroid connectors:

Centroid connectors should reflect the local road system wherever possible;

No centroid connector should be directly connected into an intersection, unless it is representing a local road;

In urban areas, centroid connectors should reflect access to and from major parking facilities;

The number of centroid connectors should be limited to four for any particular zone, with one or two being preferred.

Figure 2.3 provides an example of the assignment of centroid connectors in downtown Peterborough.



Figure 2.2: Model Network


Model NetworkParadigmwww.ptsl.com



Figure 2.3: Centroid Connector Example (Downtown Peterborough)


Centroid Connector ExampleDowntown Peterborough

Paradigmwww.ptsl.com



2.5 Network Attributes

There are a number of roadway network attributes that are contained in the network file. These have been updated to reflect the nature of the modelling desired within the City. Important attributes include:

Link Length – the length of the link expressed in kilometres;

Direction of Travel – a flag used by the model to assist in assignment with “0” representing two-way flow, -1 and 1 representing one-way flow depending on the direction of the link’s insertion into the network;

Street Name – as it appears in the City or County GIS

Functional Classification – the assigned functional classification (see 2.6.3);

Alpha – alpha variable in the BPR VDF formulation;

Beta - beta variable in the BPR VDF formulation;

Posted Speed – legal posted speed on the link;

User Assigned Free Flow Speed – user-defined variable per direction (AB and BA) used to influence assignment;

Free Flow Travel Time – calculated travel time on link per direction (AB and BA) based on the User Assigned Free Flow Speed used to influence assignment;

Number of Lanes – number of lanes per direction (AB and BA) in the link;

User Assigned Per Lane Capacity - user-defined variable per lane per direction (AB and BA) used to influence assignment;

Capacity - calculated variable per direction (AB and BA) used to influence assignment;

Existing – PM peak hour traffic volumes per direction (AB and BA) as observed in the field.

2.5.1 Physical Attributes (Length, lanes, and posted speed)

The number of travel lanes and posted speed data were obtained directly from the City and County GIS along with the direction of travel. Note one-way links are assigned values of 1 or -1 to indicate one-way travel with respect to the network topology. Link length is automatically calculated within TransCAD based on the Euclidean distance between the endpoints of each line segment. The network is based on the UTM NAD 83 projection contained in the City GIS.

2.5.2 Functional Classification and Planning Capacity

Functional classification in transportation modeling is used to identify not only the intended function of a particular road, but also the actual function. It is important to realize that not arterial roads perform equally and in some cases, it is arguable that collector roads are performing minor arterial function.

A thorough review of the network was performed by City staff to assign major and minor function to each of the arterial and collector links within the model framework.



The planning capacities assigned to each model link are based on the functional classification assigned to each link, along with the area type designation. Table 2.1 summarizes the generalized planning capacities assigned in the City of Peterborough Model.

TABLE 2.1: PLANNING CAPACITIES

Functional Classification Grade Peterborough WALTS WUTS SEMCOGRegion of Waterloo

Region of Niagara

Region of Ottawa-Carleton

Brantford St.Thomas London

Freeway 1800 1850 1800 1850-1900 1800 1850 1800 1800 1800 1800

Freeway Ramps Fwy. To Arterial 1300 1300 n/a 1200-1300 900 1300 1200 1300 1300 1300

Fwy. To Fwy. 1500 1600

Highway Rural 1000 1100 1000 1100 1100 1200-1600 1000 1000 1100

Arterial High 800 900 800 850-950 900 900 1000 900 800 900

Medium 700 800 700 650-850 750 800 800 800 700 750

Low 600 n/a n/a n/a 650 n/a 600 n/a n/a

Collector High 500 650 600 550-700 550 650 600 650 500 500

Medium 400 500 250 500-575 n/a 500 400 500 400 n/a

Local 300 350 n/a n/a 400 350 400 350 300 n/a

The entries in the table highlight the intended role of each class of facility within the various area types. For example a two-lane major arterial could carry up to 800 vehicles per lane per hour (e.g. Downtown Peterborough), while in areas such as Downtown Peterborough arterial facilities would only be expected to carry 700 vehicles per lane per hour.

The generalized planning capacities noted above were modified during the model calibration process. Table 2.2 summarizes notable changes made during the calibration process along with the rationale for the change.

TABLE 2.2: PLANNING CAPACITY ADJUSTMENTS MADE DURING CALIBRATION PROCESS

1 Parkhill Rd. West Brealey Drive to Wallis Drive 1600 veh/hr 700 veh/hrThis section of Parkhill is a two lane rural cross-section equivalent to the road segment west of Brealey.

2 Parkhill Road West Monaghan Road to Fairbairn Street 1400 veh/hr 1600 veh/hrThis section of Parkhill immediately east of Monaghan Road over the bridge operates the same as the link to the west.

3 Monaghan Road McDonnel Street to Parkhill Road 1400 veh/hr 700 veh/hrThis section of Monaghan Road is striped as and operates as a two-lane facility. It has pavement of a four lane facility but doesn’t operate as such.

4 Charlotte Street Monaghan Road to George Street 1400 veh/hr 700veh/hr This section of Charlotte Street operates as a wide two lane road.

5 Sherbrooke Street Glenforest Boulevard to Wallis Drive 800 veh/hr 1600 veh/hrThis section of Sherbrooke Street is a four lane arterial with few side street and driveway conflicts.

6 Sherbrooke Street Wallis Drive to Monaghan Road 1600 veh/hr 1400 veh/hrThis section of Sherbrooke is four lane but has numerous side street and direct residential driveway interfaces.

7 Sherbrooke Street Monaghan Road to George Street 1400 veh/hr 700 veh/hrThis section of Sherbrooke Street operates as a wide two lane road.

8 Hunter Street Alymer Street to George Street 1200 veh/hr 600 veh/hrThis section of Hunter Street is a two lane facility with on-street parking.

9 Hunter Street East George Street to Rogers Street 1400 veh/hr 700 veh/hrThis section of Hunter Street is a wide two lane facility with on-street parking both sides of the road.

10 Chemong Road Parkhill Road to Sunset Boulevard 1600 veh/hr 1200 veh/hrThis section of Chemong Road is base 4 lane however there are no turning lanes, lane geometry is narrow and significant number of uncontrolled commercial accesses.

11 Chemong Road Towerhill Road to Milroy Drive 800 veh/hr 1600 veh/hrThis section of Chemong Road is a four lane arterial with turning lanes at intersections.

Adjusted Link Capacity

Based Link Capacity

From/toNo. Street Comment / Explanation



2.5.3 Link Delay Estimates

The link performance function is a mathematical representation of the relationship between flow (i.e. traffic volumes and travel cost (i.e. travel time) on any given link in the network. In the case of the City of Peterborough model link delay calculations are based on the BPR formulation.

cvtt f /1 (Equation 1)

where:

t: congested link travel time

tf: link free-flow travel time

v: link volume

c: link planning capacity

: calibration parameter

: calibration parameter

The BPR formulation is the default link performance function provided in TransCAD. In the case of Peterborough, the and parameters are assigned by functional classification. The default values are 0.15 and 4.00 respectively.

To refine the calibration parameters, it was necessary to collect speed and travel time data across the Peterborough network, so that travel speeds could be verified by functional classification and by corridor. In order to calibrate and refine the VDF functions, a comprehensive dataset of traffic operating speeds are required for each functional classification and through a range of V/C values. It was important that data were collected on links that are experiencing as broad a range as possible to ensure that the VDF functions replicate the delay and speed conditions that are occurring on these links. In order that the V/C values could accurately be represented, the measured volumes on the links were used.

Overall, data were collected on about 169 km of roadways. To ensure statistical reliability, three days of sampling was undertaken. To improve sampling efficiency, to provide a full range of V/C conditions and to respect timing and budgetary constraints, sampling was undertaken during the AM and PM peak periods. Section 8.0 of the report details the process undertaken to calibrate the VDF functions.

Based on the result, the following suggested changes to the modelling framework were made:

in general the link free speeds, be set to the average observed speed by functional class grouping (Arterial, Collector, Local), subject to calibration adjustments; and

the BPR formulation be implemented such that the alpha constant reflects the function classification under consideration and the exponent on the V/C term remain at 4.

Table 2.3 contains the values as assigned by functional classification. The importance of the BPR values is to create congestion on lower class facilities sooner, so that short-cutting is reduced. The shape of the functions creates delay on the lower class roads sooner than for the higher class roads, thus encouraging the assignment of traffic to the higher class facilities wherever possible.



TABLE 2.3: BPR VARIABLES

Class Alpha BetaFreeway/Expressway 0.20 4Arterial Highway (Rural Regional Road) 0.25 4Major Arterial 0.30 4Minor Arterial 0.35 4Major Collector 0.40 4Minor Collector 0.45 4Local 0.50 4



3.0 MODELLING PROCESS

3.1 Transportation Demand Modelling

Transportation demand modelling has a history dating back to the late 1960’s, when the standard four-stage procedure (trip generation, trip distribution, mode split and assignment) was first introduced. These models were initially developed to assist municipalities in dealing with rapid automobile growth and the planning of new roadway infrastructure. In later years, the planning of transit systems became more important, which resulted in advances in transit modelling techniques. The last 40 years has seen much progress, but the basic inputs and outputs have not changed significantly. In general, the model inputs consist of:

land use data allocated according to a set of traffic zones; and

network data that describe all the physical characteristics of the road links and transit routes that connect these zones.

The model outputs include estimates of travel volumes and travel times for:

all origin/destination pairs by mode; and

each link on the road and transit network.

These outputs are used for many transportation-related activities including: strategic planning; transportation demand management analysis; highway and transit project evaluation; traffic and revenue studies; transit route planning and local site impact analysis.

Despite the complex mathematical equations employed by the model, they represent a simplification of human travel behaviour. Many of the data inputs as well as the formulas used to estimate travel represent average conditions or behaviour, and cannot hope to replicate the real world in all its detail. Therefore, while the model produces remarkably accurate estimates of travel over the system in general, and reasonable comparisons with observed counts on many individual road and transit links, in some cases there will remain significant variations between observed and estimated values.

To some extent, the model accuracy can be improved by introducing site-specific trip generation rates and enhanced traffic zone and network detail. For instance actual traffic attracted to a particular zone may be higher than estimated because the specific type of retail in that zone attracts more trips per foot than the average square foot of retail space. When detailed forecasts are required, a sub-area model can be developed for a specific area or municipality, using the City of model as a starting point. This step will result in improved model accuracy within the sub-area, such that the model outputs can be directly input to other software used for specific applications, such as the design and signal timing of intersections.

3.2 Transportation Model Overview

The City of Peterborough transportation model is comprised of three main components:

a traffic zone system and associated land use data;

a base network; and

a four-stage transportation modelling procedure.



City of Peterborough has been divided into a system of Traffic Analysis Zones (Figure 2.1). The zone size varies according to population and employment densities and geographical features and barriers.

The updated TAZ boundaries are coincident with the Traffic Analysis Zones used the previous Transportation Master Plan and thus are a subset of the TAZ’s and can be aggregated to compare longitudinal data over time. These boundaries also attempt to adhere to the federal government’s census tracts and to municipal boundaries wherever possible. It must be noted that census boundaries are adjusted over time and may in time not remain consistent with these boundaries. Detailed demographic information has been developed for current and future years for each traffic zone.

Also included in the zone system are external zones located at entry points to the Peterborough area to account for traffic entering, leaving or passing through the area.

