selection of a simulation software to model a...
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SELECTION OF A SIMULATION SOFTWARE TO MODEL A SMALL
SIGNALIZED SYSTEM OF A MULTILANE ARTERIAL IN
THE SOUTHEASTERN US
by
ELSA GEBRU TEDLA
A THESIS
Submitted in partial fulfillment of the requirements for the degree of Master of Science in the Department of Civil,
Environmental and Construction Engineering in the Graduate School of
The University of Alabama
TUSCALOOSA, ALABAMA
2009
Copyright Elsa Gebru Tedla 2009 ALL RIGHTS RESERVED
ii
ABSTRACT
Employment of traffic simulation tools has become a popular practice in traffic
operations analyses as the transportation system has become more complex and more frequently
congested. Most of the commercially available traffic simulation models work best for free-flow
or unsaturated conditions. Depending on the type of traffic condition and type of analysis, the
performance of simulation models varies and there is little information available to help the
analyst to select the most appropriate and accurate model for a given analysis. To address this
need, two traffic simulation tools, SimTraffic and AIMSUN, were evaluated and compared for a
congested arterial segment. Both simulation packages are designed to model almost any
combination of surface street and freeway facilities. In this paper, an arterial segment in
Tuscaloosa, Alabama (McFarland Boulevard) between 13th street and 31st street was coded and
simulated for AM, Mid day, and PM peak periods. The network was simulated 10 times for each
peak period using both simulation models, and average values were taken for comparison. Then
the network was evaluated using output measures of effectiveness (MOE) such as Vehicle Hours
Travel (VHT), Vehicle Miles Travel (VMT), average speed, and flow rate at the network level,
along with delay, travel time, and average speed at the arterial level, and delay and traffic
volume at a link level. Using statistical methods and graphical plots for comparison, each
simulation model was evaluated for its capability to replicate existing field conditions using
default and calibrated traffic parameters. In addition to accuracy, the models were also compared
with respect to ease of coding, and quality/usefulness of output. This report documents relevant
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results and calibration processes used for employing the models in future studies and practices
regarding congested arterials.
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LIST OF ABBREVIATIONS
AIMSUN Advanced Interactive Microscopic Simulator for Urban and Non-urban
Networks
CORSIM CORridor-microscopic SIMulation program
CV Coefficient of Variance
DYNASMART DYnamic Network Assignment Simulation Model for Advanced Road
Telematics
GETRAM Generic Environment for TRaffic Analysis and Modeling
HCM Highway Capacity Manual
HCS Highway Capacity Software
ITS Intelligent Transportation Systems
LOS Level of Service
MOE Measures of effectiveness
NB North Bound
ODBC Open Data Base Connectivity
SB South Bound
St. Dev Standard Deviation
VHT Vehicle Hours Travel,
VMS Variable Message Sign
VMT Vehicle Miles Travel
3-D 3 Dimensional
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ACKNOWLEDGMENTS
I am pleased to have this opportunity to thank the many colleagues, friends, and faculty
members who have helped me with this research project. Without their support and
encouragement, the completion of this thesis would not have happened.
I would like to thank my committee members: Dr. Daniel Turner, Dr. Jay Lindly, Dr.
Steven Jones, and Dr. Daniel Fonseca for serving on my committee and for their invaluable
feedback. In particular, I would like to thank my advisor, Dr. Daniel Turner for his patience and
excellent guidance throughout this research and throughout my academic career. If it was not for
his support and encouragement, this would not have happened. Dr. Turner, I cannot thank you
enough.
I am grateful for the support I had from friends and UTCA staff during my stay at UA.
I wish to thank Ms. Connie Harris and Dr. Janet Norton for their help in various matters. I like
to thank Ayse Narci for lending me a hand whenever I needed.
I would like to express my sincere thanks to my parents and to my family. I like to
thank my parents, Keki and Ababa, for their love and care throughout my life. Their prayers and
thoughts have got me where I am today in my life. I like to thank my husband, Getiye Yene
Fikir, for his endless love, support, and continuous encouragement when I did not think I would
ever finish this thesis. I am thankful and most fortunate to have him in my life.
Finally and most importantly, I would like to thank God for everything he has given me
and everything he has done for me. God, I know that you always have the best for me. I thank
you a million times.
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CONTENTS
ABSTRACT .................................................................................................................................... ii
LIST OF ABBREVIATIONS ........................................................................................................ iv
ACKNOWLEDGMENTS ...............................................................................................................v
LIST OF TABLES ...........................................................................................................................x
LIST OF FIGURES ....................................................................................................................... xi
CHAPTER ONE ..............................................................................................................................1
INTRODUCTION ...........................................................................................................................1
1.1. Project Objectives ................................................................................................................ 3
1.2. Report Organization ............................................................................................................. 3
CHAPTER TWO .............................................................................................................................5
LITERATURE REVIEW ................................................................................................................5
2.1. Evaluation of Modeling Capabilities of AIMSUN .............................................................. 5
2.2. Evaluation of Modeling Capabilities of SimTraffic ............................................................ 7
CHAPTER THREE .......................................................................................................................12
SIMULATION MODELS .............................................................................................................12
3.1. Car-following Model ......................................................................................................... 14
3.1.1. Car-following in AIMSUN ..................................................................................... 15
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3.1.2. Car-following in SimTraffic ................................................................................... 15
3.2. Lane-changing Model ........................................................................................................ 16
3.2.1. Lane-changing in AIMSUN .................................................................................... 18
3.2.2. Lane-changing in SimTraffic .................................................................................. 18
3.3. Gap-acceptance Models ..................................................................................................... 19
3.3.1. Gap-acceptance in AIMSUN .................................................................................. 19
3.3.2. Gap-acceptance in SimTraffic ................................................................................ 20
3.4. Background of Individual Models ..................................................................................... 20
3.4.1 AIMSUN .................................................................................................................. 20
3.4.2. SimTraffic ............................................................................................................... 23
CHAPTER FOUR ..........................................................................................................................26
METHODOLOGY ........................................................................................................................26
4.1. Modeled Project Area ........................................................................................................ 26
4.2. Project Data ........................................................................................................................ 27
4.2.1. Network Layout ...................................................................................................... 27
4.2.2. Traffic Demand Data .............................................................................................. 28
4.2.3. Traffic Control ........................................................................................................ 28
4.3. Comparison of Simulation Models .................................................................................... 31
4.4. Validation Data .................................................................................................................. 36
CHAPTER FIVE ...........................................................................................................................38
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RESULTS OF COMPARISONS ...................................................................................................38
5.1. Data Entry and Ease of Coding .......................................................................................... 38
5.2. Simulation Output .............................................................................................................. 40
5.3. Comparison of Simulation Results .................................................................................... 42
5.3.1. Model Comparison.................................................................................................. 42
5.3.1.1. Comparison of Network MOEs ........................................................................ 42
5.3.1.2. Comparison of Arterial MOE – Average Delay .............................................. 44
5.3.1.3. Comparison of Link MOE – Average Delay .................................................... 46
5.3.2. Comparison of Model Output and Field Data ......................................................... 47
5.3.2.1. Comparison of Arterial MOE – Travel Time ................................................... 47
5.3.2.2. Comparison of Arterial MOE – Average Speed .............................................. 49
5.3.2.3. Comparison of Link MOE – Link Volume........................................................ 51
5.4 Summary of Results ............................................................................................................ 52
CHAPTER SIX ..............................................................................................................................54
COMPARISON OF MODELS AFTER CALIBRATION ............................................................54
6.1 Model Calibration ............................................................................................................... 55
6.2 Calibration Outputs ............................................................................................................. 56
6.2.1 Arterial Travel Time ................................................................................................ 56
6.2.2 Arterial Speed .......................................................................................................... 58
CHAPTER SEVEN .......................................................................................................................60
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CONCLUSIONS AND RECOMMENDATIONS ........................................................................60
7.1 Conclusions ......................................................................................................................... 60
7.2 Recommendations ............................................................................................................... 62
REFERENCE .................................................................................................................................63
Appendix – A .................................................................................................................................66
Resources used in Simulation Modeling........................................................................................66
Appendix – B .................................................................................................................................69
Simulation Results Using Default Parameters - Graphs ................................................................69
Appendix – C .................................................................................................................................88
Simulation Results Using Default Parameters – Tables ................................................................88
Appendix – D .................................................................................................................................98
Hypothesis Tests – Before Calibration ..........................................................................................98
Appendix – E ...............................................................................................................................102
Simulation Results after Model Calibration - Graphs..................................................................102
Appendix – F................................................................................................................................109
Simulation Results after Model Calibration - Tables ..................................................................109
Appendix – G ...............................................................................................................................113
Hypothesis Tests – After Calibration. ..........................................................................................113
x
LIST OF TABLES
Table 3-1 Default Driver/Vehicle Parameters in AIMSUN and SimTraffic .................................25
Table 4-1 Definition of MOEs .......................................................................................................35
Table 5-1 AIMSUN Network MOEs - AM Peak Hour ................................................................ 43
Table 5-2 SimTraffic Network MOEs - AM Peak Hour .............................................................. 43
Table 5-3 Comparison of AIMSUN and SimTraffic MOE - AM Peak Hour .............................. 44
Table 5-4 Model Estimation of Arterial MOEs ............................................................................53
Table 6-1 Suggested Calibration Parameters for AIMSUN ......................................................... 55
Table 6-2 Suggested Calibration Parameters for SimTraffic ........................................................ 56
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LIST OF FIGURES
Figure 3 - 1 Lane-changing Zones .................................................................................................18
Figure 4 - 1 Study Network in AIMSUN Model .......................................................................... 29
Figure 4 - 2 Study Network in SimTraffic Model ........................................................................ 29
Figure 4 - 3 AM Peak Hour Traffic Volumes ............................................................................... 30
Figure 4 - 4 Mid Day Peak Hour Traffic Volumes ....................................................................... 30
Figure 4 - 5 PM Peak Hour Traffic Volumes ................................................................................31
Figure 5 - 1 Screen Shot of Arterial Report from the AIMSUN Model ....................................... 41
Figure 5 - 2 Screen Shot of Arterial Report from the SimTraffic Model ..................................... 41
Figure 5 - 3 Simulated NB Arterial Delay – AM Peak Hour ........................................................ 45
Figure 5 - 4 Simulated NB Arterial Delay – PM Peak Hour ........................................................ 45
Figure 5 - 5 Simulated Average Delay - PM Peak Hour .............................................................. 47
Figure 5 - 6 Simulated NB Arterial Travel Time vs. Field Travel Time - AM Peak ................... 48
Figure 5 - 7 Simulated NB Arterial Travel Time vs. Field Travel Time - PM Peak .................... 49
Figure 5 - 8 Simulated NB Arterial Speed vs. Field Speed - AM Peak ........................................ 50
Figure 5 - 9 Simulated NB Arterial Speed vs. Field Speed - PM Peak ........................................ 50
Figure 5 - 10 Simulated Volume vs. Field Volume - AM Peak ....................................................52
Figure 6 - 1 Simulated vs. Observed NB Travel Time - AM Peak ............................................... 57
Figure 6 - 2 Simulated vs. Observed NB Travel Time - PM Peak ............................................... 57
Figure 6 - 3 Simulated vs. Observed NB Average Speed - AM Peak .......................................... 58
Figure 6 - 4 Simulated vs. Observed NB Average Speed - PM Peak ........................................... 58
1
CHAPTER ONE
INTRODUCTION
Microscopic simulation models are useful tools that enable the traffic engineer to model
real-life traffic conditions without disrupting everyday conditions, making them a low cost
method to test potential traffic improvement measures. In addition, by using simulation models
theoretical improvements can be modeled before they are introduced in highway applications to
obtain the best result desired. While these tools enhance the analyses performed by traffic
engineers, the concern should be with accuracy of these models in replicating field conditions.
Due to continuing growth in traffic demand, a large proportion of the urban highway
system operates under saturated and oversaturated conditions throughout most of the day.
Concurrently, the search for improved operational procedures and computer simulation softwares
to handle different traffic conditions has increased, and has produced a large number of traffic
simulation packages in the current market. Transportation professionals are faced with a problem
of selecting the most appropriate of many commercially available traffic simulation tools for
analyzing saturated traffic conditions, since most of these tools were developed for, and are
better suited for, free-flow or unsaturated conditions. The increased effect of vehicle interaction
under saturated conditions makes it difficult to evaluate impacts of congestion accurately; hence,
if inappropriate tools are used to analyze oversaturated conditions, the analysis could lead to a
misleading result. Therefore, there is a need for selection, evaluation, and validation and/or
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calibration of the simulation tools to identify the one(s) that are best suited for analyzing
congested traffic highway components.
In this project, two microsimulation models: AIMSUN (Advanced Interactive
Microscopic Simulator for Urban and Non-urban Networks) and SimTraffic were compared to
determine their capabilities for simulating saturated conditions. These two highly accepted and
marketed simulation models were chosen based on expert recommendation, availability, and
literature review.
AIMSUN developed in 1997 at the Department of Statistics and Operational Research at
Technical University of Catalonia in Barcelona, Spain, is marketed by Transport Simulation
Systems (TSS). It is a microscopic simulator that can reproduce real traffic conditions of
different traffic networks on a computer. The behavior of every single vehicle is continuously
modeled throughout the simulation using several driver behavior models such as car-following,
lane-changing, and gap-acceptance (Barcelo, Ferrar, & Montero, 2001). AIMSUN is a highly
accepted simulation model in most European countries but is a relatively new simulation
package in the U.S.
SimTraffic is one part of software suite consisting of the coordinated models, Synchro
and SimTraffic. It was developed in 1994 by Trafficware, a Texas based company. Within this
suite, Synchro conducts static capacity analysis, signal optimization and coordination with
deterministic models (Park, Park, & Choi, 2004). Synchro also provides the coding of the
transportation network and the settings of traffic and driver related parameters required as inputs
to SimTraffic. SimTraffic performs a microscopic simulation and produces animation by tracing
the trajectory of a vehicle with its movement parameters and recorded simulation. SimTraffic is a
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traditional, highly accepted, and easy to use model that has been around for more than 15 years
in the U.S.
1.1. Project Objectives
The primary objectives of this project follow:
• Examine two popular simulation tools, AIMSUN and SimTraffic, to determine which
simulation model is best for the available data and traffic conditions for a congested
portion of McFarland Boulevard in Tuscaloosa, Alabama by performing the following
steps:
o Creating a study network using the two simulation models and running a
simulation of the network, and
o Validating the simulation outputs to existing traffic conditions for selected
measures of effectiveness (MOE),
• Document relevant results and calibration processes carried out during the study for
employing the tools in future studies and practices.
• Extend the previous research on AIMSUN and SimTraffic.
1.2. Report Organization
This report is divided into seven chapters. Chapter 1, “Introduction,” contains
background information, the significance of this research, the project objectives, and the research
plan followed to accomplish the project objectives. Chapter 2, “Literature Review,” provides a
summary of the literature review conducted during the early stages of the project. The literature
review concentrated on research efforts performed in the previous ten years that were believed to
be relevant to this project for evaluating the modeling capabilities of AIMSUN and SimTraffic.
Chapter 3, “Simulation Models,” provides detailed information about microsimulation models
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and the processing logic embedded in them. Chapter 4, “Methodology,” provides a detailed
explanation of the methods and procedures used during the project. It provides information about
the modeled project site, including the network layout, traffic control, and traffic demand data. In
addition, this chapter describes how the selected simulation models were coded to represent the
project site. Chapter 5, “Results of Comparisons,” presents the results and findings of the
comparison of the two simulation models using default parameters. Chapter 6, “Comparison of
Models after Calibration,” presents the calibration process undertaken for both models and
discusses the final simulation outputs. Chapter 7, “Conclusions and Recommendations,”
discusses some of the conclusions drawn from the study and associated recommendations to be
considered for future studies.
