port everglades area cube avenue mesoscopic model … · port everglades area cube avenue...

PORT EVERGLADES AREA CUBEAVENUE MESOSCOPIC MODEL

presented by

Akbar Bakhshi Zanjani, Citilabs

Date

Dec 8-10, 2015

Scope of the Project

• Obtain Regional macro model (SERPM 7ABM)

• Mesoscopic model development and simulation using CubeAvenue

• Validate Avenue model to reflect the data collected usingdynamic O-D estimation (Cube Analyst Drive)

• Forecasting of horizon year Avenue model

• Sensitivity testing

Port Everglades DTA Model

Microscopic Model

Travel Demand ModelSERPM

Mesoscopic ModelAvenues

Multi Resolution Drill DownAbility to Scenario Plan at an OperationalLevel.

Procedure for Port Simulation

Run Regional Model

TDM Subarea Analysis forProject Area

O-D Trips by Time Segment

Dynamic O-D Estimationby Time Segments using

Cube Analyst Drive

Divide Peak PeriodTrips by 12

Calibration & Validation

Static O-D Estimation forModel Period using Cube

Analyst Drive

Subarea Network

Peak Period ApproachCounts

Using All Counts

*Subarea Analysis is performed for both base (2010) and future years (2040) in SERPM 7 ABM

Cube Model (Subarea Analysis)

Static ODME (AM Peak*)

Dynamic ODME (AM Peak*)

DTA Model (AM Peak*)

MODEL INPUTS

• 4,584 zones with two scenario years (2010,2040)• 5 time periods:

– Early: 10pm - 6am– AM Peak: 6am – 9am– Midday: 9am - 3pm– PM Peak: 3pm – 7pm– Evening: 7pm – 10pm

• Subarea : 300 zones (230 internals)• Storage is calculated using following formulas:

– Freeways:• Link distance * number of lanes * 220

– Other roadways:• Link distance * number of lanes * 330

Information of SERPM 7.0 ABM

• Capacity values for each link are re-calculated based onfacility/area type and according to lookup tables in theSERPM 7 ABM

Capacity Values Used in SERPM Network

• AM and PM peak period trip tables from SERPM 7 ABM

• Subarea trip table is extracted in the “subarea analysis”application

– Similar highway assignment scripts as in SERPM 7 ABM areused to generate subarea trip tables

– Other inputs to this highway program, such as turn penalties,are also obtained from SERPM 7.

• Three major vehicle classes:

– Auto trips

– Long-Haul truck trips

– Short-haul truck trips

Trip Tables

• SERPM 7 network isused as the basenetwork for the studyarea (5 miles radius ofPort Everglades)

Updating Highway Network

• Modifying/updating the highway network to reflectthe existing highway facilities

– ramp distances,

– addition of roundabouts on Hollywood Blvd,

– number of lanes,

– capacities,

– True shape Display

Updating Highway Network

• SERPM network does not reflect the actual network ata few locations

Examples of Network Modifications

SERPM Network (US 1 and I-595)


Actual Network (US 1 and I-595)


Source: Google Maps

Modified Network (US 1 and I-595)


SERPM Network


Actual Number of Lanes


Source: Google Maps

Modified Network


Short Ramps

SERPM NetworkModified Network


Junction Data

Junctions with available Data(43 intersections)

VALIDATION

• Highway network validation (as explained previously)

• Development of Prototype Cube Avenue Model– A mesoscopic simulation to simulate the O-D trips into the highway

network for 12 time segments for each model period as AM peak and PMpeak.

– Uses the similar modeling components and settings as specified in theSERPM regional model.

– Outputs of the model Run: loaded highway network

packet log files

– These output results can be used to identify the heavily congestedlocations with displaying bandwidths of queues and link flows as well asanimating each individual vehicle simulation.

– The estimation results are summarized for VMT (Vehicle-Miles Traveled)and VHT (Vehicle-Hours Traveled) per each facility type in a post-process.

Validation

Validation

• Observed traffic count data– Traffic count data for 181 locations are constructed for every 15-min

interval for AM peak and PM peak, respectively.– These count data are used as part of model validation to replicate the

traffic pattern in the project area.