The second component is a digital base network that covers City of Peterborough (Figure 2.2). The auto network is comprised of all freeway, arterial and collector facilities within City of Peterborough and County of Peterborough (model the model area). Within the highly urbanized parts of City of Peterborough important local roads are also included. Each auto link contains information on the number of lanes, posted speed limit, and capacity. The digital auto network consists of approximately 1,847 nodes (intersections) and 2,419 links (road segments).

The third component is the transportation modelling procedure that predicts the number of auto vehicle trips during the PM peak hour. This procedure is represented schematically in Figure 3.1. This diagram contains three types of boxes, which differentiate between policy input variables, sub-model algorithms and model outputs. The directional arrows indicate the flow of the modelling procedure through a series of sub-models, which are referenced according to the sections in this report.

The boxes shaded in yellow represent major sub-models in the transportation demand estimation process, where this is described in more detail in the following sections:

Trip Production and Attraction: estimate the number of person trips in each traffic zone for each trip purpose, based upon the population and employment demographics;

Mode Split: estimates the mode of choice for a trip for each origin/destination (e.g. walk/bike, transit or auto) by using the relative share of Auto trips compared to Non-Auto trips;

Trip Distribution: estimate the trip interchanges, of the number of person trips between zones, based upon trip impedance;

Traffic Assignment: based upon the final trip matrices from the mode split stage, this step estimates route choice on the road network.



Figure 3.1: City of Peterborough Modelling Procedure


Peterborough Modelling ProcedureParadigmwww.ptsl.com

Land Use Data

TripProduction

TripAttraction

HBWProd

HBOProd

NHBProd

HBWAttr

HBOAttr

NHBAttr

TripDistribution

Trip Balancing

AutoImpedance

Total Trips

Home-Based Work

Home-Based Other

Non Home-Based

ModeSplit

Auto Person Trips

VehicleAssignment

Auto Trips

Auto ModeShare

Auto OccupancyTotal Vehicle Trips

External Auto Trips



4.0 LAND USE

As outlined above, land use information is one of the primary inputs to the modelling process. It is used to create the projected amount of travel demand produced and attracted to any particular area within the City of Peterborough. This section of the report provides an overview of the demographic information used in this study.

4.1 Data Sources

Data used in this update were provided by City of Peterborough Planning staff at the TAZ level and were obtained from the following sources:

2001 Census Canada Data – The primary source of demographic (population and employment data) for this study was that which was available from the 2001 Census Canada database. Data provided to the City was made available at the Dissemination Area (DA) level and through GIS allocation procedures were used by planning staff to allocate to the TAZ structure.

4.2 Limitations and Assumptions

The population data used in the modelling process is widely considered to be the most reliable source of information available as it is based on a virtually 100% sample. It should be noted that there are known issues with Census Data including:

Under-reporting: Experience across Canada has shown that under-reporting of population and employment data does occur. In particular, work at-home, nomadic (no regular place of work – or work in several locations) and student population data are affected. It addition, it should be noted that the Statistics Canada employment data is based on a 20% sample as only 1 in 5 households receive the “long form” census survey which requires detailed employment locations

Data Suppression: Statistics Canada applies data suppression policies when the values for population or employment fall below a specified minimum threshold. In the sparsely occupied areas of Peterborough, this can affect the overall distribution of residents and jobs.

4.3 TAZ Framework

In the scope of work identified for the project, a refinement of the TAZ structure to provide more refined assignments had been identified as a key deliverable. This process (Section 2.2 and Section 2.3) was seen as important to the overall improvement of the model. At the outset of the project, City of Peterborough Planning staff indicated that provision of population and employment data at any level more discrete than the TAZ structure would not be possible, therefore limiting the ability to provide a more discrete structure.



5.0 TRIP GENERATION

As outlined above, the trip generation modules (productions and attractions) are the first modelling processes. They make use of the land use information to create the projected amount of travel demand produced and attracted to any particular area within the City of Peterborough. This section of the report provides an overview of the trip generation modules contained in the City of Peterborough model.

5.1 Data Sources

The trip generation relationships are based on the following data sources:

Transportation Tomorrow Survey – This information is collected each census year and is based on a telephone survey of residents within the Greater Golden Horseshoe (GGH) area. It provides the City with a 24-hour database of travel patterns for all travel modes and four primary trip purposes; and

Demographic Data – The population and employment data produced by City of Peterborough staff provide the necessary independent variables for determining the trip generation relationships. In this case the independent variables included total population, total primary employment, total manufacturing employment, total institutional employment and total other employment

5.2 Methodology

The trip generation sub-model determines the number of trips produced and attracted by each traffic zone. Separate production and attraction equations were developed for three typical PM peak hour trip purposes:

Home-Based Work (HBW)

Home-Based Other (HBO)

Non Home-Based (NHB)

These trip purposes are an aggregation of detailed trip purposes (e.g. work to home, work to dropping off passenger, work to shop) that exhibit common trip characteristics. These trip purposes have similar demographic generators, trip lengths and mode biases.

The first step in developing trip generation rates and equations was to test the overall rigour of the data contained in the TTS database. The daily trips per capita were calculated based on the observed trips and the demographic information. Overall the person-trips per capita were estimated to be in the order of 2.5 person-trips per capita and about 6.5 trips per household. When compared to data collected across North America these values were found to be in the order of 8 to 10 person trips per household on a daily basis and 3-4 trips per person during the peak hour. This indicates that the TTS data experiences under-reporting of trips in the order of 20-35% compared to experience across North America. Previous documentation in Peterborough indicated that the Data Management Group (DMG) at the University of Toronto who collected this information, has recognized the under-reporting of trips as an issue with the data as far back as 1996. Reports prepared for the 1996 survey data and suggest that non-Home-Based-Work travel was under-reported by 27% to 41% in 1996 which is significant.

Another known issue with the TTS data is the temporal distribution of trips. Significant values of the trip making are coded to the quarter-hours. Therefore, determination of a single peak hour is highly sensitive to



whether a particular 15-minute is included. A common technique to avoid this concern is to use the PM peak period (i.e. 3:00 PM to 6:00 PM) and then apply a peak hour factor to obtain the peak hour data.

For the City of Peterborough model, to avoid these two particular issues, the peak period was extended to include all trips coded between 3:44 PM to 5:16 PM. Overall this resulted in 0.30 person trips during the PM peak hour which is consistent with experience elsewhere across North America.

Having established that there was sufficient overall trip-making contained in the database, linear regression techniques were used to test the relationship of various independent variables and combinations for each trip purpose. Several combinations of the independent variables, along with geographical stratification (i.e. urban, vs. rural) were assessed to determine which combination of independent variable(s) and geographical stratification provided the most statistically reliable model.

5.2.1 Comparison to 2002 Model

In the 2002 Transportation Master Plan, trip generation functions were developed to produce PM peak hour Auto Driver trips. These were based on the 2001 TTS data. The first step in model calibration was to test these functions against the 2006 TTS data to determine if the functions were still relevant, or in need of an update.

Figure 5.1 depicts the PM Peak hour Auto trip Demand production and attraction functions used in the 2002 study for each of the three trip purposes.

The demographic data provided by the City in mid-February 2009 were then fed into the trip generation module to produce the estimated trips produced and attracted to each Superzone. The trip generation results were then compared to the data collected in the 2006 TTS. (Figure 5.2)

Overall the predicted productions were within 4% of the observed values, while the predicted attractions were within 9%. In each case, there predicted values were underestimated. The industry-accepted measure of the “Goodness of Fit” of the observed versus the modelled trips is the Coefficient of Determination (R2). The FHWA’s “Model Validation and Reasonableness Checking Manual1” (MVRCM) identifies that the Coefficient of Determination (R2) should be greater than 0.88. In the case of the Peterborough model, the Coefficient of Determination (R2) was calculated to be 0.91 for the overall productions and 0.81 for the overall attractions, indicating a good degree of correlation between the predicted and the observed trips. (Figure 5.3)

Based on the reviews of the preliminary results that were provided, it was felt by the project team that additional effort to gain increased precision in the model would be a prudent course of action. As such, additional effort was expended on updating the trip generation functions within the model. The results follow.

5.2.2 2006 Trip Generation Rate Comparison

Using the same process that was followed in the 2002 TMP, automobile trip generation functions were developed for each trip type using the 2006 Transportation Tomorrow Survey Data and the revised demographic data provided by the City in mid-August 20092. (Figure 5.4) This process led to improved

1 Model Validation and Reasonableness Checking Manual, FHWA, Barton-Aschman Associates Inc. and Cambridge Systematics Inc., 1997. 2 City Staff updated/refined the 2006 base year population and employment data between February 2009 and August 2009 to reflect the City’s the Growth Plan Policy initiative which was running contemporaneously with the study at the time.



overall precision with respect to the Trip Generation Functions. (Figure 5.5) The results of this process are discussed below:



Figure 5.1: 2002 Model Auto PM Peak Hour Trip Generation Equations

Fig

ure

5.1

2002 M

odel

PM

Pea

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our

Aut

o Tr

ip G

ener

atio

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200.0

300.0

400.0

500.0

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2000

3000

4000

5000

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t

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HB

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400.0

600.0

800.0

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2000

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5000

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1000.0

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Pop

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0.0

200.0

400.0

600.0

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1000.0

1200.0

05000

10000

15000

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400.0

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05000

10000

15000

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100.0

200.0

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400.0

500.0

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01000

2000

3000

4000

5000

6000

Em

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City o

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Figure 5.2: 2002 Model Auto Trip Generation Equations vs. 2006 TTS Observed Trips

Fig

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Figure 5.3: 2002 Model Auto Trip Generation Equations vs. 2006 TTS

Fig

ure

5.3

2002 A

uto

Trip

Gen

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qua

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svs

. 2006 T

TS

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0

500

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1000

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2500

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1500

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2500

3000

3500

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Pro

ductions

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Figure 5.4: Comparative Differences February 2009 and August 2009 Data

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Overall Fit – Overall the predicted productions were within 0% of the observed values, while the predicted attractions were within 0%. The Coefficient of Determination (R2) overall was calculated to be 0.95. (Figure 5.6) For the trip productions, the value was calculated to be 0.91 for the overall productions and 0.97 for the overall attractions, indicating a very high degree of correlation between the predicted and the observed trips. (Figure 5.7)

Home Based Work (HBW) – A number of model constructs were tested to determine of the good-of-fit measures previous observed could be improved. The HBW productions were determined to be statistically related to both population and employment. The following equations were developed: HBW (productions) = 0.1558(Employment) + 0.0061(Population) - 6.75 HBW (attractions) = 0.0768(Population) + 0.004(Employment) + 4.2 Scattergram plots of the predicted versus observed values for the productions (Figure 5.8) and attractions (Figure 5.9) were prepared. The purpose of the exercised was to identify the closeness to the line X=Y that the data fell to identify the overall goodness of fit and identify any outliers. The graphics indicate a high degree of correlation between the predicted and observed values. The Coefficient of Determination for the productions was 0.94, while for the attractions it was 0.91.

HBO – A number of model constructs were tested to determine of the good-of-fit measures previous observed could be improved. The HBO productions were determined to be statistically related to both population and employment. The following equations were developed: HBO (productions) = 0.0657(Employment) + 0.0745(Population) - 62.73 HBO (attractions) = 0.0693(Population) + 0.0376(Employment) – 70.91 Scattergram plots of the predicted versus observed values for the productions (Figure 5.10) and attractions (Figure 5.11) were prepared. The purpose of the exercised was to identify the closeness to the line X=Y that the data fell to identify the overall goodness of fit and identify any outliers. The graphics indicate a high degree of correlation between the predicted and observed values. The Coefficient of Determination for the productions was 0.89, while for the attractions it was 0.96.