5
CHAPTER TWO
LITERATURE REVIEW
Many traffic simulation tools have been studied and evaluated extensively. Multiple
research papers have been written to help decide which simulation tools are better than others for
a particular job, or to compare two or more available traffic simulation models. In this study,
attention is given to studies that identify limitations or advantages of AIMSUN, SimTraffic, or
both. This chapter focuses on reviewing the studies performed in the previous ten years that are
relevant to this effort.
2.1. Evaluation of Modeling Capabilities of AIMSUN
Kondyli, Duret, & Elefteriadou (2007) performed an analysis to evaluate the capabilities
of CORSIM (5.1) and AIMSUN (5.0) in replicating freeway breakdown events. The authors
examined the breakdown process at freeway on-ramps and on merging operations under
congested and non-congested conditions using default values of each of the models. They
evaluated three flow parameters; maximum pre-breakdown flow (the highest one-minute flow
that occurred prior to occurrence of congestion), breakdown flow (the one-minute flow per lane
for the interval before the breakdown), and maximum queue discharge flow (the highest one-
minute flow per lane that occurred during congested conditions after the breakdown flow). A
total of three hours were simulated in both models while increasing the demand gradually every
10 minutes to record the exact breakdown point in time. They pointed out that both models have
strengths and weaknesses in reproducing the breakdown. Although the two models simulate the
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breakdown process differently, CORSIM appeared to give results closer to reality than
AIMSUN. In addition, the authors evaluated the effect of driver behavior parameters on
modeling breakdown conditions by performing a sensitivity analysis. Then they identified which
of the driver behavior parameters are most important in modeling the breakdown process on
freeways. Their network was not calibrated.
Cheu, Tan, & Lee (2003) compared GETRAM/AIMSUN (version 4.0) with PARAMICS
(version 4.0) using six different aspects: graphical user interface, network modeling capabilities,
traffic modeling, statistical simulation output, run-time performance, and other considerations
such as Application Programmer Interface (API) and technical support. A network in Singapore
consisting of a freeway and arterials was coded and run with default parameters using both
simulation models. The researchers found that GETRAM/ AIMSUN had strengths such as ease
of network coding, modeling transit systems, modeling basic ITS functions such as variable
message signs (VMS) and ramp metering, running performance, and others. On the other hand, it
lacked realistic graphical animation and dynamic graphic outputs, had fewer API functions and
plug-ins, and had less capability for modeling intersections and links than PARAMICS.
Xiao, Ambadipudi, Hourdakis, & Michalopoulos (2005) presented a procedure for
selection of appropriate simulation models and performed a comparative evaluation of AIMSUN
and VISSIM. The procedure followed qualitative criteria (e.g., input/output features, ease of use)
and quantitative criteria (e.g., accuracy of simulators, parameters involved). As a result, they
found that AIMSUN and VISSIM simulated a freeway segment with reasonable accuracy. They
also concluded that selection of the right simulation tool to be a subjective decision for the
particular job to be performed.
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Jones & Sullivan (2005) used AIMSUN to examine the effects of various alternatives to
improve traffic flow and travel time savings on a principal arterial. The alternatives were
categorized into five scenarios: existing conditions, urban interchange alternatives, parallel
corridor alternative, at-grade improvement alternative, and transit alternative. 3-D animation
capabilities of AIMSUN were used to visualize the proposed alternatives.
Hass (2001) used AIMSUN for the assessment of effects of development such as
increasing land use activities, implementation of a proposed “Bus Rapid Transit Project”, and
inclusive bus route changes in an increasingly developing area in New Zealand. This study found
that the existing and future traffic conditions could be reproduced with a good degree of
accuracy using AIMSUN and it was also recommended for future applications.
Boxill & Yu (2000) conducted an evaluation of 84 simulation models available at the
time to identify models that could be potentially applied in ITS-equipped networks. An in-depth
evaluation of the models showed that AIMSUN and PARAMICS could model almost any
combination of ITS systems but that both simulation models had limited validation and
calibration in the U.S. As a result CORSIM and INTEGRATION were recommended for ITS
applications.
2.2. Evaluation of Modeling Capabilities of SimTraffic
Using SimTraffic and CORSIM, Qureshi, Jitta, & Spring (2003) compared control delay
for actuated traffic signals with observed control delay from the field. In the analysis, both
SimTraffic and CORSIM estimated delays adequately for left-turn and through-turn movements,
but gave lower delay estimates for right-turn movements compared to observed delays.
A similar comparison was performed between SimTraffic and CORSIM by Xie &
Parkany (2002) to investigate the performance and capability of the two models for simulation of
8
signalized intersections. Traffic modeling and geometry modeling were investigated based on
qualitative analysis, while signal modeling was evaluated quantitatively using two measures of
performance: approach control delay and intersection control delay. The two simulators were
found to be equivalent and both gave LOS values similar to that of HCS (Highway Capacity
Software) when comparing control delay under a variety of traffic conditions. However, the
researchers believe SimTraffic showed more potential in simulating the microscopic
characteristics of driver reaction and vehicle performance. It was more accurate than CORSIM
with its 0.1 second simulation step, compared to CORSIM’s relatively larger increment of 1.0
second simulation step.
Selinger, Speth, & Trueblood (2003) conducted an analysis of CORSIM and SimTraffic,
both versions 5.0, to examine which software was best suited to the various types of traffic
analyses conducted by transportation professionals. The authors presented three case studies of
three different facility types: an isolated intersection, a coordinated arterial, and a freeway
facility. From the analysis of an isolated intersection case study, SimTraffic provided more
reliable results than CORSIM, since CORSIM did not account for moderate (less than 100
pedestrians per hour) pedestrian volume. As a result, SimTraffic was recommended for studies
along urban collectors and arterial roads where pedestrians create significant conflicts. In the
same study, for the analysis of coordinated arterials CORSIM provided results that were more
closely matched to existing conditions with less calibration effort than SimTraffic. This was due
to the effect of closely spaced intersections along the arterial system. SimTraffic has a gridlock
avoidance feature that will not allow vehicles to queue into an intersection, hence limiting the
number of vehicles that could queue across road intersections and reducing the number of
vehicles served in an hour. Based on the freeway case study, which was conducted specifically to
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determine calibration parameters within a freeway major weave area, SimTraffic could not
replicate field measured results for speed and density compared to CORSIM, even after
significant calibration effort. CORSIM was recommended for use on freeway facilities.
Trueblood (2001) compared results from CORSIM (version 4.3) and SimTraffic (version
4.0) on two arterial systems with low to moderate traffic volumes. Two subsystems, one with
typical and another with untypical roadway geometry and signal phases were selected for
comparison. Overall, the comparison of the two simulation models for performance measures
such as speed, volume, delay/LOS, and stops found that they were somewhat similar except for
discrepancies with the queue lengths. This discrepancy resulted from the different descriptions of
how each model considers a vehicle to be queued and how the models actually report the queue
lengths. The author stressed how crucial it is to understand each simulation model to interpret
and compare results.
Jones, Sullivan, Anderson, Malave, & Cheekoti (2004) performed a detailed evaluation
of three microscopic simulation modeling packages: CORSIM, AIMSUN and SimTraffic. The
research was intended to provide guidance on the weaknesses and strengths of each model, by
comparing them using a variety of criteria including ease of network coding, hardware/software
requirements, data requirements and appropriateness of default parameters, ease of calibration
and validation, among others. Each simulation model was evaluated using three corridor types
including an interstate with interchanges, a signalized principal arterial, and an urban collector.
All of them were in the City of Birmingham, Alabama. Even though the research did not rank
any model as best or worst, it pointed out the outstanding features of each model as well as the
limitations based on particular criteria. SimTraffic was the easiest model to use, and it produced
results that were closer to the observed traffic conditions than the other two models even before
10
the calibration effort. However, its lack of transit modeling, ITS modeling, and traffic
assignment could limit its use for complex urban models. CORSIM was found to be more
suitable for modeling larger urban networks than SimTraffic, as it can simulate transit and
parking even though it requires more effort for coding and generally tends to overestimate
roadway capacity. AIMSUN required a greater effort to construct and run an equivalent network,
and provided a smaller range of MOEs than the other two models. In addition, it was found to
overestimate link capacities, and therefore required more validation and calibration. Still,
AIMSUN was the only software tested to satisfy crucial requirements such as ITS and dynamic
traffic assignment modeling capability for large regional networks. This could not be matched by
SimTraffic or by CORSIM. The researchers recommended using a combination of the micro
simulation packages: SimTraffic for signal and corridor studies, and either CORSIM or
AIMSUN for larger urban models.
In summary, a literature review was conducted on research topics relevant to this project.
The two simulation models have been evaluated by many researchers. Most of the studies
identified limitations and advantages of the two models by comparing them with other
simulation models. AIMSUN was compared with models such as CORSIM, PARAMICS and
VISSIM for various modeling capabilities. It was studied for freeway break down events, ITS
applications, public transport and many others on different road types. Similarly, SimTraffic was
compared with CORSIM and AIMSUN for different parts of transportation network but mainly
for intersections. The literature review did not indicate if either of the models selected for this
study could be the most applicable for a specific congested arterial network.
11
In later chapters, this project will evaluate if AIMSUN or SimTraffic is an appropriate
model for arterial roadway superior to the other with respect to ease of coding, modeling
capability, accuracy, and similar criteria.
The next chapter provides information on microscopic simulation models in general and
detailed background information on AIMSUN and SimTraffic. The discussion includes car-
following, lane-changing, and gap-acceptance models embedded in the individual
microsimulation models. It also includes a description of the modeling features of AIMSUN and
SimTraffic.
12
CHAPTER THREE
SIMULATION MODELS
Traffic simulation is an abstraction of the traffic stream in reality, and the manner in
which the stream is represented is a significant characteristic of the simulation model used.
Traffic simulation models describe the movement of traffic flow based on theories of car-
following, lane-changing, and gap-acceptance, employing the fundamental traffic flow
characteristics between flow, density, and speed (Middleton & Cooner, 1999). There are three
categories of traffic simulation models: macroscopic, microscopic, and mesoscopic depending on
how they describe the traffic stream. Macroscopic models represent the movement of traffic flow
as a homogenous aggregate group rather than individual vehicles. They work based on a
mathematical relationship (deterministic process) developed through research (Lieberman &
Rathi, 2008). Such models are much faster to run but do not account for the stochastic variations
in traffic operations and the random components in driving behavior. HCM is a common
example of a macroscopic simulation model.
Microscopic models, on the other hand, describe the movement of traffic by tracing the
movement of individual vehicles in the system and considering the interaction between vehicles
in the traffic stream. These simulation models contain processing logic which describes how
each individual vehicle behaves in the traffic stream. Typically, vehicles are input into the
network using a probabilistic arrival distribution (a stochastic process), and they are traced
through the parts of the system on a second-by-second basis (Lieberman et al., 2008). Both
SimTraffic and AIMSUN are examples of microscopic models.
13
The third class of simulation models encompasses both microscopic and macroscopic
models, and it is referred to as mesoscopic. Such models typically incorporate the movement of
clusters or platoons of vehicles using equations that indicate how such clusters of vehicles
interact (Middleton et al., 1999). DYNASMART and INTEGRATION are two examples.
In microscopic traffic simulation, each vehicle is assigned a vehicle type, vehicle
performance characteristic, and driver characteristic. Drivers tend to travel at their desired speed
in each section, but the environment (i.e. preceding vehicle, adjacent vehicles, traffic signals,
signs, blockages, etc) conditions their behavior. During their journey along the network, all
vehicles are updated every simulation cycle according to vehicle behavior models: car-following,
lane-changing, and gap-acceptance. The simulation cycle or simulation step is a small time
interval, and the user can set this value within the range 0.1 – 1.0 seconds.
Each simulation cycle, the position and speed of every vehicle in the system is
manifested according to the following algorithm:
if (it is necessary to change lanes) then
Apply Lane-Changing Model
endif
if (the vehicle has not changed lanes) then
Apply Car-Following Model
endif
Once all vehicles have been updated for the current cycle, vehicles scheduled to arrive
during the cycle are introduced into the system, and the next vehicle arrival times are generated
(TSS, 2006).
14
In the following sections, detailed descriptions of the three models are given for car-
following, lane-changing, and gap-acceptance in terms of their application within AIMSUN.
These models are equally applicable to SimTraffic and to most microscopic simulation packages.
3.1. Car-following Model
The car-following model characterizes the relationship between a vehicle’s desired speed
and the distance headway to its preceding vehicle in the same lane (Rakha, Pecker, & Cybis,
2007). The car-following model implemented in AIMSUN and SimTraffic is based on Gipps’
model. It consists of two components, acceleration and deceleration. The first represents the
intention of a vehicle to achieve a desired speed, while the second reproduces the limitations
imposed by the preceding vehicle when trying to drive at the desired speed. The model states that
the maximum speed to which a vehicle (n) can accelerate during a time period (t, t + T) when it
is not obstructed by a preceding vehicle is given by the following formula (TSS, 2006):
( ))(*
),(025.0
)(*
),(1)(5.2),(,
nV
tnV
nV
tnVTnatnVTtnV +
−+=+
Where:
V (n, t) = the speed of vehicle (n) at time t
V*(n) = the desired speed of vehicle (n) for current section
a (n) = the maximum acceleration for vehicle (n)
T = is the reaction time
On the other hand, the maximum speed that the same vehicle (n) can reach during the
same time interval (t, t + T), according to its own characteristics and the limitations imposed by
the presence of the lead vehicle (vehicle n-1) is:
( 1 )
15
( ) { }
−−
−−−−−−−==+)1(
),1(),(),()1(),1(2)()()(,
222
nd
tnVTtnVtnxnstnxndTndTndTtnV
Where:
d(n) (< 0) = the maximum deceleration desired by vehicle (n)
x(n,t) = position of vehicle (n) at time t
x(n-1,t) = position of preceding vehicle (n-1) at time t
s(n-1) = the effective length of vehicle (n-1)
d'(n-1) = an estimation of vehicle (n-1) desired deceleration
The definitive speed for vehicle (n) during time interval (t, t + T) is the minimum of those
previously defined speeds:
V (n, t + T) = min { Va (n, t +T), Vb (n, t +T) }
Then, position of vehicle (n) inside the current lane is updated incorporating this speed into the
movement equation:
x (n, t + T) = x(n, t) + V (n, t + T)T
3.1.1. Car-following in AIMSUN
Car-following in AIMSUN is implemented based on Gipps’ car-following model
discussed in the previous section. The model uses a default reaction time (T) of 0.75 seconds for
trailing vehicles but the user can adjust it between 0.1 and 1.0 second.
3.1.2. Car-following in SimTraffic
SimTraffic uses a formula that has vehicles track a leader vehicle at a fixed headway. The
headway is dependent on speed, driver type, and link headway factor. A “fast following” model
is used when the leading vehicle is traveling faster than 2 ft/s. A “slow following” model is used
( 2 )
( 4 )
( 3 )
16
to track a slow or stopped vehicle or to stop at a fixed point such as the stop-bar or mandatory
lane change starting point (Husch & Albeck, 2004). SimTraffic’s car-following model attempts
to have the trailing car follow the leading car with one second headway between vehicles.
Stopped vehicles will not start until the leading vehicle has moved at least five feet, which is the
minimum distance between vehicles.
3.2. Lane-changing Model
The lane-changing model introduced by Gipps is implemented in most of the
microsimulation tools including AIMSUN and SimTraffic. The driver’s behavior is governed by
two basic considerations: maintaining a desired speed and being in the correct lane for an
intended turning maneuver. The model considers the necessity, desirability, and possibility of a
lane change depending on the location of the vehicle in the road network and updates the
behavior of the driver by asking the three questions (TSS, 2006):
a. Is it necessary to change lanes?
The answer to this question depends on several factors: the turning feasibility in the
current lane, the distance to the next turning, and the traffic conditions in the current lane. The
traffic conditions are measured in terms of speed and queue lengths. When a driver is going
slower than he wishes, he tries to pass the preceding vehicle. On the other hand, when he is
traveling fast enough, he tends to go back into the slower lane. If the answer to the first question
is affirmative, the next two questions must be answered to change lanes successfully.
b. Is it desirable to change lanes?
The model checks whether there will be any improvement in the traffic condition for the
driver as a result of lane changing. This improvement is measured in terms of speed and distance.
17
If the speed in the future lane is fast enough compared to the current lane, or if the queue in the
future is short enough, then it is desirable to change lanes.
c. Is it possible to change lanes?
To answer this, the gap-acceptance model is applied to verify whether there is a large
enough gap to make the lane change with complete safety. For this purpose, the model
calculates both the braking imposed by the future downstream vehicle to the changing vehicle,
and the braking imposed by the changing vehicle to the future upstream vehicle. If both braking
ratios are acceptable then the lane change is possible and the lane-changing model is applied.
In the lane-changing process, the distance to the intended turn defines which zone the
driver is in, with each zone corresponding to a different urgency of lane changing motivations.
The three different zones are (Barcelo, 2001):
Zone 1: This is the farthest distance from the next turning point. The lane changing decisions are
mainly governed by the traffic conditions of the involved lanes. Inside this zone, the turn is far
away so it has no effect on the behavior and the driver concentrates on maintaining a desired
speed.
Zone 2: This is the intermediate zone. The lane changing decision is affected by the desired
turning lane condition, and the driver tries to look for a gap and make a lane change if possible.
Zone 3: This is the shortest distance to the next turning point. The driver focuses on keeping the
correct lane and is forced to reach the desired turning lanes by reducing speed or even coming to
a complete stop, if necessary. Also, vehicles in the adjacent lane can modify their behavior in
order to provide a gap large enough for the vehicle to succeed in changing lanes. The different
zones involved in lane change are shown in Figure 3-1.
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Figure 3 - 1 Lane-changing Zones
3.2.1. Lane-changing in AIMSUN
If the lengths of the links are short, the method discussed in the preceding paragraph for
modeling the lane changing process can cause vehicles to miss their desired turn because they
may be unable to reach the appropriate turning lane on time. When simulating with traffic states,
this could cause turning proportions to not exactly match the input data. AIMSUN includes a
“Look Ahead” model whose main purpose is to make the vehicles reach the turning lane earlier.
The idea is to provide vehicles with the knowledge of the next two additional turning
movements, rather than just one, enabling them to make decisions based not only on the
immediate next turning movement, but on a set of next turning movements. However, in urban
networks where intersections are closely spaced and the next two segments could be very short,
it could still be a problem to provide the vehicles enough distance to accomplish the desired lane
change specially when traffic volumes are high and gaps are few and short (Jones et al., 2004).
3.2.2. Lane-changing in SimTraffic
In SimTraffic, a vehicle always knows the next ten turning movements. Whenever a
vehicle enters a new link, the tenth turn is assigned (Husch et al., 2004). Vehicles can initiate
lane changes well in advance, and they can avoid the undesirable behavior of missing their turn
19
or the need to stop in an adjacent lane while waiting to merge into the proper lane to make the
turn.
3.3. Gap-acceptance Models
The gap-acceptance model is an important element in simulation of traffic flow, mainly
during the lane-changing process and while turning across conflicting traffic streams. To execute
a lane change, the driver assesses the positions and speeds of the conflicting vehicles in the
chosen lane and decides whether the available gap between them is sufficient to execute the
desired lane change. This decision is made by comparing the available gap with the critical gap,
which is the minimum acceptable time gap for a safe execution of the maneuver. The algorithm
used by the gap-acceptance model to check whether a gap is acceptable is presented in
Appendix A.
Several vehicle parameters may influence the behavior of the gap-acceptance model:
acceleration rate, desired speed, speed acceptance, and maximum give-way time. Other
parameters which are related to the section such as visibility distance at the intersection and
turning speed may also have an effect. Among these, acceleration rate, maximum give-way time,
and visibility distance at the intersection are the most important. The acceleration rate gives the
accelerating capability of the vehicle and therefore has a direct influence on the required safety
gap.
3.3.1. Gap-acceptance in AIMSUN
In AIMSUN, the maximum give-way time (5 - 15 seconds) is used to determine when a
driver starts to get impatient if he cannot find the required safe gap. If a driver has been waiting
for more than this time, the safety margin (two simulation steps) is reduced to half, and execution
20
of crossing or lane changing is impossible. Therefore the driver must give way, decelerate and
stop if necessary (TSS, 2006).
3.3.2. Gap-acceptance in SimTraffic
In SimTraffic, the gap time for entering an unsignalized intersection is based on the type
of turn made and the length of the turning path. The gap times in SimTraffic are more consistent
with the amount of time required for a vehicle to enter and clear the intersection, and are between
2.0 and 5.6 seconds. The gap time for a small intersection varies between 4.0 and 5.5 seconds
(Husch et al., 2004).
3.4. Background of Individual Models
In this section, reviews of background information on AIMSUN and SimTraffic are
presented. The section includes brief information about vehicle generation, default parameters,
types of simulation outputs, and features modeled by each model.
3.4.1 AIMSUN
Vehicles are generated and input into the network through individual input sections,
following a random trip generation model based on the mean input flows for those sections.
Therefore, the time interval between two consecutive vehicle arrivals, called headway, is
sampled from a random distribution. The user may select among six different distributions:
exponential, uniform, normal, constant, ASAP, and external. The default distribution is
exponential. Once a vehicle enters the network, it is assigned a set of vehicle characteristics such
as length, maximum speed, and maximum acceleration used by car following, lane changing, and
gap acceptance models to simulate the vehicle’s movement.
21
Users can select from seven varieties of vehicle types, and within each type there are
variations of vehicle attributes based on statistical distributions using random numbers.
AIMSUN sets mean driver performance values, and it varies driver behavior for each vehicle
about the mean within specified minimum and maximum values (Jones et al., 2004). For
example, the desired speed of a vehicle is randomly generated from an assumed normal
distribution of desired speeds with a mean of 65 mph and a standard deviation of 5 mph.
Stochastic simulation models have a logic that generates random numbers; and changing the
random number seed produces a different sequence of random numbers which in turn produces
different values of driver/vehicle characteristics (Dowling, Skabardonis, & Alexiadis, 2004). The
default driver attributes used for a vehicle type “Car” in both models are shown in Table 3-1 at
the end of this section.
AIMSUN is a full function microscopic simulation tool with a broad range of simulation
capabilities:
• Surface street networks, freeways, interchanges and weaving sections.
• Pre-timed signals, actuated signals, and stop controlled intersections.
• Multi-lane roundabouts.
• Bus stops, bus routes, carpool lanes, and light rail.
• Street parking, lane blockage, and short term events.
It also has features not contained in SimTraffic, such as full trip distribution capabilities,
dynamic traffic assignment, and 3-D animations. AIMSUN can simulate vehicle actuated
control systems that give priority to public transport; Advanced Traffic Management Systems
(VMS, traffic calming strategies, ramp metering policies, etc.); applications aimed at estimating
the environmental impact of pollutant emissions; and energy consumption (Barcelo, 2001).
22
AIMSUN outputs several performance measures at different levels of aggregation: whole
system, individual links (called sections in AIMSUN), series of links (called streams in
AIMSUN), and turning movements. Regardless of the level of aggregation, the following outputs
can be generated:
• Mean Flow
• Density
• Mean Speed
• Harmonic Mean Speed
• Total Travel Distance or Vehicle Miles Travel (VMT)
• Total Travel Time or Vehicle Hours Travel (VHT)
• Mean Travel Time
• Mean Delay Time
• Stop Time
• Number of Stops
• Fuel Consumed
• Pollutants Emitted
For individual links or a series of links, the following additional outputs are provided:
• Mean Queue Length (veh/lane)
• Maximum Queue Length (veh/lane)
Detectors can be placed on the network to gather instantaneous MOEs. Time series views are
also available for most MOEs.
23
3.4.2. SimTraffic
The basic structure of SimTraffic is similar to AIMSUN’s structure. Vehicles are added
to entry points based on the volume counts at the downstream intersection at a fixed headway.
Additional vehicles are also added mid-block if mid-block traffic is specified or a volume source
is needed to balance traffic. For any given time period, more or fewer vehicles will appear, and
over many time periods the vehicles will exhibit a Poisson arrival distribution (Husch et al.,
2004).
As in AIMSUN, the movement of each vehicle through the network is determined by car-
following, lane-changing, and gap-acceptance models. Each vehicle is assigned a set of vehicle
characteristics that are used by these models to simulate vehicle movement. SimTraffic has ten
possible selections of vehicle types, and within each vehicle type there will be a variation of ten
discrete driver types. SimTraffic is a deterministic model, which means the model assumes that
there is no variability in the vehicle characteristics. For example, it is assumed that all passenger
cars have a vehicle length of 16 ft. This differs from AIMSUN which uses a statistical
distribution to assign vehicle characteristics for each vehicle type used in the traffic stream.
SimTraffic is designed to model networks of signalized and unsignalized intersections,
including roundabouts. SimTraffic is especially useful for analyzing complex situations that are
not easily modeled macroscopically, including closely spaced intersections with blocking and
lane change problems. The following list summarizes the features modeled by SimTraffic:
• Pre-timed signals, actuated signals, and two-way and four- way stop intersections.
• Single lane roundabouts.
• Roadway bends and curved links.
• Right turn islands, and lane additions and lane drops.
24
• Cars, trucks, buses, pedestrians.
The following are outputs that SimTraffic provides in its reports:
• Stopped Delay
• Stops
• Queue Lengths
• Average Speed
• Total Travel Time
• Total Travel Distance
• Fuel Consumption and Efficiency, and Exhaust Emissions
• Observed Actuated Green Times
SimTraffic (Version 6.0) does not model ramp metering, bus stops, bus routes, bus and
carpool lanes, light rail, on-street parking, and short-term events. SimTraffic does not model
passing in opposing flow lanes; thus it is not designed to evaluate 2-lane rural roads (Dowling,
2007). However, Trafficware launched Version 7.0 of SimTraffic while this thesis research was
ongoing. It is the author’s belief that many of the listed features have been added.
Table 3-1 displays values used by AIMSUN and SimTraffic for 14 individual attributes. Many,
but not all, of them are discussed in the previous pages.
25
Table 3-1 Default Driver/Vehicle Parameters in AIMSUN and SimTraffic
Attributes SimTraffic AIMSUN
Driver types 10 discrete types Probabilistic Vehicle length, ft 16.0 11.0 - 15.0 Vehicle spacing, ft 5.0 1.5 - 5.0 Vehicle width, ft 6.0 6.6 Max speed, mph 75.0 50.0 - 93.0
Max acceleration, ft/sec2 10.0 8.5 - 11.0
Courtesy deceleration, ft/sec2 10.0 - 3.0 11.5 - 15.0 (normal deceleration)
Yellow deceleration, ft/sec2 12.0 - 7.0 16.5 - 23.0 (max deceleration) Speed factor 0.75 - 1.27 0.9 - 1.3 (speed acceptance) Yellow reaction, sec 0.7 - 1.7 N/A Reaction time, sec 0.8 - 0.2 (green) 1.35 (reaction time at stop) Headway, sec 1.0 0.75 (reaction time) Gap times, sec 2 - 5 5 - 15 (giveway time)
Simulation step, sec 0.1 0.75
This chapter has provided information about microsimulation models and their
processing logic, including how simulation vehicles are generated. The three behavioral models:
car-following, lane-changing, and gap-acceptance were discussed with respect to their
application in the AIMSUN. Detailed descriptions of AIMSUN and SimTraffic were presented,
including their modeling features and default parameters were presented. The next chapter
provides the methodology followed to accomplish the project
26
CHAPTER FOUR
METHODOLOGY
After completion of the literature review and background study of the AIMSUN and
SimTraffic models, the next task in this project was to create the study network using the
selected simulation models. Developed in Europe and relatively new to North American users,
AIMSUN is a sophisticated simulation model that is attracting the attention of transportation
modelers. On the other side, SimTraffic is a highly accepted simulation model and has been used
by American professionals for more than fifteen years.
This chapter describes the research methodology followed to compare the two models.
The chapter explains the network coding process and how the performance of each of the
selected models was evaluated using the default parameters. It includes descriptions of the
project area, traffic data source, and definitions of selected performance measures used for
comparing the simulation results. Finally, it discusses the model validation portion of the project.
4.1. Modeled Project Area
The study site was McFarland Boulevard (US 82) in Tuscaloosa, Alabama, between 13th
Street and 31st Street. McFarland Boulevard is a six-lane arterial facility that works under
coordinated traffic signal timings and mostly under saturated conditions during peak hours. The
segment considered for the study is approximately 2.2 miles long, and consists of three major
signalized intersections: two four-leg intersections and one T-intersection. This thesis project
was started in 2007 using data prepared in 2006 by a consulting engineering firm. While this
27
project was ongoing, the T-intersection was converted to a four-leg intersection as a result of a
major development in the area. The project continued using the older network layout, but without
considering the modified intersection due to the difficulty and time related to acquiring updated
data for that intersection. Although the amount of error introduced by the conversion of the
intersection is unknown, based on an analysis discussed in a later section of this report, the
author made the assumption that the amount of error would be minimal.
4.2. Project Data
Microscopic simulation is characterized by the high level of detail at which the system is
modeled. The quality of the model is highly dependent on the availability and accuracy of the
input data. Therefore, the user must be aware that to build a good network, a large amount of
data is required. The data are described in the following sections of this report.
4.2.1. Network Layout
A traffic network model is composed of a set of links connected to each other by nodes
(intersections) which may contain different traffic features. To build the network model, the
following input data is required:
• A map of the area, preferably digitized in .DXF or .bmp format.
• The number of lanes for every link and side lanes.
• Possible turning movements for every intersection, including details about the lanes from
which each turning movement is allowed.
• Speed limits for links and turning speeds for allowed turns at every intersection.
• Detectors including their position and measuring capabilities.
28
4.2.2. Traffic Demand Data
Traffic demand data can be defined in two ways: by the traffic flows at the sections or by
Origin/Destination (O/D) matrix. Depending on the type of model selected, the following input
data must be provided:
• Vehicle types and their attributes.
• Flows at the input links (entrances to the network) for each vehicle type.
• Turning proportions at all sections for each vehicle type.
4.2.3. Traffic Control
All simulation models take into account different types of traffic control: traffic signals,
stop signs, and yield signs. The input data required to define the traffic control follows:
• Signalized intersection: location of signals, the signal groups into which turning
movements are grouped, the sequence of phases, the offset for the junction and the
duration of each phase.
• Unsignalized junctions: definition of priority rules and location of yield and/or stop signs.
The study segment in this project was analyzed based on one hour traffic state volumes
for the AM, Mid day, and PM peak periods. The traffic data was collected in the fall of 2006 by
a local transportation consulting firm. The same firm provided all input data for Synchro network
including turning movement counts for intersections, traffic signal timings, roadway geometry,
and speed limits. For coding the network in AIMSUN, input values were extracted from the
available Synchro file, and aerial photos were used to create the lane geometry. Figure 4-1 and 4-
2 below show the study network in AIMSUN and SimTraffic, respectively. Figures 4-3 through
4-5 show the three peak hour traffic volumes used for coding the project network.
29
Figure 4 - 1 Study Network in AIMSUN Model
Figure 4 - 2 Study Network in SimTraffic Model
30
Figure 4 - 3 AM Peak Hour Traffic Volumes
Figure 4 - 4 Mid Day Peak Hour Traffic Volumes
31
Figure 4 - 5 PM Peak Hour Traffic Volumes
4.3. Comparison of Simulation Models
Both simulation models have default parameters embedded in their processing logic
which controls traffic operations. Most of these consist of vehicle performance and driver
behavior parameters such as vehicle length, maximum acceleration/deceleration, speed factors,
and other factors that are quite difficult to measure in the field (Jones et al., 2004). Since all the
data required to perform a simulation may not be available or easily measured, professionals tend
to depend on the default parameters for their analysis.