NHB – A number of model constructs were tested to determine of the good-of-fit measures previous observed could be improved. The NHB productions were determined to be statistically related to both population and employment. The following equations were developed: NHB (productions) = 0.0852(Employment) + 0.0146(Population) – 43.59 NHB (attractions) = 0.0205(Population) + 0.0623(Employment) – 43.69 Scattergram plots of the predicted versus observed values for the productions (Figure 5.12) and attractions (Figure 5.13) were prepared. The purpose of the exercised was to identify the closeness to the line X=Y that the data fell to identify the overall goodness of fit and identify any outliers. The graphics indicate a high degree of correlation between the predicted and observed values. The Coefficient of Determination for the productions was 0.69, while for the attractions it was 0.72.



Figure 5.6: Total Predicted Trips vs. Total Observed Trips (2006)

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Figure 5.7: Total Predicted Trips vs. Total Observed Trips (Productions and Attractions)

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Figure 5.8: PM Peak Hour Auto Trip Calibration HBW Productions Predicted vs. Observed

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Figure 5.9: PM Peak Hour Auto Trip Calibration HBW Attractions Predicted vs. Observed

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Figure 5.10: PM Peak Hour Auto Trip Calibration HBO Productions Predicted vs. Observed

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Figure 5.11: PM Peak Hour Auto Trip Calibration HBO Attractions Predicted vs. Observed

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Figure 5.12: PM Peak Hour Auto Trip Calibration NHB Productions Predicted vs. Observed

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Figure 5.13: PM Peak Hour Auto Trip Calibration NHB Attractions Predicted vs. Observed

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5.3 Trip Generation Calibration Summary

The trip generation equations were generally overall found to have the highest reliability on the attraction side. The HBO attraction equation had an R2 value of 0.96 and while the HBW attraction equation had an R2 value of 0.91. NHB trips were generally found to have poor fits to the independent variables.

The total amount of trips predicted on the attraction side was found to be virtually replicating the observed trips. Therefore, trip balancing to the attraction side was determined to provide the best results.



6.0 TRIP DISTRIBUTION AND MODE SPLIT

As outlined above, the trip distribution module is used to allocate trips between all origins and destinations. They are generally based on a gravity model formulation, which is based on Sir Isaac Newton’s Law of Universal Gravitation. The fundamental nature of the equation indicates that the relative attractiveness of any two zones is directly proportional to the population and employment in each zone and inversely proportional to the travel time separating them. This section of the report provides an overview of the trip distribution modules contained in the City of Peterborough model.

6.1 Data Sources

The trip distribution relationships are based on the following data sources:

Transportation Tomorrow Survey – This information is collected each census year and is based on a telephone survey of residents within the Greater Golden Horseshoe (GGH) area. It provides the City with a 24-hour database of travel patterns for all travel modes and four primary trip purposes.

6.2 Methodology

The most common method of distributing trips between any two given TAZ’s used in transportation planning is the gravity model. The essence of the model is that the relative attractiveness of any two given TAZ’s is directly proportional to the cross product of a measure of the propensity to create trips and inversely proportional to an impedance function. This is based on Sir Isaac Newton’s Law of Universal Gravitation:

Within the Peterborough modelling process, the trip distribution module estimates the number of person trips travelling between OD pairs for each trip purpose (HBW, HBO, NHB). The trip distribution models are developed for internal trip-making and do not include trips that originate or are destined to areas outside the City of Peterborough. A separate external trip matrix has is added to the final internal auto matrix prior to assignment. (see Section 7.0)

Internal trip distribution is a multi-step process that starts with the calculation of travel impedances (or travel time in this case) between OD pairs. The impedance matrices are then used to calculate friction factors, which describe the propensity to travel between different locations. Friction factors are calculated for each trip purpose as they exhibit different trip length characteristics. A balancing algorithm is used to implement the gravity models and convert the trip production and attraction vectors into full OD matrices.

Travel impedance is based on the travel time (including link delay and intersection delay) between any given two zones. Auto impedances were developed for each trip purpose for the City of Peterborough model. These impedances were then used to calibrate gravity models for each trip purpose. Gravity models are used to distribute the production and attraction vectors between OD pairs.

The friction factors take the form of a negative exponential equation. The functions are calibrated to the Trip Length Distribution (TLD) curves for each trip purpose. As these values become less negative, the average trip lengths increase as illustrated in Figure 6.1.



Figure 6.1: Example of Gravity Model Travel Impedance Function

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Individual trip purpose matrices are then computed using the two-dimensional, or doubly constrained balancing procedures in TransCAD. Inputs to the gravity models include the balanced production and attraction vectors and the friction matrices. Each trip purpose is subject to the same iterative balancing process. The results of this sub-model are four trip purpose matrices that describe the travel between all origins and destinations.

6.3 Gravity Model Calibration

Calibrating the gravity model consists of evaluating the parameters of the impedance function (or the values in the friction factor table) so that the gravity model reproduces, as closely as possible, the base year productions and/or attractions and the base year trip length distribution.

TransCAD provides a procedure that calibrates a friction factor lookup table, a K-Factor matrix, and exponential, inverse power, and gamma impedance functions. Regardless of the model being calibrated, the calibration procedure requires:

a base year P-A matrix;

a base year impedance matrix; and

a zone layer

All of the calibration procedures use the base year P-A matrix and the impedance matrix to generate the Observed Trip Length Distribution (OTLD), and the aim is to calibrate the model such that this OTLD is reproduced as closely as possible.

Table 6.1 shows the average calibrated trip lengths using the trip distribution parameters and friction factors developed for City of Peterborough. The calibrated parameters by purpose are:

HBW – 0.085

HBO – 0.140

NHB – 0.095

The entries in the table indicate that the functions are able to replicate the average trip lengths to within about 1.16% or about 15 seconds for HBW trips and to within 1/100th of a minute for the other two trip types.

TABLE 6.1: TRIP DISTRIBUTION CALIBRATION

Trip TypeObserved Average

Travel Time (min)

Predicted Average Travel Time

(min)Difference (%)

Home Based Work (HBW) 21.50 21.25 -1.16%

Home Based Other (HBO) 18.02 18.01 -0.06%

Non Home Based (NHB) 19.25 19.24 -0.05%



Another measure of the accuracy of the distribution model is to compare the trip length frequency distributions of the estimated person trips from the model to the observed person trips from the household survey. The coincidence ratio is one such measure. The coincidence ratio is the ratio in common between two distributions as a percentage of the total area of those distributions. In general, the coincidence ratio measures the percent of the area that “coincides” between the two curves. This ratio for each trip purpose should be at least 60 percent. For Peterborough, all purposes exceed the 60 percent target.

6.4 Mode Split and Auto Occupancy

Given the dominance and ubiquitous use of the auto share in City of Peterborough, the development of a mode split model was not deemed to be worthwhile and was outside the TOR for the study. Therefore, the resulting person trip demands were then converted to vehicle trips through the application of global auto person trip share and auto occupancy factors which were calculated from the TTS database. (Table 6.2)

TABLE 6.2: MODE SHARE AND AUTO OCCUPANCY

Purpose % Auto PersonsAuto -

OccupancyHBW 94.9% 1.03HBO 91.7% 1.24NHB 98.1% 1.14



7.0 EXTERNAL PASSENGER VEHICLE TRAFFIC

Passenger travel demand to, from and through Peterborough area is an important component of the traffic flows on the City of Peterborough road system. These data were collected from a number of sources and as illustrated in Figure 3.3 were added to the internally generated travel demands to complete the passenger travel demand matrices.

7.1 Data Sources

Transportation Tomorrow Survey Data – Travel demand to and from the west of Peterborough was supplemented with the O-D information available in the TTS database.

7.2 Methodology

The above data sources provided the combined Internal-to-External (I-X), External-to-Internal (X-I) and External-to-External (X-X) travel demand portions of the travel matrices. These demands were subsequently added to the Internal-to-Internal (I-I) travel demands from the trip distribution and model split modules to complete the auto demand matrices. These data sources were used in the following manner:

the TTS data were processed so that the origin-destination location information was consistent with the updated TAZs and both were imported into TransCAD matrices.



8.0 MODEL SPEEDS AND VDF EQUATIONS

Through the modelling process enhancements to the Volume-Delay Functions and Model Speed estimation were made. These are described below.

8.1 Data Sources

In order to calibrate and refine the VDF functions, a comprehensive dataset of traffic operating speeds was required for each functional classification and through a range of V/C values. It was important that data were collected on links that are experiencing as broad a range as possible to ensure that the VDF functions replicate the delay and speed conditions that are occurring on these links. In order that the V/C values were accurately represented, the measured volumes on the links were used. Figure 8.1 illustrates the location of 2008/2009 ATR data that was provided by the City of Peterborough. In addition, the red links illustrate the links measured for overall travel time (which included delay experienced at intersections) and mid-block operating speeds (a measure of free-flow speed). Overall, data were collected on about 169 km of roadways which was down as follows:

Freeway Ramp 1.9 km

Freeway 13.5 km

Arterial 89.3 km

Collector 48.8 km

Local 14.3 km

Private 1.0 km

Given that there was an assumed average operating speed of about 40 km /h and that some deadheading was required to accomplish efficient routing of the network, it required about 4.5 hours to complete the sample the entire proposed network. To ensure statistical reliability, three days of sampling was undertaken. To improve sampling efficiency, to provide a full range of V/C conditions and to respect timing and budgetary constraints, sampling was undertaken during the AM and PM peak periods.

8.2 Methodology

The data collected were used in a two-fold purpose. Firstly, the average travel speed which gives consideration to delay experienced at intersections was plotted against the V/C ratio measured for the section and used in the model. The link-based BPR functions were reviewed against these plots to determine if the BPR function is indeed the most applicable given the observed shapes of the delay curves. Where necessary, further assessment of BPR, Conical, Logit-Based, Acelik and generalized cost functions were conducted to determine if the shape of the observed volume-delay relationship better followed other curves. Following this assessment, the VDF functions were re-calibrated to the observed data to a statistically-valid degree. In addition to the above, the observed mid-block (undelayed) operating speeds were used as a surrogate for the free flow speeds (desired undelayed travelling speed) that are desired on the links. This method improved the overall estimates of delay using the refined VDF functions and contemporaneously refines the network assignment. Subsequently, for each roadway segment, the average loaded travel speed (one that



gives consideration to the delays experienced at intersections) were then compared to the calculated V/C ratio and plotted against the VDF functions that have been used in the model development. The average loaded speed by functional class from above was used as the starting speed for each functional class.



Figure 8.1: Travel Time Sections Studied

City of Peterborough Model Update Figure 8.1Travel Time Sections Studied


LegendATR Traffic DataCollected in 2008/09

Proposed Travel Time and Travel Speed Measurement Location



8.3 VDF Development

Figure 8.2 illustrates the scattergram of the overall average loaded speeds compared to the V/C ratio calculated for the roadway segment. Note that the V/C ratios are calculated based on the respective hourly volume that was provided for each of the 2009 ATR locations provided. That is, if the speed sample was measured at 2:30 PM, the volume data associated to 2:30 PM were used to calculate the V/C ratios. The assumed capacity for the link under question was extracted for the model. The information presented in Figure 8.2 shows that there is generally a wide variation in the average travel speed that is occurring on the links across all V/C values. However, there was a general trend of decreasing average speed with increasing V/C. The trend line shown on the graph generally indicates that the average travel speed reduces by about 0.7 km/h for each 0.1 change in V/C.

8.4 VDF Calibration and Validation

The data collected for each roadway segment were reduced to extract the average travel time (including delays at intersections) and the average peak mid-block speed. Table 8.1 summarizes these two measures by functional classification. The entries in Table 8.1 show that:

The overall average travel speed was 43.5 km/h, while the average peak speed was more than 53 km/h, or about 22% (10 km/h) higher;

The average travel speed generally reflected the pattern of decreasing overall average speed with decreasing functional classification; and

The average loaded speeds were generally lower than those suggested by the current model calibration.

TABLE 8.1: AVERAGE LOADED SPEEDS VS. AVERAGE PEAK SPEED WITHIN FUNCTIONAL CLASSIFICATIONS

Functional ClassAverage

Travel Speed

Average

Peak Speed

Freeway Average 89.4 102.0

Highway Average 43.8 66.2

High Capacity Arterial Average 46.6 54.4

Medium Capacity Arterial Average 43.6 52.6

Low capacity Arterial Average 38.3 48.1

High Capacity Collector Average 41.4 51.7

Low Capacity Collector Average 41.2 52.2

Local Average 35.7 50.9

Grand Average 43.5 53.2



Figure 8.2: Observed Speed vs. V/C Ratio

City of Peterborough Model Update Figure 8.2Observed Speed vs. V/C Ratio


y = ‐7.0463x + 46.949R² = 0.0126

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8.4.1 Comparison to Existing VDF Functions

The above data were then re-evaluated to determine the average loaded travel speed compared to the V/C ratio and plotted against the VDF functions that have been used in the model development. Figure 8.3 illustrates the average loaded speeds compared to the V/C ratio calculated for the roadway segment. The calculated average loaded speed by functional class from above was used as the starting speed for each functional class. The following observations are noted:

the shape of the VDF function generally follows the observed average loaded speed pattern albeit it appears to be slightly flatter; and

it appears that the effect of congestion begins to affect average loaded travel speed by a V/C of about 0.3.

8.4.2 Existing VDF Calibration Conclusion

Based on the foregoing, it is concluded that:

the average speeds within the current model are higher than those observed, indicating that global reductions in the free speeds are warranted;

the existing model VDF functions generally well-represent the observed Speed-Delay relationships;

potential improvements to the VDF function include improving the sensitivity to V/C to begin closer to a V/C of 0.3 and flattening of the VDF curve.

8.4.3 Adopted VDF Enhancements

Based on the foregoing, the following are suggested changes to the modelling framework:

in general the link free speeds, be set to the average observed speed by functional class grouping (Arterial, Collector, Local), subject to calibration adjustments; and

the BPR formulation be implemented such that the alpha constant reflects the function classification under consideration and the exponent on the V/C term remain at 4. (Figure 8.4).

8.5 Results

In the redevelopment of the Volume-Delay Functions, data were collected with respect to the average loaded travel speed. This information was used to refine the VDF equations within the model. The resultant model loaded speeds compare as follows:

Overall the average loaded speed is within 9% of the observed average mid-block speed;

For arterial roads, the average loaded speeds are within 12-15% of the observed average mid-block speeds; and

For collector roads, the average loaded speeds are within 12-20% of the observed average mid-block speeds.



Figure 8.3: Observed Average Travel Speed vs. V/C and Existing VDF Functions

City of Peterborough Model Update Figure 8.3Observed Average Travel Speed vs. V/C

and Existing VDF FunctionsParadigmwww.ptsl.com

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5

Loaded Speed (km/h)

V/C Ratio

High Capacity Arterial

Medium Capacity Arterial

Low Capacity Arterial

High Capacity Collector

Low Capacity Collector

Local

Observed Arterial

Observed Collector



Figure 8.4: Observed Average Travel Speed vs. V/C and Adopted VDF Functions

City of Peterborough TMP Figure 8.4Observed Average Travel Speed vs. V/C

and VDF FunctionsParadigmwww.ptsl.com

0.00

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60.00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5

Loaded Speed (km/h)

V/C Ratio

High Capacity Arterial

Medium Capacity Arterial

Low Capacity Arterial

High Capacity Collector

Low Capacity Collector

Local

Observed Arterial

Observed Collector



9.0 ASSIGNMENT

The final step in the modelling process is to assign the assembled auto vehicle demands to the roadway networks. The following describes this process.

9.1 Methodology

The vehicle assignment sub-model determines the actual path taken by the vehicle trips. The main inputs to this step are the auto trip matrices and the road network. The following are traffic assignment methods encountered in transportation planning practice, all of which are available in TransCAD:

All-or-Nothing Assignment (AON) - Under All-or-Nothing Assignment, all traffic flows between O-D pairs are assigned to the shortest paths connecting the origins and destinations. This model is unrealistic in that only one path between every O-D pair is used, even if there is another path with the same or nearly the same travel time or cost. Also, traffic on links is assigned without considering whether or not there is adequate capacity or heavy congestion; travel time is a fixed input and does not vary depending on the congestion on a link.

Stochastic Assignment – The assignment method distributes trips between O-D pairs among multiple alternative paths that connect the O-D pairs. The proportion of trips that is assigned to a particular path equals the choice probability for that path, which is calculated by a Logit route choice model. This method does not assign trips to all the alternative paths, but only to paths containing links that are considered "reasonable." A reasonable link is one that takes the traveler farther away from the origin and/or closer to the destination. The link travel time in this method is a fixed input and is not dependent on link volume. Consequently, the method is not an equilibrium method.

Incremental Assignment - Incremental Assignment is a process in which fractions of traffic volumes are assigned in steps. In each step, a fixed proportion of total demand is assigned, based on All-or-Nothing Assignment. After each step, link travel times are recalculated based on link volumes. When there are many increments used, the flows may resemble an equilibrium assignment; however, this method does not yield an equilibrium solution. Consequently, there will be inconsistencies between link volumes and travel times that can lead to errors in evaluation measures. Also, Incremental Assignment is influenced by the order in which volumes for O-D pairs are assigned, raising the possibility of additional bias in the results.

Capacity Restraint - Capacity Restraint attempts to approximate an equilibrium solution by iterating between all-or-nothing traffic loadings and recalculating link travel times based on a congestion function that reflects link capacity. Unfortunately, this method does not converge and can flip-flop back and forth in the loadings on some links. The capacity restraint method as implemented in some software packages attempts to lessen this problem by smoothing the travel times and by averaging the flows over a set of the last iterations. This method does not converge to an equilibrium solution and has the additional problem that the results are highly dependent on the specific number of iterations run. Performing one more or one less iteration usually changes the results substantially.

User Equilibrium (UE) - User Equilibrium uses an iterative process to achieve a convergent solution, in which no travelers can improve their travel times by shifting routes. In each iteration network link flows are computed, which incorporate link capacity restraint effects and flow-dependent travel times.

Stochastic User Equilibrium (SUE) - Stochastic User Equilibrium is a generalization of user equilibrium that assumes travelers do not have perfect information concerning network attributes and/or they



perceive travel costs in different ways. SUE assignments produce more realistic results than the deterministic UE model, because SUE permits use of less attractive as well as the most-attractive routes. Less-attractive routes will have lower utilization, but will not have zero flow as they do under UE.

System Optimum Assignment (SO) - System Optimum Assignment computes an assignment that minimizes total travel time on the network. Under SO Assignment, no users can change routes without increasing their total travel time on the system, although it is possible that travelers could reduce their own travel times. This method can be thought of as a model in which congestion is minimized when travelers are told which routes to use.

Based on experience in developing hundreds of models across North America, the User Equilibrium process models across has been found to provide the most reliable means of assigning trips. This step is implemented in TransCAD using the built-in assignment procedure “User Equilibrium Assignment”. This method is an iterative process. The first iteration loads trips on the shortest path between origin and destination and travel times are calculated based on the link volume delay functions. The next iteration re-assigns a percentage of the trips to a second optimal path and so on until the network is in a state of equilibrium (usually requiring 30-60 iterations). Outputs from this stage include: origin/destination travel times, link travel times, link speeds and link volumes for autos.

9.2 Validation

When validating the model’s assignment, reliance was placed on the FHWA’s “Model Validation and Reasonableness Checking Manual3” (MVRCM) to provide guidance with respect to the acceptable precision of the assignment modules within the planning model. Accordingly, assignment validation targets were set on three increasingly detailed levels of precision:

System-wide (VMT and Volumes);

Corridor Volumes (Screenlines); and

Link Specific Volumes.

9.2.1 System-Wide Vehicle Miles of Travel (VMT)

When evaluating the accuracy of the assignment, the first check was observed versus modelled Vehicle Miles of Travel (VMT). VMT is the product of the link volume and the link distance, summed over the desired geographic area and facility types. The observed VMT is a product of a comprehensive traffic count program. Since not every link in the network was counted for the validation year, estimates of observed VMT were developed.

In the case of the Peterborough model, the primary source of observed VMT is the traffic count database maintained by City staff. It is important to note that these data have been used as provided to the project team. No attempts have been made to rationalize the count data between stations along arterials, or to normalize the data into a consistent weekday PM peak hour (e.g. Thursday PM peak hour in October).

3 Model Validation and Reasonableness Checking Manual, FHWA, Barton-Aschman Associates Inc. and Cambridge Systematics Inc., 1997.



It is important to note that a cursory review of the data has indicated that within individual count locations there is significant variation in the traffic counts within the data. In some cases this variations was observed to be as much as 30%. This variation can affect the assessment of the calibration, particularly at the screenline and link level.

Further, data provided by City staff represented a sampling of about 250 locations within the City. According to the MVRCM, the modelled VMT should be within 5% of the observed VKMT on a network level. The observed traffic count data is insufficient to provide an overall assessment of the VKMT as the counts only cover about 9% of the estimated VKMT in the network. Nonetheless to assess the model’s performance comparisons were made where observed data were available.

The MVRCM also suggests that VKMT breakdown for populations such as Peterborough is typically as follows:

Freeway/Expressway 33-38%;

Major Arterial 27-33%;

Minor Arterial 18-22%; and

Collectors 8-12%.

In the case of the Peterborough model, the VMT breakdown in the count data provided does not follow this distribution. It should be noted however, that these are based on that travel patterns in American cities where they have a more developed freeway network for commuter traffic. Peterborough also has an overall lower density than many similar size populations in the US. Overall the modelled VKMT for the all functional classes is within 1.5% of the observed VKMT. More specifically, the modelled VKMT for the arterial functional classes which comprise 73% of the model network is within 2.2% of the observed VKMT. Medium capacity arterials which comprise 62% of the model network are calibrated within 0.5% of the observed VKMT.

9.2.2 System-Wide Traffic Volumes

Consistent with the MVRCM, the next level of validation of the highway assignment is the comparison of observed versus estimated traffic volume on the highway network. As noted above, the observed count data were derived from the traffic count data provided by the City.

As indicated in the MVRCM, traffic volumes were validated at the system-wide level by first comparing the overall assignment performance

The first level of validation was to compare observed versus estimated volumes for all links with counts. To compare the system-wide assignment performance, a scattergram of the counts versus the assigned volumes was prepared. The degree to which the scattergram follows a 45-degree line (i.e. observed = estimated) is a measure of the ability of the model to replicate the observed volumes. Figure 9.1 indicates that in the case of the Peterborough model, the observed and predicted volumes generally follow a 45-degree line.