After the network coding was completed, the next step was simulating existing conditions
using the model default parameters. This was done to evaluate the performance of each
simulation model before any adjustments or calibration measures were considered.
Since microsimulation models rely on random numbers, a single simulation run cannot be
expected to reflect exact field conditions. Results from individual runs can vary by up to 25%,
and higher standard deviations may be expected for facilities operating at or near capacity (Chu,
32
Liu, Oh, & Recker, 2004). The minimum number of simulation runs was determined based on
the guidelines published in the “Traffic Analysis Toolbox” (Dowling et al., 2004). The guideline
furnished the minimum number of repetitions for various desired confidence intervals and
degrees of confidence using the Student’s t-statistics shown below:
�����% � 2 ����/ �,����
√�
Where:
CI = Confidence interval for the true mean
α (alpha) = Probability of the true mean not lying within the confidence interval
����/ �,��� = Student’s t-statistics for the probability of two-sided error summing to
alpha with N-1 degrees of freedom
N = Number of repetitions
S = Standard deviation
It is up to the analyst to decide the required length of confidence interval and desired
level of confidence based on the purpose of the analysis. For this study, a desired interval of two
standard deviations at 95% confidence level was selected as satisfactory. Based on the
guidelines, a minimum of eight repetitions were required to obtain the desired confidence
interval.
Therefore, each simulation model was run ten times, for the entire network, for each peak
periods, using a random number seed to create a variation among the runs. The average was
taken for comparison.
Selection of performance measures is dependent on the objectives of a particular project
or work to be accomplished by the analyst. In this project, the main objective is to determine
whether one model is better than the other in reproducing actual field condition, so it was felt
( 1 )
33
that the two models should be compared at more than one aggregation level. Therefore, the
network was evaluated according to the output of representative measures of effectiveness
(MOE) at three aggregation levels.
At the total network or system level, the following MOEs were used:
• Vehicle Hours of Travel (VHT) in hours,
• Vehicle Miles of Travel (VMT) in miles,
• Average speed in mph, and
• Network flow rate in vph.
At the arterial level, three MOEs were used:
• Delay in seconds,
• Travel time in seconds, and
• Average speed in mph.
At link or segment level, three MOEs were used:
• Delay in seconds, and
• Volume in vph.
Comparisons of the two models at three aggregation levels were performed for three peak
periods; AM, Mid day, and PM. A total of fifty four paired MOE outputs were compared based
on visual inspection (using graphical plots) and statistical methods (Student’s t-test and f-test).
These methods were selected largely due to their common usage and application on previous
studies (Qureshi et al., 2003; Shaaban & Radwan, 2004: & Xie et al., 2002).
All simulation pairs were compared graphically to see if the simulation results produced
values close to field data. Simulation outputs within the range of 5%-10% of field values were
assumed to be satisfactory in this study. Arterial MOEs were compared using t-statistics for the
34
difference of means and f-statistics for the difference of variances between model outputs and
field data. The t-statistics was used to test whether or not the sample means from the model and
field came from equivalent of non-equivalent populations. The hypothesis test with a 95%
significance level was:
• Ho (null hypothesis): mean of model MOE = mean of field MOE
• H1 (alternate hypothesis): mean of model MOE ≠ mean of field MOE
Failing to reject the null hypothesis would mean that the two samples are not significantly
different. The f-statistics is used to test the null hypothesis that two samples do not have
significant difference or the individual samples are equally spread around the sample mean. The
differences of sample variance between model MOEs and field MOEs were tested as follows:
• Ho (null hypothesis): variance of model MOE = variance of field MOE
• H1 (alternate hypothesis): variance of model MOE ≠ variance of field MOE
It should be noted that different simulation models tend to formulate their computations
of performance measures in different ways. In addition, each model has slightly different
definitions of MOEs and definitions of the vehicles considered in the computation of the MOEs,
making comparison of the two models indirect and difficult. For example, SimTraffic considers
only those vehicles that are entering a link when computing VMT, but it includes those vehicles
denied entry when computing VHT. In the case of AIMSUN, only the vehicles leaving a link or
system are considered. For clarification purposes, the definitions of the performance measures
used in this analysis are given in Table 4-1, taken from user’s manual of each model.
35
Table 4-1 Definition of MOEs
MOE SimTraffic AIMSUN
Vehicle Hours Travel
Vehicle Hours Travel (VHT) is the total time each vehicle was present on the link. The travel time includes time spent by vehicles denied entry (waiting to enter the network), but does not include the time spent by vehicles on the upstream link waiting to enter the subject link.
Total travel time experienced by all the vehicles that have exited the network during the simulation period. Vehicles remaining in the system are excluded from the total system travel time computation.
Vehicles Miles Travel
Vehicle Mile Travel (VMT) is the total distance traveled by all vehicles on the link including the curve distance within intersections. Vehicles that are denied entry are not included.
Total distance travelled by all the vehicles that have crossed the network. Vehicles remaining in the system are excluded from the computation.
Average Speed
VMT divided by total time spent on the network. The time used does not include time spent by denied entry vehicles. Average speed may therefore be higher than VMT divided by VHT for the link.
Average speed for all vehicles that have left the system. This is calculated using the mean journey speed for each vehicle.
Delay per vehicle
Delay per vehicle is the total delay divided by the number of vehicles. Total delay is defined as the travel time minus the time it would take the vehicle if traveling at the maximum permitted speed (the speed limit or the maximum safe turning speed, whichever is lesser). The delay accrued by vehicles denied entry is added to this total.
Average delay time per vehicle. This is the difference between the expected travel time (time it would take to traverse the section under ideal conditions) and the actual travel time. It is calculated as the average of all vehicles.
Arterial Vehicle Count
It uses origin destination data to only count vehicles on the current link that came from the arterial on the next upstream link. This is not the same as taking only those vehicles that travel the entire length of the arterial.
At the stream level, the vehicle data gathered considers only the vehicles that followed the complete stream.
36
4.4. Validation Data
After the network was created and simulation runs were performed, validation of the
simulation outputs from AIMSUN and SimTraffic were performed. Validation is a process in
which simulation outputs are compared with field data to determine how close the model
replicates the field conditions. In this project, field data used for validation of the outputs were
average arterial travel time, average arterial speed, average link speed, and link volume. The
author collected arterial and link travel times using the “floating car run” method described in
the Federal Highway Administration report “Traffic Analysis Toolbox” (Dowling et al., 2004).
In this method, a vehicle was driven the length of the facility ten times during the analysis
period, and the mean travel time was computed. The required number of repetitions to gather
field data was estimated by a criteria discussed in the previous section of this thesis.
Arterial speed was computed using the field collected travel time and using the link
distance from the network geometry. Link volumes were extracted from inputs into the starting
Synchro file provided by a private consulting firm. It should be noted that the original Synchro
file was created at the end of 2006 and field data for validation was collected at the end of year
2008. The author believes that the two year difference between the beginning and completion of
this project could possibly affect the comparison result to some extent. However, the author
gathered one hour of traffic volume on one of the study links and compared it to the older data
set from 2006. The difference found to be less than 1%. Therefore, for the links used in this
study the author made the assumption that the effect of the traffic growth over the two year
period would be minimal on the validation of the models and continued working with the older
data set.
37
In summary, this chapter presented a review of the research methodologies followed to
accomplish the objectives of the project. It included brief descriptions of the project area, traffic
data source, and definitions of selected performance measures. It also included the data
collection procedure followed for collecting validation data of the models. In addition, discussion
of the graphical and statistical methods used for comparisons of the simulation results was also
presented. The next chapter presents the comparison results of AIMSUN and SimTraffic.
38
CHAPTER FIVE
RESULTS OF COMPARISONS
This section presents analysis of the two simulation models used in this project;
AIMSUN and SimTraffic. The models are compared three ways: ease of coding and data entry,
usefulness of simulation output, and accuracy of performance measures.
5.1. Data Entry and Ease of Coding
SimTraffic has a straightforward data entry process, which is probably one reason it is a
popular choice among professionals. For this project, it took approximately 25 hours to create a
new network using Synchro and to perform a SimTraffic simulation. Using AIMSUN, it took
more than 200 hours for the same person to complete coding for the same network, plus an
additional 20 hours to read and understand the user’s manual. For example, coding a traffic
signal was difficult, complicated, and more time consuming than SimTraffic. For coding the
traffic signal, the author had to seek help from an experienced modeler and even so it took a
considerable amount of time.
Synchro serves as a platform to create a traffic signal and also to create the traffic
network for SimTraffic using a link-node system. An intersection is generated automatically at
the point where two links intersect and a simple data input window is displayed for the entry of
number of lanes and lane directions. AIMSUN uses a system of links and “joins” to create the
network elements. User needs to create an intersection (called junctions in AIMSUN) by joining
39
every single lane and specifying lane directions based on permitted tutoring movements. This
process results in coding times that are longer than expected for a simple standard intersection.
Entry of traffic volume for the AIMSUN network was another process found to be
cumbersome for the author. For SimTraffic, Synchro provided a window format for a direct input
of the volumes at the intersections. For AIMSUN, volumes need to be converted into percentages
based on their arrival from a previous intersection. The user must convert the data for each
turning movement and for each vehicle type separately before entering the traffic volume.
In addition, the geometric layout features in AIMSUN are less realistic at matching the
details of a roadway layout, and lack the flexibility to simulate some types of lane alignments.
For instance, use of two or more exclusive left turn lanes or a channelized right turn lane is not
supported in AIMSUN. For this project, these geometric elements were approximated by using
consecutive piecewise linear sections. This approximation could possibly impact the quality of
the simulation result but it is beyond the capacity of the author to quantify the extent of the
impact. On the other hand, AIMSUN has some positive aspects. For instance, the model
performs error checking while the data is being input and saves the modeler from the time
consuming task of identifying and debugging data errors at a later stage. Another advantage is
that the user can define different traffic streams, and can place a detector anywhere in the
network to collect statistical data. These features are not available in SimTraffic.
In summary, creation of a simple three signal network and conducting a simulation on
that network took approximately eight times longer in AIMSUN than in SimTraffic. The network
coding and simulation using AIMSUN was felt to be excessively detailed for a small network
with a standard type of intersection. In the author’s opinion, SimTraffic is significantly easier to
use. In coding the network each program had features that were desirable, and a user might
40
select which model to use based on which features were most important for modeling a particular
arterial.
5.2. Simulation Output
Both simulation models provide detailed output, and both provide animated graphics and
tabular format. Animation output is powerful in that it enables the user to quickly assess the
overall performance of the network qualitatively. It also provides detailed information at specific
locations. AIMSUN has some desirable features that are not available in SimTraffic. It allows a
single vehicle to be traced through the simulation, and it provides a time series view of the
vehicle trajectory. Performance measures such as flow, speed, density, and number of stops can
be displayed in time series viewer windows during the simulation for sections, vehicles, or
detectors.
AIMSUN is more flexible at storing simulation output. The user can choose to store the
output in either an Excel or Access database in an ODBC format. In addition, AIMSUN reports
more MOEs than SimTraffic. A report for an arterial by SimTraffic produces four MOEs, but
AIMSUN reports more than eight MOEs. Screen shots of arterial reports from both models are
given in Figures 5-1 and 5-2.
41
Figure 5 - 1 Screen Shot of Arterial Report from the AIMSUN Model
Figure 5 - 2 Screen Shot of Arterial Report from the SimTraffic Model
42
SimTraffic saves the report as a text file and the user must copy it into Excel for further
analysis. AIMSUN reports the mean and standard deviation for each output, whereas SimTraffic
reports only mean values. In AIMSUN, simulation results can be reported periodically in user-
defined time intervals or over the entire simulation time period. This is not possible in
SimTraffic.
In summary, AIMSUN appears to perform better in the output category. It provides better
graphical output, the tabular output is more versatile, and the user has better control of the type
and frequency of output.
5.3. Comparison of Simulation Results
In this section, two comparisons were performed. First, MOEs were compared at the
network level (VMT, VHT, average speed, and flow rate) and arterial and segment levels
(average delay) to evaluate whether the two models deliver similar analytical outputs. Second,
MOEs from simulation were compared to MOEs from field measurements to gauge if the models
replicated arterial field conditions. The MOEs used for the second comparison were travel time
and average speed at the arterial level, and volume at the segment level.
5.3.1. Model Comparison
5.3.1.1. Comparison of Network MOEs
Network MOEs are important for measuring a systemwide performance. For each peak
hour, ten simulation runs of the network were performed using both models. Network MOEs
from AIMSUN and SimTraffic for the AM peak period are presented in Table 5-1 and Table 5-2.
The values are given for each of the ten runs, and the summary statistics of all ten were used for
purposes of comparison.
43
Table 5-1 AIMSUN Network MOEs - AM Peak Hour
Simulation Runs
VMT, miles
VHT, hours
Average Speed, mph
Flow Rate, vph
1 7726.3 258.5 31.9 5789 2 7666.9 256.4 31.9 5749 3 7573.2 251.4 32.1 5645 4 7771.3 259.6 31.9 5809 5 7653.4 255.3 31.9 5681 6 7563.9 251.6 32.0 5645 7 7726.6 256.6 32.1 5768 8 7812.7 261.1 31.9 5804 9 7642.7 255.3 31.8 5688 10 7543.0 252.3 31.9 5651 Average 7668.0 255.8 31.9 5723 St. Dev. 91.1 3.3 0.1 67.8 % CV 1.2 1.3 0.3 1.2
Table 5-2 SimTraffic Network MOEs - AM Peak Hour
Simulation Runs
VMT, miles
VHT, hours
Average Speed, mph
Flow Rate, vph
1 7166.0 256.2 28.0 5727 2 7382.2 264.8 28.0 5879 3 7351.2 263.9 28.0 5887 4 7173.5 259.8 28.0 5722 5 7417.1 266.8 28.0 5946 6 7361.1 262.6 28.0 5890 7 7321.2 262.0 28.0 5837 8 7242.1 258.3 28.0 5806 9 7320.6 264.6 28.0 5910 10 7354.7 264.9 28.0 5866 Average 7309.0 262.4 28.0 5847 St. Dev. 86.4 3.3 0.0 74.8 % CV 1.2 1.3 0.0 1.3
44
Both models gave consistent values between runs as shown by the small values of the
coefficient of variation (CV, standard deviation divided by mean). Summary of the mean values
from both models are shown in Table 5-3.
Table 5-3 Comparison of AIMSUN and SimTraffic MOE - AM Peak Hour
MOE VMT, miles
VHT, hours
Average Speed, mph
Flow Rate, mph
AIMSUN 7668 255.8 31.9 5722.9 SimTraffic 7309 262.4 28 5847 Abs. Diff. 359 6.6 3.9 124.1 % Diff. 4.8% 2.5% 13.0% 2.1%
Except for average speed, both models delivered similar outputs with less than 5%
absolute difference. Higher average speed and less travel time in AIMSUN suggest that
AIMSUN predicts a higher level of service for the network than SimTraffic. It should be noted
that the models compute average speed differently as discussed in Chapter Four. SimTraffic
computes average speed by dividing the accumulated total traveled distance by the total travel
time spent on the network, whereas AIMSUN calculates mean journey speed for each vehicle
that has left the system. Another reason for disparity could be the different definitions of a
vehicle count for the computation of MOEs. AIMSUN counts vehicles that exited the network
for all computations, but SimTraffic uses vehicles that entered the network. Similar disparities
were observed for the Mid day and PM peak hour simulation results. Tabular and graphical
simulation results for the Mid day and PM peak hours are presented in Appendices B and C.
5.3.1.2. Comparison of Arterial MOE – Average Delay
Arterial delay is the average delay experienced per vehicle when traveling on the arterial
links (McFarland Boulevard). Figures 5-3 and 5-4 show the simulated arterial delay values for
the north bound traffic for AM and PM peak periods, respectively.