The industry-accepted measure of the “Goodness of Fit” of the observed versus the modelled volumes is the Coefficient of Determination (R2). The MVRCM identifies that the Coefficient of Determination (R2) should be greater than 0.88. In the case of the Peterborough model, the Coefficient of Determination (R2) was



calculated to be 0.89, which exceeds this value indicating a strong degree of correlation between the modelled volumes and the observed traffic counts.



Figure 9.1: Scattergram Plot of Observed vs. Predicted Directional Volumes (PM Peak Hour)

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Another important industry-accepted measure of the “Goodness of Fit” of the modelled volumes compare to the observed traffic volumes is the Root Mean Square Error (RMSE). This value is calculated as follows:

The MVRCM does not provide strict guidelines with respect to the % RMSE values, though it is generally accepted that % RMSE values for models should be in the order of 35%. Referring to assignment results the % RMSE for the entire network is calculated at 30% which is consistent with these targets.

In terms of absolute volumes, the average loading compared to the average count indicated that volumes were predicted within 9-38 vehicles per direction (varies by direction) on high capacity arterial sections which is excellent, 32-56 vehicles per direction (varies by direction) on medium capacity arterial sections which is very good and within 45-66 vehicles per direction (varies by direction) on low capacity arterial sections, which is also very good. Accuracy on the high capacity collectors was acceptable with the average error being 90-98 vehicles per direction (varies by direction) on and excellent on low capacity collectors with the average error being 31-55 vehicles per direction (varies by direction). On average across all functional classifications, the volumes were predicted within 24-62 vehicles per direction (varies by direction), which is very good.

Therefore, based on the requirements outlined above, the system-wide calibration met FHWA and industry-standard requirements both in terms of VMT and traffic volumes.

9.2.3 Corridor Volumes

Having satisfied the overall system-wide calibration targets, the next level of investigation carried out was at screenlines. Typically, screenlines run across the model from edge to edge with sub-groupings referred to as “cut lines”. In the case of Peterborough, a combination of screenlines and cutlines were used.

The MVRCM provides guidance with respect to ADT volumes across screenlines and the maximum allowable deviation permitted. Figure 9.2 illustrates that lower volume screenlines have a higher allowable deviation (> 50%) than do higher volume screenlines (20%). Various agencies have established differing degrees of precision with respect to screenline assignments. For example, the State of Michigan uses 10% at screenlines for its statewide model. In the case of Peterborough, the MVRCM method targets which are based on the volume at the screen line were used to determine the acceptability of the assignment using the PM peak hour volumes rather than the ADT volumes and assuming the same relationship applies to the peak hour values.

For Peterborough, seven screenlines were developed to verify the model assignment. Figure 9.3 illustrates the three screenlines used to verify north-south flows through the City (100, 300 and 500) and east-west through various sections of the City (200, 400, 600, 800 and 1000).

Figure 9.4 illustrates the screenline calibration across the screenlines running east-west across the City which monitor the north-south flows. Overall the screenline located north of Parkhill Road (Screenline 100) was calibrated within 7-10% of the of the observed traffic volumes which is very good. All links meet the maximum allowable deviation criteria, except University Road southbound. Ackison Road and University Road experience among the highest deviations. Within this screenline, there appears to be over-assignment on George Street.



Figure 9.2: Maximum Allowable Deviation Across Screenlines

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For the screenline north of Sherbrooke-Charlotte-Hunter (Screenline 300), the overall demand was projected to be within 3-8% of the observed demand, which is very good. All links fall within the acceptable deviation levels. Within this screenline, there appears to be on over-assignment on Reid Street and Wallis Drive and an under-assignment on Water Street.

For the screenline north of Lansdowne Street (Screenline 500), the overall demand was projected to be within 5-13% of the observed demand, which is very good. All links fall within the acceptable deviation levels. Within this screenline, there appears to be on over-assignment on Brealey Drive, and The Parkway and an under-assignment on Clonsilla Road, Monaghan Road and Ashburnham Drive.

Figure 9.5 illustrates the screenline calibration across the screenlines running north-south across the City which monitor the east-west flows. Overall the screenline located east of Brealey Drive (Screenline 200) is calibrated within 8-28% of the of the observed traffic volumes and is within the acceptable range. All individual streets experience deviations within the recommended ranges. Within this screenline, there appears to be over-assignment on Lansdowne and under-assignment on Sir Sandford Fleming Drive.

For the screenline west of Monaghan Road (Screenline 400), the overall demand was projected to be within 5-6% of the observed demand, which is excellent. All individual streets experience deviations within the recommended ranges. Within this screenline, there appears to be over-assignment on Charlotte Street and under-assignment on Sherbrooke Street.

For the screenline west of George Street (Screenline 600), the overall demand was projected to be within 3-13% of the observed demand, which is good. All individual streets experience deviations within the recommended ranges. Within this screenline, there appears to be over-assignment on Parkhill Road and under-assignment on Lansdowne Street and Hunter Street.

For the screenline crossing the Trent-Severn waterway (Screenline 800), the overall demand was projected to be within 5-10% of the observed demand, which is very good. All individual streets experience deviations within the recommended ranges. Within this screenline, there appears to be over-assignment on Nassau Mills Road.

For the screenline west of Television Road (Screenline 1000), the overall demand was projected to be within 2-8% of the observed demand which is very good. All individual streets experience deviations within the recommended ranges. Within this screenline, there appears to be a general under-assignment, except on Lansdowne Street.



Figure 9.5: East-West Screenline Calibration

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9.2.4 Link-Specific Calibration

As a final check of the model calibration, individual link assignments and select turning movements were reviewed. In some cases, adjustment to centroid connectors will be made as well as additional minor network changes were made to improve localized assignment, but did not affect the overall screenline calibration.



10.0 MODEL VALIDATION CONCLUSION AND FUTURE ENHANCEMENTS

Through the modelling process a number of future enhancements have been identified for consideration. These are described below.

10.1 Validation Conclusion

The Terms of Reference for the project identified that the updated model is to be “calibrated to base year (2006) conditions”. Based on the information above, the following is concluded:

The system-wide modelled VKMT on medium capacity arterials, which comprise more than 62% of the network, exceeded the FHWA required precision;

The comparison of observed versus predicted link volumes met the FHWA required precision;

The RMSE targets suggested by the FHWA are met;

In terms of absolute volumes, the average assigned volume compared to the average observed count indicates that volumes are predicted within 24-55 vehicles per direction overall; and

The modelled speeds provide supportable accuracy for use in the evaluation of alternatives for the TMP project.

Therefore, the updated model calibration meets or exceeds industry accepted standards and thresholds and as such, is suitable for use in preparing assessments of performance and travel demand forecasts.

10.2 Future Model Enhancements

While it was not possible to address all of the model shortcomings that have been identified within the time and budgetary constraints of the project, the following sections offer suggestions for future model upgrades.

10.2.1 Temporal Models

Appropriate consideration to developing an AM peak hour model should be given. Most government agencies are now carrying models for two peak hours as each has unique transportation issues. Better overall transportation solutions are provided when both peak periods are considered.

10.2.2 Special Generators

Appropriate consideration to including special generators should be given. Most modern transportation planning models are now carrying special generators which have unique trip generation characteristics such as major tourist attractions, hospitals, stadiums and arenas, universities and colleges, etc. Better overall transportation solutions are provided when these are considered.

10.2.3 External Travel Demands

It is recommended that as soon as practically possible, the external travel demands to/from the west be updated with information that is available from the Ministry of Transportation in their GGH study.



11.0 BASE YEAR (2006) CONDITIONS

This section documents current traffic conditions, operational deficiencies, and constraints experienced by the public travelling within Peterborough in the base year (2006). The operational deficiencies and constraints identified at this stage will provide input to understanding the current performance of the roadway network and will be fundamental to the process of defining future needs.

11.1 Deficiency Definition

Defining the condition against which future forecast traffic flows must be evaluated, it is necessary to firstly analyze the performance of the current roadway network and any capacity deficiencies that exist. For the purpose of this study, capacity deficiency is defined in relation to the mid-block PM peak hour planning capacity established in the model development.

In the model development (Section 2) capacities were defined at a level representing the maximum desirable flow volume for various link types. Table 11.1 summarizes these values. The entries in the table highlight the intended role of each class of facility within the various area types. For example a two-lane major arterial could carry up to 800 vehicles per lane per hour (e.g. Downtown Peterborough), while in areas such as Downtown Peterborough arterial facilities would only be expected to carry 700 vehicles per lane per hour.

TABLE 11.1: PLANNING CAPACITIES

Functional Classification Grade Planning Capacity

Freeway all 1800

Freeway Ramps Fwy. To Arterial 1300

Fwy. To Fwy. 1500

Highway Rural 1000

Arterial High 800

Medium 700

Low 600

Collector High 500

Medium 400

Local all 300

In terms of defining a deficiency, one is considered to exist when the volume to capacity (V/C) ratio is higher than 1.0, or the high end of LOS E. Table 11.2 summarizes the V/C ratios and relates them to the LOS for the purpose of establishing measures of performance in the network. The entries in the table indicate that once congestion levels reach 80% of the planning capacity, the LOS would be classed as LOS D, 90% would be LOS E and if the current or projected volume exceeds the planning capacity, the LOS would be classed as F.



TABLE 11.2: LEVEL-OF-SERVICE AND V/C RELATIONSHIP

From To

AFree-flow conditions with unimpeded maneuverability.Stopped delay at signalized intersection is minimal.

0.00 0.60

BReasonably unimpeded operations with slightly restricted

maneuverability. Stopped delays are not bothersome.0.61 0.70

CStable operations with somewhat more restrictions in makingmid-block lane changes than LOS B. Motorists will experience

appreciable tension while driving.0.71 0.80

DApproaching unstable operations where small increases in

volume produce substantial increases in delay and decreasesin speed.

0.81 0.90

EOperations with significant intersection approach delays and

low average speeds.0.91 1.00

FOperations with extremely low speeds caused by intersection

congestion, high delay, and adverse signal progression.1.00 -

Level of ServiceVolume-to-Capacity Ratio (V/C)

Description

Sources: Highway Capacity Manual, Special Report 209. Washington, DC: TRB, 1985. and Transportation Research Circular 212: Interim Materials on Highway Capacity. Washington, DC: TRB, 1980.

11.2 Base Year (2006) Network Performance

Using the model predicted flows which were calibrated to with a high degree of accuracy (R2 = 0.92) estimates of the overall network performance were estimated. A number of measures of effectiveness (MOE’s) were estimated:

Vehicle-Kilometres Travelled (VKMT) – This aggregates the number of vehicles on each link multiplied by the link length, for the various LOS groups identified above and by functional classification. Thus the more vehicle-kilometres travelled under LOS E and F, the worse is the situation.

Vehicle-Hours Travelled (VHT)– This aggregates the number of vehicle on each link multiplied by the time spent on the link, for the various LOS groups above and by functional classification giving weight to congestion.

Average Speed (km/h) – This is the average travel speed that occurs by functional classification across the network providing an indication of the performance of the various network components.

Figure 11.1 summarizes the network MOE’s by LOS grouping. The entries in the table indicate that less than the vast majority (93%) of the network operates under good operating conditions (i.e. LOS A-D) with about 5% of the VKMT and VHT operating under congestion and a very small percentage of the overall network experiencing failure conditions.

Figure 11.1 also summarizes the network MOE’s by roadway functional classification. The entries at the rightmost column of the table indicate the overall average operating speed is about 62 km/h. The average operating speed on collector roads is about 6-8 km/h below the posted speed. Average travel speed on the arterial roads ranges from 48-60 km/h which is quite good. Similarly, average travel speeds of about 80 km/h on the highway and County road network is very good – indicating very little congestion.

In terms of the network use, about 44% of the VKMT and 49% of the VHT occurs on the Major Arterials with a further 44% of the VKMT and 34% of the VHT on the Highways.



Figure 11.1: Base Year Performance Measures

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11.3 Base Year (2006) Network Link Deficiencies

To review isolated areas of the network, a plot of the modelled volume compared to the planning capacity for each link within the system was prepared. The operation of entire network in terms of the volume to capacity ratios is illustrated in Figure 11.2. The links are colour coded to reflect the degree to which they are satisfactory. Green links are acceptable, yellow links are experiencing congestion and warrant further review, and red links exceed their desirable planning threshold and thus require consideration for improvement. Based on the data contained in the image, the vast majority of the network is found to be performing at acceptable LOS values. However, the graphic also illustrates that the following links warrant immediate review:

Parkhill Road West from Armour Road to Park Street;

Fairbairn Street from Highland Road to Parkhill Road West;

Monaghan Road from Weller Street to Charlotte Street;

Charlotte Street from Medical Drive to Aylmer Street; and

Hunter Street from Water Street to Burnham Street.



Figure 11.2: 2006 PM Peak Hour Network LOS

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12.0 LAND USE FORECASTS

12.1 Background

As detailed in the Model Documentation, the zonal demographics data is the key ingredient in producing travel demand forecasts. This section of the report describes the methodology and results of the population and employment forecasting methods used to derive these data.

12.2 Population and Employment Projections

The land use data and forecasts were extracted to review the growth patterns occurring in the study area. Using the Super Analysis Zones developed in the 2002 Master Plan Study (Figure 12.1) the data were compressed to 20-zone level for discussion purposes. Figure 12.2 summarizes the overall growth projections. The following is noted:

Over the 2006 to 2021 period, the study area is projected to increase by about 7,600 residents from its current level of about 95,046;

Over the 2021-2031 period, the study area is projected to further increase by about 4,650 residents to reach a total of about 107,299 residents by 2031;

A number of Super Zones are projected to experience an overall decline in the population levels as average occupancy levels per dwelling decrease.

Over the 2006 to 2021 period, the study area is projected to add by about 1,868 new jobs from its current level of about 47,165;

Over the 2021-2031 period, the study area is projected to not see any further increases in employment.

Figure 12.3 details growth projections with respect to the percentage increases within each of the planning periods and the net contribution to overall growth. The following is noted:

SAZ 9 accounts for 65% of the projected increase in population over the 2006-2031 period with SAZ 10 accounting for a further 15%;

Several SAZ’s are projected to experience a net decrease in population (SAZ’s 1, 3, 4, 5, 8, 16, 17);

SAZ 9 accounts for 32% of the projected increase in population over the 2021-2031 period with SAZ 6 and SAZ 7 accounting for a further 19% each;

Several SAZ’s are projected to continue experience net decreases in population (SAZ’s 1, 3, 5, 8, 13, 16, 17);

employment growth is much more spread out within the study area with the increases in any particular SAZ ranging from 0-14%;

SAZ 7 accounts for 14% of the projected increase in employment over the 2006-2021 period with SAZ 19 accounting for a about 13% and SAZ 2 accounting for a further 11%; and



according to official City of Peterborough and County of Peterborough forecasts, Provincial Policy requirements under the Places to Grow mandate will keep employment levels constant in the study area over the 2021 to 2031 period.



Figure 12.1: Super Analysis Zone (SAZ) Structure

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Figure 12.2: Projected Growth in Population and Employment (2006 to 2031)

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Figure 12.3: Relative Growth in Population and Employment (2006 to 2031)

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The above information was compared over time to discern any overall growth trends. The data depicted in Figure 12.4 shows that in general there are modest growth assumptions made for the study area over the 2006-2031 period. Growth projections for the first 15 years also outpace the rate in increase in the last decade of the planning period. In addition, the following is noted:

over the 2006-2031 period the population in the study area is projected to increase to about 107,300 residents, which represents an average annual rate of increase of about 0.5% per annum;

over the 2006-2031 period the employment in the study area is projected to increase to about 49,300 jobs, which represents an average annual rate of increase of about 0.16% per annum;

employment increases were held at 2021 levels for the 2021-2031 period; and

the forecasted rate of increase in population over to 2006-2031 period is projected to occur at a rate that is generally consistent with what has occurred historically, while the annual rate of increase in employment over the 2006-2031 period is forecast to reduce compared to historical increases.

The data prepared by the City’s planning staff is contained in the Appendices (Appendix A).



Figure 12.4: Study Area Population and Employment Trends

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

55000

60000

65000

70000

75000

80000

85000

90000

95000

100000

105000

110000

115000 2000

2005

2010

2015

2020

2025

2030

2035

Population

Employm

ent

Auto Trips

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13.0 FORECAST CONDITIONS

Using the forecast land use data described above as input to the planning model, future forecasts were prepared along with the Measure of Effectiveness to understand the overall network performance over time. The following sections detail the network performance in 2021 and 2031. It should be noted the for forecast planning horizons, it has been assumed that the mode shares values from the base year (2006) remain constant.

13.1 Travel Demand Increases

13.1.1 Internal-based Travel Demands

Using the travel demand model and the above inputs, the forecast PM peak hour travel demands were prepared. Using the Super Analysis Zones (SAZ) developed in the 2002 Master Plan Study (Figure 12.1) the demands were compressed to the SAZ level for discussion purposes. The following is noted:

2006 Travel Demand (Figure 13.1) – Superzones 2, 3 and 7 produced the highest amount of HBW demand. Superzones 2 and 9 were the highest attractors of HBW trips. Superzone 2 produced the highest amount of HBO demand with Superzones 8 and 9 producing similar amounts at about 60-75% of the level of Superzone 2. Superzones 2 and 9 were the highest attractors of HBO trips. Superzone 2 produced the highest amount of NHB demand with Superzones 3, 7 and 9 producing similar amounts at about 60-80% of the level of Superzone 2. Superzone 2 attracted the highest amount of NHB demand with Superzones 3, 7 and 9 attracting similar amounts at about 65% of the level of Superzone 2.

2021 Travel Demand (Figure 13.2) – Superzones 2, 3 and 7 produced the highest amount of HBW demand. Superzones 2 and 9 were the highest attractors of HBW trips. Superzone 2 produced the highest amount of HBO demand with Superzone 9 producing about 95% of the level of Superzone 2. Superzones 2 and 9 were the highest attractors of HBO trips. Superzone 2 produced the highest amount of NHB demand with Superzones 3, 7 and 9 producing similar amounts at about 60-80% of the level of Superzone 2. Superzone 2 attracted the highest amount of NHB demand with Superzones 7 and 9 attracting similar amounts at about 68% of the level of Superzone 2.

2031 Travel Demand (Figure 13.3) – Superzones 2, 3 and 7 produced the highest amount of HBW demand. Superzones 2 and 9 were the highest attractors of HBW trips. Superzones 2 and 9 produced the highest amount of HBO demand. Superzones 2 and 9 were also the highest attractors of HBO trips. Superzone 2 produced the highest amount of NHB demand with Superzones 7 producing similar amounts at about 88% of the level of Superzone 2. Superzone 2 also attracted the highest amount of NHB demand with Superzones 7 and 9 attracting similar amounts at about 72-80% of the level of Superzone 2.



Figure 13.1: 2006 Internal Travel Demands

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13.1.2 External Travel Demands

A number of forecasting methods related to the external travel demands were reviewed and discussed. These included tying future external travel demand to population and employment growth outside the area, making use of data available from other studies such as the Places to Grow study. It was decided that the most expedient method of predicting future external demands would be to use a growth factor approach for this component of travel demand. An annual growth rate of 1.0% based on the population growth forecasts provided for in the Places to Grow Report was used to factor the external passenger traffic.

13.1.3 Total Travel Demands

Together the internal travel demand increases combined with the forecast increases in external travel demands suggest that by the year 2031, travel demands on the system will have increased by about 7000 vehicle trips or 27% over 2006 conditions. Table 13.1 summarizes the net changes by planning horizon.

TABLE 13.1: TOTAL TRAVEL DEMAND INCREASES

Horizon YearTotal Auto

Vehicle Trips

Relative Increase

Percent Increase

Annual Rate of Increase

2006 25403

2021 28988 3585 14.1% 0.9%

2031 32357 3369 11.6% 1.1%

13.2 Committed Road Network Improvements

While the City and County have a 10-year Capital Works budgets, there certainty of the projects is subject to annual review. In discussions with City staff it was felt that only the following projects should be considered as committed for the purpose of evaluating future network performance:

Hospital Access Road – A two-lane limited access arterial road to be constructed from Clonsilla Avenue to Parkhill Road West with signalized intersections at Sherbrooke Street West, Hospital Drive, Weller Street and Parkhill Road West; and

Lansdowne Street West – Widen to a 5-lane cross-section from the west City Limits to Kawartha Heights Boulevard/Spillsbury Drive from its current 2-lane cross-section.

In addition to the above, collector road networks will be developed within each of the growth areas. Table 13.2 summarizes the assumed collector road networks and the status as defined at the time of development of the model.



TABLE 13.2: NETWORK ASSUMPTIONS WITHIN GROWTH AREAS

Area General Growth Area Roadway Network Assumptions Status

JacksonEast of Brealey Drive/Ackison Road. North and

South of Parkhill Road west.

Extension of Nornabell Avenue northeasterly to connect with Parkhill Road west, east of Brealey Drive Collector Road

north of Parkhill Road

Conceptual only. Schedule G on OP, Collector Road alignment south of Parkhiil Road illustrated on Plan. Collector Road north of Parkhill

approved on draft plan of subdivision.

Lilly LakeWest of Fairbairn Street and South of Lilly Lake

Road.

Three potential collector connections to from the south to Lilly Lake Road and a signle conneciton to the east to

Fairbairn Street.

Conceptual only. No Approved Secondary Plan or Draft Plans of

Subdvision.

Chemong WestArea bounded by Fairbairn Street, 3rd Line of Smith (County Road 19), Chemong Road and

Towerhill Road

Extension of Towerhill Road northwesterly to connect with County Road 19, west of Chemong Road. Connector loop

from Towerhill Road northeasterly to Chemong Road, north of Milroy Drive.


Subdvision.

Chemong EastArea bounded by Hillaird Street, County Road

19, Chemong Road and Milroy Drive

Connector Loop linking Chemong Road north of Milroy Drive to County Road 19, west of Hillard Street. South Easterly connection from loop road to Milroy Drive, west of Milroy

Park.

Conceptual only. No approved Secondary Plan. Draft Plan of

Subdivision with collector road network recommended by Planning Committee subject to Council approval, March 14,

2011.

Carnegie WestArea bounded by Hillaird Street, City Limit extension of County Road 19, Cumberland

Avenue and mid-block north of Parkway ROW

Realignment of Cumberland Avenue east of Unagava northwesterlky to connect to County Road 19. Termination

of Cumberland as a through street east of Ungava. Heritage Trail extended westerly to connect to realigned Cumberland Avenue. Potential local road connections to

Cumberland Avenue and Heritage Trail.


Subdvision.

Carnegie EastArea bounded by mid-block north of Parkway

ROW, City Limit, Cumberland Avenue and Carnegie Road

Heritage Trail extended westerly to connect to realigned Cumberland Avenue. Potential local road connections to

Heritage Trail.

Conceptual only. No Approved Secondary Plan. Approved Draft Plan of

Subdivision for east portion of the growth area.

LiftlockArea bounded by Trent Canal, Rail line, Television Road and Parkhill Road East.