45
Figure 5 - 3 Simulated NB Arterial Delay – AM Peak Hour
Figure 5 - 4 Simulated NB Arterial Delay – PM Peak Hour
First, both models gave reasonably consistent results from simulation run to simulation
run for the ten repetitions for the AM peak period. There appeared to be more variation for the
PM results when the traffic volume was higher. Second, Figure 5-3 shows that the models
produced diferent results, with AIMSUN’s average delay almost twice as large as SimTraffic’s
delay for the AM peak period. For the PM peak traffic flow (Figure 5-4), the two models
produced average delay values that were closer togther than the values shown in Figures 5-3.
2030405060708090
100
0 1 2 3 4 5 6 7 8 9 10
Ave
rage
Del
ay,
sec
Simulation Runs
AIMSUNSimTrafficField
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0 1 2 3 4 5 6 7 8 9 10
Ave
rage
Del
ay,
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Simulation Runs
AIMSUNSimTrafficField
46
As traffic volume increased from the AM to the PM peak hours, SimTraffic delay values
increased almost proportionatley. This is expected in “real life” traffic conditions. However, the
opposite was true for AIMSUN, where delay decreased from AM to PM.
It should be noted that AIMSUN computes arterial delay only for vehicles that have followed the
complete arterial, while SimTaffic computes arterial delay only for vehicles on the current link
that came from the arterial on the next upstream link.
It is also possible that the different methods used to compute delay contributed to the
differences in simulation results of the two models. But without being able to manually follow
the flow of each vehicles on each arterial for each model, it was imposible to confirm the reason
for the large difference in arterial delay values of AIMSUN and SimTraffic.
5.3.1.3. Comparison of Link MOE – Average Delay
Figure 5-5 shows the average delay per vehicle experienced at four different links along
the arterial for the PM peak poriod. Each point is the average delay value of ten simulation runs.
The results indicate that the models provide different estimates of average delay. As traffic
volume on the links increased from AM to Mid day and to PM peak periods, average delay
values from SimTraffic also increased. However, AIMSUN produced similar values of delay for
all peak periods indicating that it is insenstive to traffic volume. This does not seem realistic for
real-life traffic conditions.
47
Figure 5 - 5 Simulated Average Delay - PM Peak Hour
In summary, the patterns observed for link delay were similar to the previously discussed
patterns found for arterial delay. Average delays from SimTraffic increased as traffic volume
increased whereas average delays from AIMSUN were almost identical for the three peak
periods regardless of the traffic increase. Therefore for the comparison of delay, SimTraffic
seems to be more realistic than AIMSUN.
5.3.2. Comparison of Model Output and Field Data
Using statistical methods and graphical plots, both simulation models were evaluated for
their capability to replicate the field data collected during the project. Several comparisons were
performed to determine the differences between model outputs and field data.
5.3.2.1. Comparison of Arterial MOE – Travel Time
Plots were prepared to compare travel time and speed profiles. They were helpful in
evaluating the ability of each model to replicate field data. Figures 5-6 and 5-7 show plots of
arterial travel times for the AM and PM peak traffic, respectively.
010203040506070
0 1 2 3 4
Ave
rage
Del
ay,
sec
Link No
AIMSUN
SimTraffic
48
Looking at the AM peak hour (Figure 5-6), SimTraffic produced travel times closer to
field values than AIMSUN. For AM traffic, AIMSUN overestimated travel time by 15% to 20%
but SimTraffic undesetimated by less than 5%.
The same pattern observed for delay in the previous sections of this thesis was exihibited
for PM peak travel time. As traffic volume increased for the PM periods, travel time from
SimTraffic also increased. However, AIMSUN produced almost identical values for all three
peak periods regardless of the difference in traffic volumes as shown in Figure 5-7. Both models
overestimated roadway capacites for the PM high traffic condition, but SimTraffic values were
again closer to field conditons than AIMSUN.
Figure 5 - 6 Simulated NB Arterial Travel Time vs. Field Travel Time - AM Peak
100
120
140
160
180
200
0 1 2 3 4 5 6 7 8 9 10
Tra
vel T
ime,
sec
Simulation Runs
AIMSUN
SimTraffic
Field
49
Figure 5 - 7 Simulated NB Arterial Travel Time vs. Field Travel Time - PM Peak
In addition to the graphical anaylsis discussed above, statistical tests were used to
compare mean values of simulated travel times with mean values of travel times from field
observation. The result of the t-test showed that there is a significant difference between the two
means whereas the f-test showed there is no significant difference among the individual runs of
both samples. Both t-test and f-test results for arterial travel time and average speed are presented
in Appendix D.
5.3.2.2. Comparison of Arterial MOE – Average Speed
Simulated arterial speed is compared to field speed using a statistical test and graphical
plots. Figures 5-8 and 5-9 show individual runs from each model and from field data for north
bound traffic for AM and PM periods. Since speed is computed from travel time, the patterns
observed for arterial speed were consistent with the patterns for arterial travel time for all peak
periods. Statistical tests suggested there is a significant difference between model outputs and
field data.
Based on the graphical plots, SimTraffic seemed to perform better than AIMSUN for the
arterial segment considered in the study. As traffic increased for the PM peak period, SimTraffic
120
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180
200
220
1 2 3 4 5 6 7 8 9 10
Tra
vel T
ime,
sec
Simulation Runs
AIMSUNSimTrafficField
50
speed decreased accordingly, whereas speeds from AIMSUN increased. For the lower volume
morning peak, both models estimated average speeds closer to observed field speeds. AIMSUN
slightly underestimated average speed, but SimTraffic overestimated them as shown in Figure 5-
8. However, looking at Figure 5-9 for the PM traffic, both models overestimated average speed,
but SimTraffic performed closer to field speeds than AIMSUN.
Figure 5 - 8 Simulated NB Arterial Speed vs. Field Speed - AM Peak
Figure 5 - 9 Simulated NB Arterial Speed vs. Field Speed - PM Peak
20
25
30
35
0 1 2 3 4 5 6 7 8 9 10
Ave
rage
Spe
ed,
mph
Simulation Runs
AIMSUN
SimTraffic
Field
15
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30
35
0 1 2 3 4 5 6 7 8 9 10
Ave
rage
Spe
ed,
mph
Simulation Runs
AIMSUN
SimTraffic
Field
51
In summary, arterial MOEs from AIMSUN and SimTraffic were compared by using
graphical plots and test statistics. For the three peak periods and for both north and south bound
traffic directions, a total of 12 sample pairs (model sample vs. field sample) were tested. Based
on the results of the t-statistics performed for the arterial travel times and average speeds,
neither model was able to reproduce the selected field MOEs. Based on results of the f-statistics
for both models, two-third of the paired sample variances shown no significant difference among
individual observations.
According to the graphical analysis, SimTraffic MOEs were generally found to be closer
to arterial observed values than AIMSUN. When traffic is congested, AIMSUN tends to
overestimate arterial capacities as compared to SimTaffic. AIMSUN indicated higher average
speeds and shorter travel times than those collected in the field, implying that AIMSUN is
overestimating the available capacity of the arterial to a higher degree than SimTraffic.
The author found that parameters used in the car-following model of AIMSUN have a
tendency to overestimate capacity, as compared to SimTraffic. For example, the default reaction
time, headway, vehicle length, and vehicle space used in the car-following models of AIMSUN
are smaller than the ones used in the car-following model of SimTraffic. The common default
parametes used in AIMSUN and SimTraffic were discussed in previous chapters.
5.3.2.3. Comparison of Link MOE – Link Volume
Both models reproduced the field volumes reasonably within the range of 5-10% of the
field volume, with the exception of one link with low traffic volume, for which both models
overestimated volume by more than 10%. The SimTraffic estimates appeared to be more
accurate than the AIMSUN estimate, as shown in Figure 5-10.
52
Figure 5 - 10 Simulated Volume vs. Field Volume - AM Peak
5.4 Summary of Results
The previous sections presented an analysis of simulation comparisons made between
AIMSUN and SimTraffic, using default parameters at different levels of aggregation. A total of
54 simulation comparisons were performed graphically and statistically. For arterial MOEs, t-
statistics were applied to test if there is significant difference between model mean values and
field mean values.
Except for few comparisons from the Mid day peak period, the test showed that the two
models delivered different estimations for the selected MOEs. Based on the graphical analysis
performed, SimTraffic appears to more closely simulate field observations for McFarland
Boulevard than AIMSUN for most comparisons. For example, compared to field data,
estimations of arterial MOEs from AIMSUN had almost twice as much variability as
SimTraffic’s estimations, as shown in Table 5-4.
800
1000
1200
1400
1600
1800
0 1 2 3 4
Flo
w, v
ph
Link No
AIMSUN
SimTraffic
Field
53
Table 5-4 Model Estimation of Arterial MOEs MOE Model AM peak Mid day peak PM peak Arterial Travel Time, sec
AIMSUN over 15-20% under 1-5 % under 15-30% SimTraffic under 5-10% over 1-5% under 10-15%
Arterial Speed, mph
AIMSUN under 5-10% over 15-20% over 40-55% SimTraffic over 5-15% under 1-5% over 10-25%
For the arterial segment used in this study, AIMSUN appeared to be insensitive to traffic
increases for the three peak periods. As traffic conditions changed from near free flow in the AM
peak to congested in the PM peak period, AIMSUN delivered almost identical values of MOEs.
SimTraffic MOEs changed in relation to traffic volume. For example, delay and travel time
from SimTraffic were higher during the PM peak period than the AM peak period, while they
were almost identical for all time periods in AIMSUN. Therefore, based on initial comparisons
of the models using default parameters, SimTraffic seemed to yield results more consistent with
field observations than AIMSUN.
In summary, differences between default parameters of each model, differences in
computation of MOEs, differences in definitions of variables, and differences in simulation
MOEs for the two models indicated that the two models may have more differences than
commonalities. In addition, the results suggest that neither AIMSUN nor SimTraffic could
replicate field conditions reasonably, and the results should not be trusted without further model
calibration. Therefore, to better replicate field conditions of the study network model calibration
is performed in the next chapter.
54
CHAPTER SIX
COMPARISON OF MODELS AFTER CALIBRATION
In the previous chapter, analyses of outputs from AIMSUN and SimTraffic showed that
the models delivered different MOEs in simulating McFarland Boulevard. Furthermore, without
calibration neither model was able to replicate field conditions accurately. This suggests the
importance of model calibration to provide a better representation of the traffic conditions. In the
process of calibration, default parameters are adjusted to reflect the local driving conditions of
the existing traffic conditions (Hourdakis, Michalopoulos, & Kottommannil, 2003). Calibration
is a time consuming process because modification of the default parameters is done iteratively
following a trail-and-error method to obtain a close match between model estimates and field
measurements.
Every model comes with a set of user-adjustable parameters for calibrating the model to
local traffic conditions. However, the difficulty, time, and cost associated with model calibration
often causes users to depend on model default parameters. For instance, most vehicle and driver
parameters such as vehicle length, vehicle spacing, and reaction time are difficult and expensive
to measure in the field. Another difficulty associated with calibration is that it is not clear which
parameter to modify to achieve the desired change. Adjustment of one link specific parameter
could have undesirable effects on simulation results of an adjacent link or somewhere else in the
network, and therefore the modeler can end up in a never-ending process. (Dowling et al., 2004)
55
In this project, the time and difficulty related to acquiring required field data for full
calibration was beyond the scope of this thesis. Therefore, the author performed only a minor
calibration of the global parameters as suggested in the user manuals of the models, as described
with the following paragraphs. Although the adjustments made to the parameters were small, the
iterative process performed by the author to achieve the final values was very time consuming.
6.1 Model Calibration
Compared to SimTraffic, AIMSUN had a higher tendency to overestimate road capacity
for congested traffic conditions. As discussed in previous sections, driving behavioral parameters
such as vehicle spacing and reaction time are smaller in AIMSUN than SimTraffic, therefore
resulting in higher saturation flow rates than other models.
For calibration of the AIMSUN model, reaction time and reaction time at stop were
adjusted to reproduce simulation MOEs similar to field MOEs. Reaction time is the time a driver
takes to react to speed changes in the preceding vehicle. It is used in the car-following model
(range from 0.1 to 1.0 sec). Reaction time at stop is the time it takes for a stopped vehicle to react
to the acceleration of the vehicle in front or to a traffic signal changing to green. Reaction time at
stop has a strong influence in queue discharge behavior (TSS, 2006). Table 6-1 shows the default
parameter in one column and then shows the changed values that were used in the three time
periods simulated.
Table 6-1 Suggested Calibration Parameters for AIMSUN
Parameter Default all peaks
Calibrated AM
Calibrated Mid day
Calibrated PM
Reaction time, sec 0.75 0.50 0.75 0.80
Reaction time at stop, sec 1.35 1.00 1.40 1.60
56
As discussed in the previous sections, SimTraffic yielded Mid day MOEs close to
observed field conditions. For AM and PM peak periods, SimTraffic overestimated the capacity
of the system. For example, arterial travel time was underestimated by 5-15% and average speed
was overestimated by 10-25 %. Therefore, an effort was made to calibrate the AM and PM
traffic conditions to lower the simulated capacity of the system. Based on the SimTraffic user
manual, the primary parameter suggested for system calibration is headway factor. By default,
SimTraffic is calibrated to a headway factor of 1.0 to give flow rates of about 1850 vehicles per
hour per lane for speeds above 30 mph (Shaaban et al., 2004). Adjusting the headway parameter
above 1.0 would result in lower saturation flow. Table 6-2 shows the adjusted parameters. .
Table 6-2 Suggested Calibration Parameters for SimTraffic
Parameter Default all peaks
Calibrated AM
Calibrated Mid day
Calibrated PM
Headway Factor 1.00 1.05 1.00 1.10
6.2 Calibration Outputs
In this section, comparisons of simulation results after model calibration are discussed for
arterial MOEs. Small adjustments of the suggested parameters were found to provide output
closer to observed field conditions than the non-calibrated results.
6.2.1 Arterial Travel Time
Looking at Figures 6-1 and 6-2, after calibration of the models, AIMSUN and SimTraffic
estimated arterial travel times closer to the observed field conditions. Both models estimated the
AM and Mid day peak traffic conditions better than the PM congested traffic conditions. Even
though both models underestimated the PM arterial travel time, SimTraffic underestimated by
less than 5% whereas AIMSUN underestimated by 10 to 15%. For the three peak periods, a total
57
of six arterial travel time comparisons were made between model estimation and field data. Out
of the six comparisons, three of the SimTraffic estimations and one of the AIMSUN estimations
showed no significant difference from field data. Based on the analyses, SimTraffic has
estimated travel time slightly better than AIMSUN. In addition, both simulation models have
shown a significant improvement after model calibration.
Figure 6 - 1 Simulated vs. Observed NB Travel Time - AM Peak
Figure 6 - 2 Simulated vs. Observed NB Travel Time - PM Peak
100
120
140
160
180
0 1 2 3 4 5 6 7 8 9 10
Tra
vel T
ime,
sec
Simulation Runs
AIMSUN
SimTraffic
Field
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220
1 2 3 4 5 6 7 8 9 10
Tra
vel T
ime,
sec
Simulation Runs
AIMSUN
SimTraffic
Field
58
6.2.2 Arterial Speed
After calibration of the simulation models, the AM peak period was found to be a better
fit to observed arterial speed than the PM peak period (Figures 6-3 and 6-4). SimTraffic
produced estimates of arterial speeds closer to field speeds than AIMSUN for all peak periods.
Figure 6 - 3 Simulated vs. Observed NB Average Speed - AM Peak
Figure 6 - 4 Simulated vs. Observed NB Average Speed - PM Peak
20
25
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35
0 1 2 3 4 5 6 7 8 9 10
Ave
rage
Spe
ed,
mph
Simulation Runs
AIMSUN
SimTraffic
Field
15
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1 2 3 4 5 6 7 8 9 10
Ave
rage
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ed,
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Simulation Runs
AIMSUN
SimTraffic
Field
59
In summary, the graphical and statistical analyses of the calibrated model results
indicated that even small adjustments of few default parameters can yield a better replication of
field conditions. For the AM and Mid day peak hours where traffic conditions range from light to
moderate, both models estimated arterial MOEs much closer to field conditions than they did
before model calibration. Even after calibration, AIMSUN was more likely to overestimate
roadway capacities for congested traffic conditions, thereby implying a better operating
condition than observed in the field. Overall, SimTraffic simulated almost all MOEs closer to
observed values than those generated by AIMSUN.