Extension of Ashburham Road northeasterly to connect to Parkhill Road East, west of Television Road. Realignbment of MacFarlane Avenue at extension. Loop connector from

Old Norwood Road to Television Road.


Subdvision.

ColdspringsArea between Ottonabee River, Highway

7/115, Wallace Point Road, and Driscoll RoadApproved Collector Network. Minor local connection

between Kennedy Road and MacNamara Road.

Secondary plan not approved. Draft Plan of Subdivision north portion of area only.

Collector Road connections shown as part of area Transportation Analysis in

support of Secondary Plan.

Auburn NorthArea between Trent Canal and Ottonabee River,

south of Nassau Mills RoadTwo loop collector roads with access only to Armour Road.

Approved Secondary Plan. Schedule N on OP illustrates approved collector

road pattern.



13.3 Future (2021) Network Performance

Figure 13.4 depicts a number of performance measures anticipated to occur by 2021 within the existing plus committed network. The upper table depicts the VKMT and vehicle hours travelled (VHT) that are occurring in the network within the varying operating conditions. The entries in the table reveal that about 94% of the VMKT and about 92% of the VHT occur in uncongested conditions. About 3.6% of the VKMT and 5.3% of the VHT occur in severely congested conditions, with the balance experiencing high levels of congestion and delay.

The lower table in Figure 13.4 depicts similar information, but is summarized by functional classification. The entries in the table show that the majority of the travel demand occurs on the highways, high capacity arterials and medium capacity arterials. Further that the average operating speed on these facilities is acceptably high, indicating an absence of systemic congestion.

The operation of entire network in terms of the volume to capacity ratios is illustrated in Figure 13.5. The network links are colour coded by V/C ratio and drawn proportional to the volume experienced on each link. Based on the data contained in the image, the vast majority of the network is found to be performing at acceptable LOS values. Based on the increased congestion to 2021 the following deficiencies are noted:

Parkhill Road East/West from Armour Road to Chemong Road;

Parkhill Road West from Fairbairn Street to Monaghan Road;

Towerhill Road from Hilliard Street to Chemong Road;

Monaghan Road from Weller Street to Charlotte Street;

Charlotte Street from Medical Drive to Aylmer Street

Fairbairn Street from Highland Road to Parkhill Road West;

Chemong Road from Sunset Road to Wolsely Street;

Hunter Street from Aylmer Street to Armour Road; and

Clonsilla Avenue from The Parkway to Goodfellow Road.



Figure 13.4: 2021 Performance Measures

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13.4 Future (2031) Network Performance

Figure 13.6 depicts a number of performance measures anticipated to occur by 2031 within the existing plus committed network. The upper table depicts the VKMT and vehicle hours travelled (VHT) that are occurring in the network within the varying operating conditions. The entries in the table reveal that about 50% of the VMKT and about 48% of the VHT occur in uncongested conditions. About 24% of the VKMT and 28% of the VHT occur in severely congested conditions, with the balance experiencing tolerable levels of congestion and delay.

The lower table in Figure 13.6 depicts similar information, but is summarized by functional classification. The entries in the table show that the majority of the travel demand occurs on the highways, high capacity arterials and medium capacity arterials. Further that the average operating speed on these facilities is acceptable in spite of the increases in systemic congestion.

The operation of entire network in terms of the volume to capacity ratios is illustrated in Figure 13.7. The network links are colour coded by V/C ratio and drawn proportional to the volume experienced on each link. Based on the data contained in the image, the vast majority of the network is found to be performing at acceptable LOS values. Based on the increased congestion to 2031 the following deficiencies are noted:

Parkhill Road East/West from Armour Road to Monaghan Road;

Towerhill Road from Hilliard Street to Chemong Road;

Monaghan Road from McDonnell Street to Charlotte Street;

Charlotte Street from Medical Drive to Aylmer Street

Fairbairn Street from Lilly Lake Road to Parkhill Road West;

Chemong Road from Milroy Street to Parkhill Road West;

Hunter Street from Aylmer Street to Armour Road;

Armour Road from Lansdowne Street to Maria Street;

George Street South from Romains Street to Sherbrooke Street; and

Clonsilla Avenue from The Parkway to Goodfellow Road.



Figure 13.6: 2031 Performance Measures

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T%

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13.5 Performance Trends

The above information was compared over time to discern any overall growth trends. (Figure 13.8) The data depicted in Figure 13.8 shows that in general that despite the modest growth assumptions made for the study area over the 2006-2031 period, the congestion levels within the study will increase at a rate that outpaces the growth in demand. Growth projections for the last decade of the planning period also outpace the rate in increase in the first 15 years.

13.5.1 Arterial and Collector Performance Trends

While the overall information provided above shows that congestion on the roadway system is increasing, it must be kept in mind that a significant amount of the network is rural and uncongested and thus reduces the overall impact.

The performance of the arterial and collector network grid (Figure 13.9) is most important to the City of Peterborough. A subset of the above information was extracted to illustrate the estimated performance of the network over time.

Figure 13.10 illustrates the vehicle kilometres travelled (VKMT) on the arterial and collector roads segregated by the level of service (LOS) that is estimated to occur on these roads, assuming a collinear relationship with volume to capacity ratios (V/C). The arterial performance illustrated in the upper graph shows that between 2006 and 2031, the VKMT under LOS A-D will increase by about 114%, while the VMKT experiencing severe congested (LOS F) will increase more than four-fold (425%).

The collector performance depicted on the lower graph shows that overall in 2006 the majority of the collector roads operate in an uncongested fashion with more than 98% of the VKMT occurring in LOS A-C conditions. By 2031 however, the VKMT experiencing LOS E/F on these roads increases from about 1.6% on 2006 to about 9% in 2031.

Figure 13.11 illustrates the vehicle hours travelled (VHT) on the arterial and collector roads segregated by the level of service (LOS) that is estimated to occur on these roads, assuming a collinear relationship with volume to capacity ratios (V/C). The arterial performance illustrated in the upper graph shows that between 2006 and 2031, the VHT under LOS A-D will increase by about 118%, while the VHT experiencing severe congested (LOS F) will increase more than four-fold (432%).

The collector performance depicted on the lower graph shows that overall between 2006 and 2031; the majority of the collector roads will operate in an uncongested fashion with about 98% of the VHT occurring in LOS A-D conditions. By 2031 however, the VHT experiencing LOS E/F on these roads increases from about 1.6% in 2006 to about 9.6% in 2031.



Figure 13.8: VKMT and VHT Growth Trends

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

220000

240000

260000

280000

300000 2000

2005

2010

2015

2020

2025

2030

2035

VKMT

VHT

Fig

ure

13

.8VK

MT

and

VH

T G

row

th T

rends

Para

dig

mw

ww.p

tsl.com

City

of Pete

rboro

ugh T

ransport

ation M

aste

r Pla

n

An

nu

al R

ate

of I

ncr

ea

se =

1.4

%A

nn

ua

l Ra

te o

f In

cre

ase

= 1

.2%

VK

MT

VH

T



Figure 13.9: Arterial and Collector System

Fig

ure

13

.9A

rter

ial an

d C

olle

ctor

Sys

tem

Para

dig

mw

ww.p

tsl.com

City

of Pete

rboro

ugh T

ransport

ation M

aste

r Pla

n



Figure 13.10: Arterial and Collector Performance (VKMT)

Fig

ure

13

.10

Art

eria

l an

d C

olle

ctor

Per

form

ance

(VK

MT)

Para

dig

mw

ww.p

tsl.com

City

of Pete

rboro

ugh T

ransport

ation M

aste

r Pla

n

Art

eria

l

Col

lect

or

0

20000

40000

60000

80000

100000

120000

140000

2006

2021

2031

VKMTHorizon Year

LOS F

LOS E

LOS A‐D

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

2006

2021

2031

VKMT

Horizon Year

LOS F

LOS E

LOS A‐D



Figure 13.11: Arterial and Collector Performance (VHT)

Fig

ure

13

.11

Art

eria

l an

d C

olle

ctor

Per

form

ance

(VH

T)Para

dig

mw

ww.p

tsl.com

City

of Pete

rboro

ugh T

ransport

ation M

aste

r Pla

n

Art

eria

l

Col

lect

or

0

500

1000

1500

2000

2500

3000

2006

2021

2031

VHT

Horizon Year

LOS F

LOS E

LOS A‐D

0

50

100

150

200

250

300

350

400

450

2006

2021

2031

VHT

Horizon Year

LOS F

LOS E

LOS A‐D

Appendix A

Population and Employment Forecasts

Individual t zones population summary table

TAZ 2006 2021 203194 339 340 326101 51 45 39102 68 60 53103 14 12 11104 1 1 1105 24 21 19106 48 42 37107 0 0 0108 0 0 0109 47 41 36110 382 335 295201 805 1534 1644202 914 834 794203 183 471 443204 308 336 320205 919 948 923206 238 362 341207 729 665 633208 278 253 241209 264 241 229210 285 260 247211 162 148 141212 400 365 348213 190 173 165214 162 148 141215 234 213 203217 180 165 157218 238 217 207219 7 6 6220 481 439 418221 7 6 6222 354 323 307223 278 253 241224 567 601 572225 349 319 303226 555 507 482227 74 68 64228 653 595 567229 1164 1062 1011230 7 6 6231 632 576 658232 611 557 530233 86 78 74234 796 787 821235 1018 929 884236 377 405 477237 1034 943 898238 530 483 460239 9 8 8240 99 91 86241 0 0 0242 366 393 374243 504 460 438244 694 634 671245 560 511 486301 416 405 401

Page 1 of 10


TAZ 2006 2021 2031302 246 252 254303 182 193 198304 41 37 36305 2 2 2306 211 191 195307 76 69 78308 264 239 229309 55 49 48310 500 557 565311 86 78 75312 571 523 503313 311 281 270314 227 205 197315 477 431 414316 244 221 224317 311 309 314318 456 412 396319 293 265 255320 389 381 389321 631 618 630322 61 55 53323 66 75 83324 319 302 302325 147 147 153326 121 124 131327 94 99 107364 111 101 695401 27 24 23402 291 255 257403 205 177 174404 121 104 102405 733 731 760406 7 6 6407 48 41 41408 351 313 317409 528 467 468410 241 227 230411 153 142 146412 5 4 4413 471 410 409414 519 459 457415 373 326 326416 296 259 261417 373 326 326418 526 458 456419 216 190 193420 483 417 410501 0 0 0502 86 76 69503 370 334 313504 219 201 191505 238 217 206506 170 158 151507 832 813 798508 450 441 431509 1749 1607 1500

Page 2 of 10


TAZ 2006 2021 2031510 47 41 38601 650 539 574602 1160 981 1031603 58 51 51604 822 695 754605 219 180 193606 12 43 122607 35 282 779608 412 339 363609 470 387 414610 715 608 650611 19 15 16612 5 4 4613 221 232 232614 352 289 309615 650 572 572616 645 686 686617 594 632 627618 140 373 373619 77 68 68620 405 632 692621 0 0 0622 12 10 10701 25 25 25702 1 1 1703 210 280 326704 45 115 161705 198 268 314706 114 219 287707 49 119 165708 279 279 279709 12 12 12710 69 173 242711 62 132 178712 166 236 282713 9 9 9714 235 305 350715 10 10 10716 162 232 278717 124 194 240718 52 157 225719 3 72 118720 4 4 4721 83 188 256722 57 232 346723 32 137 206724 8 8 8801 924 861 842802 343 343 347803 174 169 171804 247 237 237805 152 148 151806 312 309 314807 71 66 65808 455 438 440809 216 208 209

Page 3 of 10


TAZ 2006 2021 2031810 327 312 311811 533 511 511812 911 856 843813 404 394 397814 563 543 542815 776 742 737816 435 416 413817 398 389 392818 223 219 220819 115 119 122820 243 238 238821 183 170 166822 1167 1095 1076823 608 585 584824 66 73 77825 18 28 34826 24 33 39827 7 7 7828 18 17 17829 641 616 614830 572 544 538831 183 181 183832 517 500 500833 58 73 83901 66 67 825902 122 127 122903 1198 1346 1250904 611 638 601905 412 478 479906 454 485 457907 297 333 356908 190 176 168909 173 159 152910 430 448 456911 0 0 0912 47 1180 1937913 323 344 331914 71 77 71915 197 581 511916 1103 1264 1129917 456 485 467918 598 646 635919 1081 1128 1063920 365 381 359921 268 285 287922 157 148 142923 78 73 70924 140 132 126925 217 205 196926 126 162 182927 306 319 307928 73 112 134929 75 79 76930 93 131 152931 91 95 91932 0 0 0

Page 4 of 10


TAZ 2006 2021 2031933 55 58 56934 13 125 736935 71 765 944936 370 1012 958937 906 1496 1439938 303 323 304939 512 545 513940 678 721 680941 512 545 513942 301 321 302943 727 773 729944 596 626 6021001 12 12 111002 14 14 131003 20 20 191004 848 1876 19831005 10 10 101006 216 241 2731007 453 353 3421008 248 304 3321009 435 404 4451010 954 1111 11111101 0 0 01102 0 0 01201 27 23 221202 64 731 11331203 24 101 2141301 4 4 41302 23 22 211401 1174 1218 12181402 49 45 451403 0 0 01501 158 154 1511502 126 170 2001503 1064 1437 16851504 599 809 9491505 97 131 154

Page 5 of 10

Individual t zones employment summary table

TAZ 2006 2021 203194 8 8 8101 240 242 242102 1855 1876 1876103 314 318 318104 0 0 0105 305 309 309106 699 707 707107 0 0 0108 196 198 198109 505 511 511110 389 393 393201 6 6 6202 34 34 34203 0 0 0204 1 1 1205 81 178 178206 44 44 44207 37 37 37208 9 9 9209 0 0 0210 16 16 16211 0 0 0212 22 22 22213 9 9 9214 3 3 3215 3 3 3217 9 9 9218 51 51 51219 2255 2330 2330220 10 10 10221 168 168 168222 153 153 153223 18 18 18224 34 34 34225 25 25 25226 12 12 12227 206 206 206228 54 54 54229 72 72 72230 84 84 84231 27 27 27232 245 245 245233 6 6 6234 229 220 220235 45 45 45236 162 162 162237 13 13 13238 64 64 64239 39 60 60240 1490 1514 1514241 0 0 0242 76 76 76243 76 76 76244 128 128 128245 136 136 136301 5 5 5

Page 6 of 10


TAZ 2006 2021 2031302 30 31 31303 6 6 6304 25 25 25305 1423 1436 1436306 83 84 84307 32 33 33308 242 245 245309 914 922 922310 271 273 273311 9 9 9312 52 52 52313 625 631 631314 452 457 457315 468 472 472316 328 331 331317 308 311 311318 164 165 165319 86 87 87320 94 95 95321 123 124 124322 86 87 87323 10 10 10324 6 6 6325 47 48 48326 7 7 7327 29 30 30364 20 20 20401 1278 1320 1320402 72 74 74403 47 49 49404 97 100 100405 84 87 87406 28 29 29407 81 83 83408 115 119 119409 34 36 36410 30 31 31411 10 10 10412 7 7 7413 19 19 19414 9 9 9415 16 16 16416 131 135 135417 62 64 64418 38 40 40419 6 6 6420 134 138 138501 0 0 0502 30 31 31503 48 48 48504 9 9 9505 0 0 0506 10 10 10507 221 221 221508 44 44 44509 86 87 87

Page 7 of 10


TAZ 2006 2021 2031510 304 305 305601 4 4 4602 33 34 34603 5 5 5604 697 710 710605 65 66 66606 20 20 20607 6 6 6608 27 28 28609 184 187 187610 80 81 81611 0 0 0612 196 200 200613 294 299 299614 527 537 537615 6 6 6616 22 22 22617 32 33 33618 38 39 39619 0 0 0620 331 337 337621 0 0 0622 14 14 14701 89 92 92702 196 203 203703 145 150 150704 113 117 117705 274 283 283706 317 327 327707 220 227 227708 44 46 46709 176 181 181710 265 274 274711 231 239 239712 777 803 803713 450 465 465714 992 1026 1026715 80 83 83716 343 355 355717 246 254 254718 411 425 425719 221 228 228720 933 964 964721 234 242 242722 284 294 294723 237 245 245724 199 206 206801 45 46 46802 157 157 157803 18 18 18804 24 24 24805 148 166 166806 19 19 19807 48 49 49808 25 26 26809 8 8 8

Page 8 of 10


TAZ 2006 2021 2031810 13 13 13811 70 71 71812 30 31 31813 32 33 33814 64 65 65815 52 53 53816 88 90 90817 123 126 126818 11 11 11819 73 75 75820 18 18 18821 16 16 16822 280 282 282823 369 371 371824 22 22 22825 29 30 30826 0 0 0827 68 69 69828 73 75 75829 601 606 606830 162 163 163831 12 12 12832 31 32 32833 90 92 92901 20 62 62902 246 287 287903 46 47 47904 3 3 3905 24 24 24906 12 12 12907 233 233 233908 0 0 0909 1 1 1910 29 30 30911 0 0 0912 18 18 18913 465 469 469914 19 24 24915 28 29 29916 113 115 115917 424 434 434918 190 208 208919 38 39 39920 34 35 35921 3 3 3922 3 3 3923 0 0 0924 23 23 23925 5 5 5926 23 23 23927 21 21 21928 0 0 0929 0 0 0930 3 3 3931 4 4 4932 0 0 0

Page 9 of 10


TAZ 2006 2021 2031933 9 9 9934 0 0 0935 13 13 13936 0 0 0937 88 90 90938 13 13 13939 12 12 12940 118 121 121941 161 165 165942 6 6 6943 54 55 55944 533 460 4601001 405 464 4641002 270 317 3171003 48 54 541004 50 56 561005 10 11 111006 105 105 1051007 12 13 131008 96 101 1011009 37 41 411010 39 43 431101 1316 1394 13941102 1019 1079 10791201 0 0 01202 39 103 1031203 0 0 01301 0 0 01302 6 6 61401 15 16 161402 1688 1824 18241403 0 0 01501 130 142 1421502 131 143 1431503 130 142 1421504 131 143 1431505 130 142 142

Page 10 of 10

Appendix B

Travel Demand Matrices

Fig

ure

B.1

Super

Anal

ysis

Zon

e (S

AZ)

Str

uct

ure

Para

dig

mw

ww.p

tsl.com

City

of Pete

rboro

ugh T

ransport

ation M

aste

r Pla

n

Not

e: S

AZ 2

0 is

Ext

erna

l

Fig

ure

B.2

20

06 D

eman

d M

atri

xPara

dig

mw

ww.p

tsl.com

City

of Pete

rboro

ugh T

ransport

ation M

aste

r Pla

n

SAZ

12

34

56

78

910

1112

1314

1519

20To

tal

198

294

164

5912

268

5935

6031

03

186

286

1161

224

291

355

318

961

133

130

389

389

914

315

1837

1212

5744

36

312

341

227

114

337

207

8930

831

267

28

5017

599

2642

425

108

7081

5147

828

134

333

421

721

783

6

535

6125

1840

295

1036

21

21

204

470

628

185

110

6782

260

9018

123

712

25

413

444

218

29

721

161

5723

1011

465

190

267

1912

01

3512

239

1228

843

341

153

4716

228

146

528

461

6019

13

7124

495

2635

912

641

814

640

2328

012

830

869

570

14

755

1880

431

25

1029

8359

6262

107

244

287

453

238

372

1387

1169

2934

3761

2031

592

83

265

619

121

31

12

10

10

08

18

132

1812

811

81

224

71

03

137

133

142

4631

1634

2067

232

18

310

135

3

150

813

33

48

1328

100

028

971

200

190

69

22

36

819

70

019

647

133

2021

611

6740

114

917

118

511

460

669

829

011

926

7462

2142

00

Tota

l10

6042

5321

0988

461

816

6892

429

0737

4890

377

3373

100

453

151

5443

2540

3

Fig

ure

B.3

20

21 D

eman

d M

atri

xPara

dig

mw

ww.p

tsl.com

City

of Pete

rboro

ugh T

ransport

ation M

aste

r Pla

n

SAZ

12

34

56

78

910

1112

1314

1519

20To

tal

199

299

167

6012

2610

6038

9435

03

227

332

1263

224

894

056

719

262

137

163

400

400

113

43

1520

4413

1473

4796

312

441

627

314

337

209

110

312

371

912

858

1869

228

62

425

108

7081

5148

1029

153

413

424

724

990

4

536

6125

1840

306

1152

21

21

236

520

628

188

111

6782

264

112

186

296

156

54

165

512

2031

723

178

6225

1012

687

209

352

2715

02

4514

299

1474

843

346

154

4816

231

182

536

547

7422

13

8425

573

2885

916

247

913

952

1733

915

930

980

788

15

863

1910

6237

07

1042

105

8062

6214

628

341

660

427

843

617

33

1174

3236

4066

2634

782

103

326

728

121

31

12

10

20

09

20

132

1812

811

81

223

81

04

142

140

142

4731

1634

2160

232

19

311

636

4

150

1015

43

512

1537

140

038

1193

259

190

610

22

38

923

90

024

758

162

2024

913

5546

317

219

721

516

370

491

643

414

1131

9283

2551

22

Tota

l11

5845

9022

1692

964

017

9011

9330

9845

7112

3389

3579

120

552

167

6510

2897

2

Fig

ure

B.3

20

31 D

eman

d M

atri

xPara

dig

mw

ww.p

tsl.com

City

of Pete

rboro

ugh T

ransport

ation M

aste

r Pla

n

SAZ

12

34

56

78

910

1112

1314

1519

20To

tal

198

303

165

5912

2911

5941

9633

03

237

357

1297

224

996

857

419

463

152

188

400

488

116

44

1520

4814

1617

5115

312

442

727

614

337

230

125

311

437

932

863

1875

330

47

425

111

7181

5153

1028

179

423

426

727

196

3

535

6225

1840

327

1056

21

21

255

548

630

204

118

7187

309

136

193

380

170

54

185

592

2322

723

182

6326

1013

910

020

840

228

150

249

1432

715

87

843

351

154

4716

253

206

528

614

7421

13

9126

618

3045

917

258

515

456

1744

422

638

313

1196

15

888

2513

7849

48

1045

110

8462

6817

028

749

262

430

847

618

99

1174

3336

4072

3033

832

113

355

772

121

31

12

10

20

010

21

132

1812

811

91

229

81

04

146

151

142

5234

1837

2584

252

111

313

843

0

150

1016

44

615

1546

150

044

1210

829

6

190

610

22

49

928

90

026

765

178

2026

915

0250

418

721

425

620

175

810

6948

314

1233

100

9828

5729

Tota

l11

9249

2722

9795

566

720

8114

3432

2757

3913

1787

3782

128

632

181

7364

3234

8

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