60
CHAPTER SEVEN
CONCLUSIONS AND RECOMMENDATIONS
This chapter focuses on conclusions drawn about AIMSUN and SimTraffic simulations
of a congested arterial segment. It summarizes the results of this study and also includes some
recommendations for future studies. These conclusions are not universal. They are based upon a
relatively small signalized arterial network on a six lane urban arterial in Tuscaloosa, Alabama.
They are limited by the quality of data provided in 2006 by engineering firm and validation data
gathered in 2008 by the author for an analysis of simulation results. The time and funding
associated with this thesis did not allow collection of a more complete dataset or a more
extensive analysis.
7.1 Conclusions
AIMSUN and SimTraffic were compared three ways: ease of coding and data entry,
usefulness of simulation output, and accuracy of performance measures. SimTraffic had an easy-
to-use graphical interface and a straightforward data entry process as compared to AIMSUN.
Creation of the study network and conducting a simulation on the network took approximately
eight times longer in AIMSUN than in SimTraffic. For example, coding a traffic signal was
difficult and more time consuming than coding the same network using SimTraffic. In addition,
creation of some geometric network features such as application of two or more exclusive left
lanes or channelized right turns were not supported in AIMSUN. Although modeling of a simple
61
network using SimTraffic was found to be much easier for this project, AIMSUN has a number
of powerful tools that are not explored in detail in this thesis. For instance, transit simulation and
traffic incident simulation are features that are more desirable for coding of complex urban
networks and AIMSUN has full capacity to code these features than SimTraffic.
The models were also compared with respect to usefulness of simulation output. Both
simulation models provide detailed output, and both provide animated graphics and tabular
format. AIMSUN has a more flexible mechanism for storing simulation output and has desirable
features that are not available in SimTraffic. The user can choose to store the output in either
Excel or Access format. In comparison, SimTraffic saves the report as a text file, so the user
must copy it to excel for further analysis. In addition, AIMSUN allows a single vehicle to be
traced through the simulation and provides a time series view of the vehicle trajectory.
Overall, AIMSUN appears to be better in the output category. It provides better graphical
output, the tabular output is more versatile, and the user has better control of the type and
frequency of output.
Another category for comparison of AIMSUN and SimTraffic was accuracy of simulated
MOEs. The modeling undertaken for the McFarland Boulevard study area found that traffic
conditions can be reproduced by the models with more accuracy if the models are calibrated. The
study has identified differences between the model outputs and the actual field data collected.
Several comparisons of MOEs at different aggregation levels were performed in order to
discern the differences between the models output and the field data. For most comparisons,
graphical plots were used to evaluate the ability of each of the two models to replicate the field
data and also to compare outputs between the two models. For arterial MOEs t-test and f-test
were employed for further analysis.
62
For AM and Mid day peak traffic periods, SimTraffic was found to be a better simulator
than AIMSUN, even before model calibration. For the PM congested traffic conditions,
AIMSUN had a tendency to overestimate arterial capacity more than SimTraffic. After
calibration of the models was performed, both models produced an improved output for all peak
periods, and SimTraffic simulated the field conditions more closely than AIMSUN.
Comparing the level of difficulty for creating a network, usefulness of simulation outputs,
and accuracy of performance measures experienced in this study, the author suggest that
SimTraffic is preferred for replicating congested traffic conditions on McFarland Boulevard.
7.2 Recommendations
The author recommends that updated modeling data such as traffic volumes and traffic
signal timings be used for further studies on the network. Field collected validation and
calibration data should be set aside before beginning of simulation so that an analysis time could
be saved.
This study used only a single day of field observed data for validating the model outputs.
It is recommended to confirm the results of the findings by testing the model using multiple days
of field data and using more rigorous statistical analysis. It is also recommended to use other
MOEs such as delay and queue length to see if the simulations produce similar results.
A variety of global and local default parameters could be modified to replicate field
conditions to a higher degree. It is recommended that further research be carried out to improve
simulation, in particular in calibration of the models.
In this study, signalized intersections parallel to the study arterial were not included in the
simulation. The author suggests that parallel streets and intersections near the study network be
added to reflect the effect of surrounding traffic.
63
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65
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66
Appendix – A
Resources used in Simulation Modeling
67
Table 1 Required Number of Repetitions
Desired Range
Desired Confidence
Minimum Repetitions
0.5 99% 130 0.5 95% 83 0.5 90% 64 1.0 99% 36 1.0 95% 23 1.0 90% 18 1.5 99% 18 1.5 95% 12 1.5 90% 9 2.0 99% 12 2.0 95% 8 2.0 90% 6
CI = Confidence Interval S = Standard Deviation
68
Figure 1 Gap-acceptance Algorithm (TSS, 2006)
69
Appendix – B
Simulation Results Using Default Parameters - Graphs
70
Figure 1 Simulated Network Travel Distance - AM Peak Hour
Figure 2 Simulated Network Travel Time - AM Peak Hour
6400
6600
6800
7000
7200
7400
7600
7800
8000
1 2 3 4 5 6 7 8 9 10
Tra
ve
l D
ista
nce
, m
ile
Simulation Runs
AIMSUN
SimTraffic
220.0
230.0
240.0
250.0
260.0
270.0
280.0
1 2 3 4 5 6 7 8 9 10
Tra
ve
l T
ime
(VH
T),
se
c
Simulation Runs
AIMSUN
SimTraffic
71
Figure 3 Simulated Network Average Speed - AM Peak Hour
Figure 4 Simulated Network Flow Rate - AM Peak Hour
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
5000
5200
5400
5600
5800
6000
1 2 3 4 5 6 7 8 9 10
Ne
two
rk F
low
, v
ph
Simulation Runs
AIMSUN
SimTraffic
72
Figure 5 Simulated NB Arterial delay-AM Peak Hour
Figure 6 Simulated SB Arterial delay-AM Peak Hour
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8 9 10
Av
era
ge
De
lay
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8 9 10
Av
era
ge
De
lay
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
73
Figure 7 Simulated vs. Observed NB Arterial Travel Time -AM Peak Hour
Figure 8 Simulated vs. Observed SB Arterial Travel Time-AM Peak Hour
60
80
100
120
140
160
180
1 2 3 4 5 6 7 8 9 10
Tra
ve
l ti
me
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
60
80
100
120
140
160
180
1 2 3 4 5 6 7 8 9 10
Tra
ve
l ti
me
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
74
Figure 9 Simulated vs. Observed NB Arterial Speed -AM Peak Hour
Figure 10 Simulated vs. Observed SB Arterial Speed -AM Peak Hour
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
Field
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
Field
75
Figure 11 Simulated Link Delay - AM Peak Hour
Figure 12 Simulated vs. Observed Link Volume - AM Peak Hour
0
10
20
30
40
50
0 1 2 3 4
Av
era
ge
Lin
k D
ela
y,
sec
Link No.
AIMSUN
SimTraffic
800
1000
1200
1400
1600
1800
0 1 2 3 4
Lin
k V
olu
me
, v
ph
Link No.
AIMSUN
SimTraffic
Field
76
Figure 13 Simulated Network Travel Distance – Mid day Peak Hour
Figure 14 Simulated Network Travel Time – Mid day Peak Hour
8000.0
8400.0
8800.0
9200.0
9600.0
10000.0
10400.0
10800.0
1 2 3 4 5 6 7 8 9 10
Tra
ve
l D
ista
nce
(V
MT
), m
ile
Simulation Runs
AIMSUN
SimTraffic
200.0
250.0
300.0
350.0
400.0
450.0
1 2 3 4 5 6 7 8 9 10
Tra
ve
l T
ime
(VH
T),
se
c
Simulation Runs
AIMSUN
SimTraffic
77
Figure 15 Simulated Network Speed – Mid day Peak Hour
Figure 16 Simulated Network Flow Rate – Mid day Peak Hour
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
7000
7200
7400
7600
7800
8000
1 2 3 4 5 6 7 8 9 10
Ne
two
rk F
low
, v
ph
Simulation Runs
AIMSUN
SimTraffic
78
Figure 17 Simulated NB Arterial delay-Mid day Peak Hour
Figure 18 Simulated SB Arterial delay-Mid day Peak Hour
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10
Av
era
ge
De
lay
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10
Av
era
ge
De
lay
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
79
Figure 19 Simulated vs. Observed NB Arterial Travel Time -Mid day Peak Hour
Figure 20 Simulated vs. Observed SB Arterial Travel Time-Mid day Peak Hour
120
130
140
150
160
170
180
1 2 3 4 5 6 7 8 9 10
Tra
ve
l T
ime
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
120
130
140
150
160
170
180
1 2 3 4 5 6 7 8 9 10
Tra
ve
l T
ime
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
80
Figure 21 Simulated vs. Observed NB Arterial Speed -Mid day Peak Hour
Figure 22 Simulated vs. Observed SB Arterial Speed-Mid day Peak Hour
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
Field
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
Field
81
Figure 23 Simulated Link Delay – Mid day Peak Hour
Figure 24 Simulated vs. Observed Link Volume – Mid day Peak Hour
0
10
20
30
40
50
60
0 1 2 3 4
Av
era
ge
Lin
k D
ela
y,
sec
Link No.
AIMSUN
SimTraffic
1400
1600
1800
2000
2200
0 1 2 3 4
Lin
k V
olu
me
, v
ph
Link No.
AIMSUN
SimTraffic
Field
82
Figure 25 Simulated Network Travel distance - PM Peak Hour
Figure 26 Simulated Network Travel Time - PM Peak Hour
9000.0
9400.0
9800.0
10200.0
10600.0
11000.0
11400.0
11800.0
1 2 3 4 5 6 7 8 9 10
Tra
ve
l D
ista
nce
(V
MT
), m
ile
Simulation Runs
AIMSUN
SimTraffic
200.0
250.0
300.0
350.0
400.0
450.0
500.0
550.0
600.0
1 2 3 4 5 6 7 8 9 10
Tra
ve
l T
ime
(VH
T),
se
c
Simulation Runs
AIMSUN
SimTraffic
83
Figure 27 Simulated Network Average Speed - PM Peak Hour
Figure 28 Simulated Network Flow Rate - PM Peak Hour
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
7600
7800
8000
8200
8400
8600
8800
9000
1 2 3 4 5 6 7 8 9 10
Ne
two
rk F
low
, v
ph
Simulation Runs
AIMSUN
SimTraffic
84
Figure 29 Simulated NB Arterial Delay -PM Peak Hour
Figure 30 Simulated SB Arterial Delay -PM Peak Hour
20
40
60
80
100
120
140
0 1 2 3 4 5 6 7 8 9 10
Av
era
ge
De
lay
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
20
40
60
80
100
120
140
0 1 2 3 4 5 6 7 8 9 10
Av
era
ge
De
lay
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
85
Figure 31 Simulated vs. Observed NB Arterial Travel Time -PM Peak Hour
Figure 32 Simulated vs. Observed SB Arterial Travel Time -PM Peak Hour
100
120
140
160
180
200
220
240
1 2 3 4 5 6 7 8 9 10
Tra
ve
l ti
me
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
120
140
160
180
200
220
240
260
1 2 3 4 5 6 7 8 9 10
Tra
ve
l ti
me
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
86
Figure 33 Simulated vs. Observed NB Arterial Speed -PM Peak Hour
Figure 34 Simulated vs. Observed SB Arterial Speed-PM Peak Hour
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
Field
5.0
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
Field
87
Figure 35 Simulated Link Delay -PM Peak Hour
Figure 36 Simulated vs. Observed Link Volume -PM Peak Hour
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
0 1 2 3 4
Av
era
ge
De
lay
, se
c
Link No.
AIMSUN
SimTraffic
1400.0
1600.0
1800.0
2000.0
2200.0
2400.0
0 1 2 3 4
Vo
lum
e,
vp
h
Link No.
AIMSUN
SimTraffic
Field
88
Appendix – C
Simulation Results Using Default Parameters – Tables
89
Table 1 Simulated Network MOEs - AM Peak Hour Simulation
Runs VMT, miles VHT, hours Average Speed, mph Flow Rate, vph
AIMSUN SimTraffic AIMSUN SimTraffic AIMSUN SimTraffic AIMSUN SimTraffic
1 7726.3 7166.0 258.5 256.2 31.9 28.0 5789 5727 2 7666.9 7382.2 256.4 264.8 31.9 28.0 5749 5879 3 7573.2 7351.2 251.4 263.9 32.1 28.0 5645 5887 4 7771.3 7173.5 259.6 259.8 31.9 28.0 5809 5722 5 7653.4 7417.1 255.3 266.8 31.9 28.0 5681 5946 6 7563.9 7361.1 251.6 262.6 32.0 28.0 5645 5890 7 7726.6 7321.2 256.6 262.0 32.1 28.0 5768 5837 8 7812.7 7242.1 261.1 258.3 31.9 28.0 5804 5806 9 7642.7 7320.6 255.3 264.6 31.8 28.0 5688 5910 10 7543.0 7354.7 252.3 264.9 31.9 28.0 5651 5866
Average 7668.0 7309.0 255.8 262.4 31.9 28.0 5723 5847 St. Dev. 91.1 86.4 3.3 3.3 0.1 0.0 67.8 74.8 % CV 1.2 1.2 1.3 1.3 0.3 0.0 1.2 1.3
90
Table 2 Simulated Arterial MOEs - AM Peak Hour Simulation Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV
Direction Average Delay, sec
AIMSUN NB 83.0 88.0 86.0 88.0 88.0 86.0 89.0 87.0 86.0 86.0 86.7 1.7 2.0
SB 90.0 90.0 88.0 91.0 88.0 89.0 87.0 86.0 87.0 89.0 88.5 1.6 1.8
SimTraffic NB 53.7 54.4 53.8 54.0 57.0 55.3 55.4 54.7 56.0 54.9 54.9 1.0 1.9 SB 52.2 48.2 49.5 47.6 52.6 54.5 51.1 49.7 49.3 52.9 50.8 2.2 4.4
Travel Time, sec
AIMSUN NB 165.0 170.0 169.0 170.0 170.0 168.0 171.0 169.0 167.0 168.0 168.7 1.8 1.0 SB 157.0 157.0 154.0 158.0 155.0 156.0 154.0 153.0 154.0 156.0 155.4 1.6 1.1
SimTraffic NB 138.9 140.4 138.8 139.2 141.8 140.2 140.8 139.6 141.0 140.6 140.1 1.0 0.7 SB 119.7 116.0 116.8 114.5 120.2 121.7 119.0 117.5 116.7 120.7 118.3 2.3 2.0
Average Speed, mph
AIMSUN NB 27.2 26.5 26.7 26.4 26.5 26.8 26.3 26.6 26.8 26.7 26.7 0.3 1.0 SB 23.9 23.8 24.4 23.6 24.2 23.9 24.3 24.4 24.3 24.0 24.1 0.3 1.2
SimTraffic NB 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 0.0 0.0
SB 29.0 30.0 30.0 30.0 29.0 29.0 29.0 30.0 30.0 29.0 29.5 0.5 1.8
Table 3 Field Observed Arterial MOEs - AM Peak
Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV Direction Travel Time, sec
NB 138.0 135.0 149.0 153.0 143.0 148.0 144.0 155.0 145.0 159.0 146.9 7.5 5.1 SB 133.0 134.0 132.0 137.0 121.0 135.0 125.0 130.0 125.0 134.0 130.6 5.2 4.0
Average Speed, mph NB 30.3 31.0 28.1 27.3 29.3 28.3 29.1 27.0 28.9 26.3 28.5 1.5 5.1 SB 25.6 25.4 25.8 24.9 28.2 25.2 27.3 26.2 27.3 25.4 26.1 1.1 4.1
91
Table 4 Simulated Link MOEs - AM Peak Hour
Simulation Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV Link No Average Delay, sec
AIMSUN
1 24.0 23.5 24.0 24.0 23.0 23.0 23.5 23.5 23.0 23.5 23.5 0.4 1.7 2 14.5 14.5 15.0 14.5 14.5 14.5 14.0 14.5 15.0 14.5 14.6 0.3 2.0 3 16.0 16.0 15.0 16.0 16.0 16.0 16.0 16.0 14.0 15.5 15.7 0.7 4.3 4 25.5 24.5 24.5 24.5 24.0 24.0 24.5 25.0 25.0 25.5 24.7 0.5 2.2
SimTraffic
1 27.7 27.8 27.9 26.8 29.7 28.0 27.2 28.0 28.5 28.1 28.0 0.8 2.8 2 31.9 29.9 31.4 31.5 33.1 34.0 31.7 32.3 33.8 33.8 32.3 1.3 4.1 3 29.5 30.3 30.1 29.7 32.3 30.3 31.1 30.1 31.7 32.7 30.8 1.1 3.6
4 21.6 20.8 20.1 20.0 20.8 21.7 20.8 20.0 19.9 19.9 20.6 0.7 3.3
Link Volume, vph
AIMSUN
1 1505 1613 1549 1517 1499 1448 1575 1529 1497 1467 1519.9 49.2 3.2 2 1270 1326 1320 1232 1272 1281 1233 1266 1232 1311 1274.3 35.8 2.8 3 1601 1644 1645 1595 1531 1540 1662 1621 1564 1540 1594.3 48.5 3.0 4 1157 1200 1195 1090 1153 1196 1126 1170 1136 1187 1161.0 35.9 3.1
SimTraffic
1 1547 1682 1645 1566 1639 1640 1590 1634 1600 1623 1616.6 40.6 2.5 2 1300 1207 1243 1222 1317 1319 1299 1299 1278 1285 1276.9 39.5 3.1 3 1508 1616 1606 1490 1611 1583 1552 1555 1529 1605 1565.5 45.7 2.9 4 1035 1012 1032 989 1083 1065 1062 1031 1043 1030 1038.2 27.1 2.6
Table 5 Field Link Volume - AM Peak Hour Link No. Travel Time, sec
Fie
ld D
ata 1 1590
2 1274 3 1565 4 906
92
Table 6 Simulated Network MOEs - Mid day Peak Hour
Simulation Runs
VMT, miles VHT, hours Average Speed, mph Flow Rate, vph
AIMSUN SimTraffic AIMSUN SimTraffic AIMSUN SimTraffic AIMSUN SimTraffic
1 10332.8 9962.8 354.3 411.8 31.2 24.0 7661 7887 2 10648.3 9970.5 369.5 412.8 30.9 24.0 7870 7808 3 10584.3 10074.3 369.7 418.1 30.8 24.0 7827 7926 4 10656.3 9934.9 366.4 413.9 31.0 24.0 7885 7793 5 10436.4 9854.8 355.8 412.6 31.3 24.0 7723 7761 6 10594.7 9992.3 366.5 416.5 30.9 24.0 7846 7863 7 10582.8 9978.3 363.9 413.7 31.1 24.0 7838 7853 8 10486.8 10089.4 357.4 420.9 31.3 24.0 7807 7905 9 10459.0 9795.0 360.1 406.5 31.0 24.0 7759 7748 10 10428.9 9961.5 367.7 419.0 30.5 24.0 7728 7858
Average 10521.0 9961.4 363.1 414.6 31.0 24.0 7794 7840 St. Dev. 107.4 88.3 5.8 4.2 0.2 0.0 73.2 60.4 % CV 1.0 0.9 1.6 1.0 0.8 0.0 0.9 0.8
93
Table 7 Simulated Arterial MOEs - Mid day Peak Hour
Simulation Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV
Direction Average Delay, sec
AIMSUN NB 81.0 82.0 84.0 83.0 80.0 88.0 84.0 80.0 81.0 84.0 82.7 2.5 3.0
SB 80.0 77.0 89.0 79.0 77.0 78.0 79.0 76.0 77.0 83.0 79.5 3.9 4.9
SimTraffic NB 77.3 80.3 79.1 81.5 76.1 79.3 77.2 80.4 80.0 84.1 79.5 2.3 2.9
SB 79.1 83.3 82.6 84.7 83.9 86.4 84.5 90.0 81.3 80.7 83.7 3.1 3.7
Travel Time, sec
AIMSUN NB 164.0 164.0 167.0 165.0 163.0 171.0 167.0 162.0 164.0 166.0 165.3 2.6 1.6
SB 147.0 145.0 156.0 146.0 144.0 146.0 146.0 143.0 145.0 150.0 146.8 3.7 2.5
SimTraffic NB 169.5 173.4 172.0 175.1 169.4 172.7 170.8 173.4 172.7 177.2 172.6 2.4 1.4
SB 152.8 156.5 155.9 158.5 157.0 159.9 158.3 163.1 155.0 153.7 157.1 3.0 1.9
Average Speed, mph
AIMSUN NB 28.8 28.6 28.4 28.5 28.8 27.3 28.3 29.0 28.6 28.3 28.5 0.5 1.6
SB 27.0 26.9 25.4 26.9 27.2 26.9 26.7 27.2 26.9 26.2 26.7 0.5 2.1
SimTraffic NB 25.0 24.0 25.0 24.0 25.0 24.0 25.0 24.0 24.0 24.0 24.4 0.5 2.1
SB 23.0 22.0 23.0 22.0 22.0 22.0 22.0 21.0 23.0 23.0 22.3 0.7 3.0
Table 8 Field Observed Arterial MOEs - Mid day Peak Hour
Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV Direction Travel Time, sec
NB 167.0 164.0 170.0 168.0 165.0 157.0 160.0 167.0 169.0 171.0 165.8 4.4 2.7 SB 159.0 144.0 158.0 150.0 155.0 152.0 160.0 153.0 143.0 157.0 153.1 6.0 3.9
Average Speed, mph NB 25.1 25.5 24.6 24.9 25.4 26.6 26.1 25.1 24.8 24.5 25.2 0.7 2.7 SB 21.4 23.7 21.6 22.7 22.0 22.4 21.3 22.3 23.8 21.7 22.3 0.9 4.0
94
Table 9 Simulated Link MOEs - Mid day Peak Hour
Simulation Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV Link No Average Delay, sec
AIMSUN
1 19.5 19.5 20.0 20.0 19.0 19.5 19.0 18.5 19.0 19.0 19.3 0.5 2.5 2 20.5 20.5 20.0 21.5 20.0 21.5 21.0 22.0 23.0 21.5 21.2 0.9 4.5
3 20.5 20.5 21.0 20.0 20.5 22.5 22.0 20.0 20.5 22.0 21.0 0.9 4.3 4 17.5 17.5 21.5 16.5 16.5 17.0 17.5 16.0 15.5 18.5 17.4 1.7 9.7
SimTraffic
1 38.7 39.2 37.5 39.4 36.8 39.6 37.3 40.1 39.0 42.7 39.0 1.7 4.3 2 46.6 48.3 46.3 49.5 47.3 48.1 51.0 49.1 48.4 45.0 48.0 1.7 3.6 3 39.4 41.7 41.8 40.8 39.6 42.0 40.2 40.7 40.2 41.8 40.8 1.0 2.4 4 30.7 31.1 32.5 32.3 31.9 35.8 29.0 36.7 30.8 30.5 32.1 2.4 7.5
Link Volume, vph
AIMSUN
1 1462 1507 1509 1524 1420 1502 1486 1465 1468 1476 1481.8 30.4 2.1 2 1581 1667 1634 1636 1613 1627 1679 1706 1716 1636 1649.3 42.1 2.6 3 1709 1809 1796 1757 1767 1829 1807 1752 1751 1768 1774.2 35.4 2.0 4 1908 1959 1964 1960 1948 1956 1985 2003 1969 1966 1961.6 24.7 1.3
SimTraffic
1 1574 1609 1559 1530 1536 1535 1590 1557 1552 1575 1561.7 25.5 1.6 2 1564 1546 1598 1595 1534 1573 1589 1644 1525 1563 1573.1 35.2 2.2 3 1682 1692 1743 1670 1652 1592 1653 1599 1660 1656 1659.9 43.4 2.6 4 1972 1936 1972 1972 1946 2038 1990 2059 1919 1974 1977.8 43.1 2.2
Table 10 Field Link Volume - Mid day Peak Hour
Link No. Travel Time, sec
Fie
ld D
ata 1 1563
2 1583
3 1670
4 1793
95
Table 11 Simulated Network MOEs - PM Peak Hour
Simulation Runs
VMT, miles VHT, hours Average Speed, mph Flow Rate, vph
AIMSUN SimTraffic AIMSUN SimTraffic AIMSUN SimTraffic AIMSUN SimTraffic
1 11573.5 10850.2 456.0 532.8 28.5 21.0 8671 8569 2 11667.0 11066.1 449.6 531.8 28.9 21.0 8754 8770 3 11900.6 10708.8 460.0 526.2 28.6 21.0 8921 8521 4 11410.8 10954.3 434.0 514.7 28.9 22.0 8557 8683 5 11492.4 10893.6 445.8 542.6 28.7 20.0 8662 8619 6 11654.2 10950.3 436.2 516.7 29.1 21.0 8740 8638 7 11654.2 10724.7 443.1 499.6 28.8 22.0 8562 8473 8 11425.2 10942.5 457.5 514.6 28.2 21.0 8739 8674 9 11528.4 10922.0 413.7 544.4 30.1 20.0 8621 8610 10 11760.7 11037.9 435.6 549.9 29.2 20.0 8840 8745
Average 11606.7 10905.0 443.2 527.3 28.9 20.9 8707 8630 St. Dev. 152.7 117.3 14.0 15.9 0.5 0.7 116.5 93.3 % CV 1.3 1.1 3.2 3.0 1.7 3.5 1.3 1.1
96
Table 12 Simulated Arterial MOEs - PM Peak Hour
Simulation Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV
Direction Average Delay, sec
AIMSUN NB 75.0 78.0 78.0 76.0 80.0 74.0 74.0 73.0 73.0 80.0 76.1 2.7 3.6
SB 95.0 94.0 90.0 89.0 88.0 93.0 94.0 96.0 90.0 96.0 92.5 3.0 3.2
SimTraffic NB 90.6 87.2 92.0 95.0 86.3 92.9 82.3 92.6 94.2 86.1 89.9 4.2 4.7
SB 120.8 129.6 110.7 117.1 122.0 131.0 109.2 120.2 122.8 127.0 121.0 7.3 6.0
Travel Time, sec
AIMSUN NB 158.0 160.0 160.0 159.0 163.0 156.0 156.0 155.0 155.0 162.0 158.4 2.9 1.8
SB 162.0 161.0 157.0 156.0 155.0 160.0 162.0 164.0 157.0 163.0 159.7 3.2 2.0
SimTraffic NB 174.7 172.0 176.8 179.4 170.3 177.4 166.0 177.6 179.1 170.5 174.4 4.5 2.6
SB 189.6 198.5 179.4 185.8 190.5 199.4 178.0 188.8 191.5 195.6 189.7 7.2 3.8
Average Speed, mph
AIMSUN NB 30.4 30.1 29.8 30.4 29.7 31.0 30.9 30.9 30.9 29.8 30.4 0.5 1.7
SB 23.4 23.5 23.8 24.1 24.2 23.5 23.3 23.0 24.0 23.1 23.6 0.4 1.8
SimTraffic NB 24.0 25.0 24.0 24.0 25.0 24.0 25.0 24.0 24.0 25.0 24.4 0.5 2.1
SB 19.0 18.0 20.0 19.0 18.0 18.0 20.0 19.0 18.0 18.0 18.7 0.8 4.4
Table 13 Field Observed Arterial MOEs - PM Peak Hour Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV Direction Travel Time, sec
NB 176.0 196.0 192.0 198.0 202.0 182.0 194.0 182.0 190.0 192.0 190.4 8.1 4.3 SB 213.0 225.0 230.0 231.0 217.0 237.0 244.0 233.0 204.0 215.0 224.9 12.4 5.5
Average Speed, mph NB 23.8 21.3 21.8 21.1 20.7 23.0 21.6 23.0 22.0 21.8 22.0 1.0 4.3 SB 16.0 15.1 14.8 14.8 15.7 14.4 14.0 14.6 16.7 15.9 15.2 0.8 5.6
97
Table 14 Simulated Link MOEs - PM Peak Hour
Simulation Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV Link No Average Delay, sec
AIMSUN
1 18.5 19.5 18.0 19.0 19.5 17.0 17.0 16.5 17.5 18.5 18.1 1.1 5.9
2 24.0 24.0 24.0 22.5 23.5 24.0 25.0 25.5 22.5 25.0 24.0 1.0 4.2
3 21.5 22.5 23.5 21.5 22.0 22.5 22.0 22.0 22.0 22.0 22.2 0.6 2.6
4 18.5 17.0 16.0 15.5 15.0 16.0 17.5 17.0 17.0 16.5 16.6 1.0 6.2
SimTraffic
1 50.9 52.5 53.6 55.2 52.5 53.2 47.2 53.7 54.7 49.6 52.3 2.4 2.8
2 60.9 64.2 58.1 60.3 63.7 68.9 56.6 58.8 57.8 58.3 60.8 3.8 4.1
3 46.7 45.4 45.4 46.2 44.0 46.1 43.1 47.5 45.5 45.3 45.5 1.3 3.6
4 44.1 50.5 40.7 44.0 41.0 46.6 42.4 46.2 50.2 53.8 46.0 4.4 3.3
Link Volume, vph
AIMSUN
1 1486 1629 1650 1578 1553 1535 1488 1478 1538 1549 1548.2 58.3 3.8 2 1867 1929 1946 1888 1862 1891 1912 1944 1842 1957 1903.5 39.8 2.1 3 1818 1921 2007 1865 1919 1897 1829 1896 1858 1971 1898.0 59.7 3.1 4 2294 2266 2330 2204 2212 2279 2293 2337 2295 2313 2282.1 44.7 2.0
SimTraffic
1 1645 1673 1704 1729 1687 1678 1605 1684 1726 1655 1678.6 37.5 2.2
2 1869 1876 1795 1858 1833 1882 1750 1793 1805 1763 1822.4 47.9 2.6
3 1748 1742 1750 1832 1774 1775 1684 1807 1802 1808 1772.2 43.1 2.4
4 2175 2206 2062 2161 2131 2194 2139 2177 2183 2222 2165.0 45.7 2.1
Table 15 Field Link Volumes - PM Peak Hour
Link No. Travel Time, sec
Fie
ld D
ata 1 1725
2 1830 3 1783 4 2045
98
Appendix – D
Hypothesis Tests – Before Calibration
99
Table 16 Hypothesis Tests for Arterial MOEs - AM Peak
Model
Travel Time, sec Speed, mph
NB SB NB SB
AIM
SU
N
Sample Mean
Model 168.7 155.4 26.7 24.1 Field 146.9 130.6 28.5 26.1
Standard Deviation
Model 1.8 1.6 0.3 0.3 Field 7.5 5.2 1.5 1.1
t - test
t - score 8.975 14.297 -4.054 -5.851
Decision on H0
Reject Reject Reject Reject
f - test
f - score 0.056 0.099 0.031 0.067
Decision on H0
Not Reject
Not Reject
Not Reject
Not Reject
Sim
Tra
ffic
Sample Mean
Model 140.1 118.3 30 29.5
Field 146.9 130.6 28.5 26.1
Standard Deviation
Model 1 2.3 0 0.5
Field 7.5 5.2 1.5 1.1
t - test
t - score -2.839 -6.805 3.155 8.887
Decision on H0
Reject Reject Reject Reject
f - test
f - score 0.017 0.197 0.000 0.240
Decision on H0
Not Reject
Not Reject
Not Reject
Not Reject
For t - test: Null Hypothesis: mean of simulated MOE = mean of field MOE Alternative hypothesis: mean of simulated MOE ≠ mean of field MOE Alpha = 0.05 For f - test: Null Hypothesis: variance of simulated MOE = variance field MOE Alternative hypothesis: variance of simulated MOE ≠ variance of field MOE Alpha = 0.05
100
Table 17 Hypothesis Tests for Arterial MOEs - Mid day Peak
Model
Travel Time, sec Speed, mph
NB SB NB SB
AIM
SU
N
Sample Mean
Model 165.3 146.8 28.5 26.7 Field 165.8 153.1 25.2 22.3
Standard Deviation
Model 2.6 3.7 0.5 0.5 Field 4.4 6.0 0.7 0.9
t - test
t - score -0.308 -2.828 12.278 13.402
Decision on H0
Not Reject
Reject Reject Reject
f - test
f - score 0.338 0.391 0.438 0.378
Decision on H0
Reject Reject Reject Reject
Sim
Tra
ffic
Sample Mean
Model 172.6 157.1 24.4 22.3
Field 165.8 153.1 25.2 22.3
Standard Deviation
Model 2.4 3 0.5 0.7
Field 4.4 6 0.7 0.9
t - test
t - score 4.268 1.873 -3.114 0.022
Decision on H0
Reject Not
Reject Reject
Not Reject
f - test
f - score 0.294 0.260 0.557 0.574
Decision on H0
Not Reject
Not Reject
Reject Reject
For t - test: Null Hypothesis: mean of simulated MOE = mean of field MOE Alternative hypothesis: mean of simulated MOE ≠ mean of field MOE Alpha = 0.05 For f - test: Null Hypothesis: variance of simulated MOE = variance field MOE Alternative hypothesis: variance of simulated MOE ≠ variance of field MOE Alpha = 0.05
101
Table 3 Hypothesis Tests for Arterial MOEs- PM Peak
Model
Travel Time, sec Speed, mph
NB SB NB SB
AIM
SU
N
Sample Mean
Model 158.4 159.7 30.4 23.6 Field 190.4 224.9 22.0 15.2
Standard Deviation
Model 2.9 3.2 0.5 0.4 Field 8.1 12.4 1.0 0.8
t - test
t - score -11.774 -16.127 24.438 28.110
Decision on H0
Reject Reject Reject Reject
f - test
f - score 0.126 0.067 0.290 0.244
Decision on H0
Not Reject
Not Reject
Not Reject
Not Reject
Sim
Tra
ffic
Sample Mean
Model 174.4 189.7 24.4 18.7
Field 190.4 224.9 22 15.2
Standard Deviation
Model 4.5 7.2 0.5 0.8
Field 8.1 12.4 1 0.8
t - test
t - score -5.474 -7.766 6.962 9.381
Decision on H0
Reject Reject Reject Reject
f - test
f - score 0.306 0.340 0.292 0.945
Decision on H0
Not Reject
Reject Not
Reject Reject
For t - test: Null Hypothesis: mean of simulated MOE = mean of field MOE Alternative hypothesis: mean of simulated MOE ≠ mean of field MOE Alpha = 0.05 For f - test: Null Hypothesis: variance of simulated MOE = variance field MOE Alternative hypothesis: variance of simulated MOE ≠ variance of field MOE Alpha = 0.05
102
Appendix – E
Simulation Results after Model Calibration - Graphs
103
Figure 1 Simulated vs. Observed NB Travel Time - AM Peak
Figure 2 Simulated vs. Observed SB Travel Time - AM Peak
60
80
100
120
140
160
180
0 1 2 3 4 5 6 7 8 9 10
Tra
ve
l ti
me
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
60
80
100
120
140
160
180
0 1 2 3 4 5 6 7 8 9 10
Tra
ve
l ti
me
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
104
Figure 3 Simulated vs. Observed NB Average Speed - AM Peak
Figure 4 Simulated vs. Observed SB Average Speed - AM Peak
10.0
15.0
20.0
25.0
30.0
35.0
0 1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
Field
10.0
15.0
20.0
25.0
30.0
35.0
0 1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
Field
105
Figure 5 Simulated vs. Observed NB Travel Time-Mid day Peak
Figure 6 Simulated vs. Observed SB Travel Time-Mid day Peak
130
140
150
160
170
180
1 2 3 4 5 6 7 8 9 10
Tra
ve
l ti
me
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
120
130
140
150
160
170
180
1 2 3 4 5 6 7 8 9 10
Tra
ve
l ti
me
, se
c
Simulation Runs
AIMSUN
SimTraffic
Field
106
Figure 7 Simulated vs. Observed NB Average Speed-Mid day Peak
Figure 8 Simulated vs. Observed SB Average Speed- Mid day Peak
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
Field
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation Runs
AIMSUN
SimTraffic
Field
107
Figure 9 Simulated vs. Observed NB Travel Time -PM Peak
Figure 10 Simulated vs. Observed SB Travel Time - PM Peak
120
140
160
180
200
220
240
1 2 3 4 5 6 7 8 9 10
Tra
ve
l ti
me
, se
c
Simulation runs
AIMSUN
SimTraffic
Field
140
160
180
200
220
240
260
1 2 3 4 5 6 7 8 9 10
Tra
ve
l ti
me
, se
c
Simulation runs
AIMSUN
SimTraffic
Field
108
Figure 11 Simulated vs. Observed NB Average Speed - PM Peak
Figure 12 Simulated vs. Observed SB Average Speed - PM Peak
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation runs
AIMSUN
SimTraffic
Field
5.0
10.0
15.0
20.0
25.0
30.0
1 2 3 4 5 6 7 8 9 10
Av
era
ge
Sp
ee
d,
mp
h
Simulation runs
AIMSUN
SimTraffic
Field
109
Appendix – F
Simulation Results after Model Calibration - Tables
110
Table 18 Simulated Arterial MOEs after Calibration - AM Peak Hour
Simulation Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV
Direction Average Delay, sec
AIMSUN NB 74.0 73.0 76.0 74.0 71.0 78.0 77.0 74.0 72.0 79.0 74.8 2.6 3.5
SB 75.0 75.0 78.0 74.0 75.0 77.0 77.0 78.0 76.0 77.0 76.2 1.4 1.8
SimTraffic NB 62.1 63.0 61.8 62.1 61.4 60.9 60.9 61.2 62.8 61.3 61.8 0.7 1.2
SB 57.0 57.1 58.9 58.4 58.1 55.9 56.4 56.5 58.2 58.6 57.5 1.1 1.8
Travel Time, sec
AIMSUN NB 154.0 153.0 156.0 154.0 151.0 158.0 157.0 154.0 152.0 159.0 154.8 2.6 1.7 SB 141.0 141.0 144.0 140.0 141.0 143.0 143.0 144.0 142.0 143.0 142.2 1.4 1.0
SimTraffic NB 141.7 141.0 141.9 142.0 142.0 142.1 141.9 143.0 141.9 142.0 142.0 0.5 0.3
SB 121.8 122.1 122.2 122.8 121.5 121.6 121.0 120.7 122.8 122.1 121.9 0.7 0.6
Average Speed, mph
AIMSUN NB 29.6 29.8 29.0 29.5 30.1 28.7 28.9 29.4 29.8 28.4 29.3 0.5 1.9 SB 26.8 26.9 26.2 26.9 26.9 26.4 26.5 26.1 26.7 26.5 26.6 0.3 1.1
SimTraffic NB 30.0 29.0 30.0 30.0 29.0 30.0 30.0 29.0 29.0 29.0 29.5 0.5 1.8
SB 28.0 29.0 28.0 29.0 29.0 28.0 28.0 29.0 28.0 28.0 28.4 0.5 1.8
111
Table 19 Simulated Arterial MOEs after Calibration - Mid day Peak Hour
Simulation Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV
Direction Average Delay, sec
AIMSUN NB 84.0 88.0 83.0 81.0 81.0 84.0 85.0 85.0 81.0 85.0 83.7 2.3 2.7
SB 72.0 80.0 76.0 76.0 79.0 82.0 88.0 82.0 79.0 76.0 79.0 4.4 5.6
SimTraffic NB 77.3 80.3 79.1 81.5 76.1 79.3 77.2 80.4 80.0 84.1 79.5 2.3 2.9
SB 79.1 83.3 82.6 84.7 83.9 86.4 84.5 90.0 81.3 80.7 83.7 3.1 3.7
Travel Time, sec
AIMSUN NB 166.0 171.0 165.0 163.0 163.0 166.0 167.0 168.0 163.0 168.0 166.0 2.6 1.6
SB 140.0 147.0 143.0 143.0 145.0 150.0 155.0 149.0 149.0 143.0 146.4 4.5 3.0
SimTraffic NB 169.5 173.4 172.0 175.1 169.4 172.7 170.8 173.4 172.7 177.2 172.6 2.4 1.4
SB 152.8 156.5 155.9 158.5 157.0 159.9 158.3 163.1 155.0 153.7 157.1 3.0 1.9
Average Speed, mph
AIMSUN NB 28.2 27.6 28.5 28.8 28.8 28.4 27.9 27.9 28.9 28.1 28.3 0.4 1.6
SB 28.4 26.6 27.3 27.5 23.8 23.2 25.6 26.3 24.6 27.5 26.1 1.7 6.7
SimTraffic NB 25.0 24.0 25.0 24.0 25.0 24.0 25.0 24.0 24.0 24.0 24.4 0.5 2.1
SB 23.0 22.0 23.0 22.0 22.0 22.0 22.0 21.0 23.0 23.0 22.3 0.7 3.0
112
Table 20 Simulated MOEs after Calibration - PM Peak Hour
Simulation Runs 1 2 3 4 5 6 7 8 9 10 Average St. Dev % CV
Direction Average Delay, sec
AIMSUN NB 77.0 77.0 81.0 82.0 84.0 90.0 79.0 81.0 77.0 83.0 81.1 4.0 5.0 SB 151.0 134.0 127.0 113.0 135.0 144.0 165.0 141.0 165.0 125.0 140.0 16.9 12.1
SimTraffic NB 97.4 90.2 101.3 97.9 105.1 90.2 111.9 114.1 115.2 104.2 102.8 9.1 8.9 SB 146.7 133.2 132.8 124.7 129.4 138.4 130.0 138.4 144.0 127.2 134.5 7.2 5.4
Travel Time, sec
AIMSUN NB 169.0 169.0 173.0 174.0 176.0 182.0 172.0 173.0 169.0 175.0 173.2 4.0 2.3 SB 218.0 201.0 194.0 200.0 203.0 211.0 220.0 208.0 215.0 192.0 206.2 9.8 4.7
SimTraffic NB 180.9 174.7 185.4 182.2 189.3 174.4 196.7 199.3 199.7 188.7 187.1 9.4 5.0 SB 215.6 201.5 201.1 193.7 198.3 206.4 198.9 206.5 212.3 195.8 203.0 7.1 3.5
Average Speed, mph
AIMSUN NB 28.2 28.4 27.1 27.0 27.0 26.2 27.6 27.6 28.0 27.2 27.4 0.7 2.4 SB 17.4 19.0 20.0 21.0 19.1 18.0 16.2 18.2 15.4 19.9 18.4 1.7 9.5
SimTraffic NB 23.0 24.0 23.0 23.0 22.0 24.0 22.0 21.0 21.0 22.0 22.5 1.1 4.8 SB 16.0 18.0 17.0 18.0 18.0 17.0 18.0 17.0 17.0 18.0 17.4 0.7 4.0
113
Appendix – G
Hypothesis Tests – After Calibration.
114
Table 1 Hypothesis Tests for Arterial MOEs - AM Peak
Model
Travel Time, sec Speed, mph
NB SB NB SB
AIM
SU
N
Sample Mean
Model 154.8 142.2 29.3 26.6 Field 146.9 130.6 28.5 26.1
Standard Deviation
Model 2.6 1.4 0.5 0.3 Field 7.5 5.2 1.5 1.1
t - test
t - score 3.154 6.773 1.540 1.303
Decision on H0
Reject Reject Not
Reject Not
Reject
f - test
f - score 0.122 0.071 0.142 0.078
Decision on H0
Not Reject
Not Reject
Not Reject
Not Reject
Sim
Tra
ffic
Sample Mean
Model 142.0 121.9 29.5 28.4
Field 146.9 130.6 28.5 26.1
Standard Deviation
Model 0.5 0.7 0.5 0.5
Field 7.5 5.2 1.5 1.1
t - test
t - score -2.090 -5.237 1.946 6.005
Decision on H0
Not Reject
Reject Not
Reject Reject
f - test
f - score 0.004 0.017 0.131 0.231
Decision on H0
Not Reject
Not Reject
Not Reject
Not Reject
For t - test: Null Hypothesis: mean of simulated MOE = mean of field MOE Alternative hypothesis: mean of simulated MOE ≠ mean of field MOE Alpha = 0.05 For f - test: Null Hypothesis: variance of simulated MOE = variance field MOE Alternative hypothesis: variance of simulated MOE ≠ variance of field MOE Alpha = 0.05
115
Table 2 Hypothesis Tests for Arterial MOEs - Mid day Peak
Model
Travel Time, sec Speed, mph
NB SB NB SB
AIM
SU
N
Sample Mean
Model 166.0 146.4 28.3 26.1 Field 165.8 153.1 25.2 22.3
Standard Deviation
Model 2.6 4.5 0.5 1.7 Field 4.4 6.0 0.7 0.9
t - test
t - score 0.123 -2.845 11.762 6.113
Decision on H0
Not Reject
Reject Reject Reject
f - test
f - score 0.349 0.556 0.421 3.840
Decision on H0
Reject Reject Reject Reject
Sim
Tra
ffic
Sample Mean
Model 172.6 157.1 24.4 22.3
Field 165.8 153.1 25.2 22.3
Standard Deviation
Model 2.4 3 0.5 0.7
Field 4.4 6 0.7 0.9
t - test
t - score 4.268 1.873 -3.114 0.022
Decision on H0
Reject Not
Reject Reject
Not Reject
f - test
f - score 0.294 0.260 0.557 0.574
Decision on H0
Not Reject
Not Reject
Reject Reject
For t - test: Null Hypothesis: mean of simulated MOE = mean of field MOE Alternative hypothesis: mean of simulated MOE ≠ mean of field MOE Alpha = 0.05 For f - test: Null Hypothesis: variance of simulated MOE = variance field MOE Alternative hypothesis: variance of simulated MOE ≠ variance of field MOE Alpha = 0.05
116
Table 3 Hypothesis Tests for Arterial MOEs - PM Peak
Model
Travel Time, sec Speed, mph
NB SB NB SB
AIM
SU
N
Sample Mean
Model 173.2 206.2 27.4 18.4 Field 190.4 224.9 22.0 15.2
Standard Deviation
Model 4.0 9.8 0.7 1.7 Field 8.1 12.4 1.0 0.8
t - test
t - score -6.023 -3.750 15.303 5.261
Decision on H0
Reject Reject Reject Reject
f - test
f - score 0.243 0.623 0.471 4.230
Decision on H0
Not Reject
Reject Reject Reject
Sim
Tra
ffic
Sample Mean
Model 187.1 203 22.5 17.4
Field 190.4 224.9 22 15.2
Standard Deviation
Model 9.4 7.1 1.1 0.7
Field 8.1 12.4 1 0.8
t - test
t - score -0.836 -4.853 1.076 6.345
Decision on H0
Not Reject
Reject Not
Reject Reject
f - test
f - score 1.334 0.328 1.279 0.681
Decision on H0
Not Reject
Reject Not
Reject Reject
For t - test: Null Hypothesis: mean of simulated MOE = mean of field MOE Alternative hypothesis: mean of simulated MOE ≠ mean of field MOE Alpha = 0.05 For f - test: Null Hypothesis: variance of simulated MOE = variance field MOE Alternative hypothesis: variance of simulated MOE ≠ variance of field MOE Alpha = 0.05