• Static O-D Matrix Estimation (ODME) using Cube Analyst Drive– the original O-D trips are based on the base year (2010) of the SERPM

ABM– Observed traffic count data provided for year 2014– Adjust the trip table based on peak period traffic counts

• Dynamic O-D Matrix Estimation (ODME) using Cube AnalystDrive– To replicate the traffic patterns in the project area for Year 2014– It modifies the O-D trips to provide the valid estimation corresponding

to the observed traffic counts

Avenue Packet Animation

Avenue Bandwidth Animation

Count Locations (181 locations)

Facility Code Facility TypeNO. ofCount

Locations

41Higher Speed

Interrupted Facility134

61Lower Speed Facility &

Collector27

71 Freeway on-Ramp 3

73 Freeway off-Ramp 17

Total 181

Observed Traffic Count Data

Traffic Mode Type FHWA Vehicle Classification

Auto 1) Motorcycle2) Passenger cars3) Pickups, panel, vans4) Buses

Short-haul trucks 5) Single unit 2-axle trucks6) Single unit 3-axle trucks7) Single unit 4 or more-axle trucks

Long-haul trucks 8) Single trailer 3- or 4-axle trucks9) Single trailer 5-axle trucks10) Single trailer 6 or more-axle trucks11) Multi-trailer 5 or more-axle trucks12) Multi-trailer 6-axle trucks13) Multi-trailer 7 or more-axle trucks

• RMSE values for link approach counts by differentfacility types:

• AM Peak:

FacilityCode

Facility TypeNO. of Count

Locations

RMSE%

Auto SH Truck LH Truck

41Higher Speed Interrupted

Facility134 18.2 30.4 29.1


Collector27 23.4 21.4 28.5

71 Freeway on-Ramp 3 4.5 15.2 14.9

73 Freeway off-Ramp 17 22.7 25.2 28.4

Total 181 19.2 31.1 30.0

Results

• PM Peak:

Results

FacilityCode

Facility TypeNO. of Count

Locations

RMSE%

Auto SH Truck LH Truck

41Higher Speed Interrupted

Facility134 20.7 25.6 26.0


Collector27 14.4 38.7 37.0

71 Freeway on-Ramp 3 6.7 21.3 24.9

73 Freeway off-Ramp 17 16.3 22.7 18.3

Total 181 20.7 26.7 26.9

FUTURE YEAR ANALYSIS

• Developing the future-year Cube Avenue model to estimate thetraffic volumes and to find out the bottleneck locations in thefuture year.

• The future year scenario is tentatively selected as Year 2025, butCitilabs may implement another future year analysis if requestedby the FDOT staffs.

• Peak Period OD trip table– interpolating the O-D trips between two scenario years as 2010 and 2040

• OD Trip table by 12 time segments for each peak period– Using the OD pair proportions in the estimated trip tables for the base

year (2014) to reflect the consistent traffic patterns

Future Year Scenario


Performance Measures(AM peak period)

Base year 2014 Future Year 2025

VMT 3,026 M 3,292 M

VHT 223 M vehicle-hours 314 M vehicle-hours

Average delay 265 hours 537 hours

Performance Measures(PM peak period)

Base year 2014 Future Year 2025

VMT 3,137 M 3,374 M



SENSITIVITY ANALYSIS

• 4 different scenarios are defined for sensitivityanalysis to estimate the effect of the changes by theVMT, VHT and average delay:– ±5% change in the trip ends for Port Everglades

– ±10% change in the trip ends for Port Everglades

Sensitivity Analysis


Performance Measures(AM peak period)

Base year 2014 10% increase in the tripproductions from PortEverglades

VMT 3,026 M 3,054 M



• SERPM 7 ABM network was modified to reflect the actualroadway system in the area of study

• A model was developed in Cube platform to simulatetraffic at a mesoscopic level

• Input files were obtained from regional model as well asother sub-consultant firms

• Model validation was performed using observed counts

• Forecasting and sensitivity testing of the horizon yearAvenue model

• Final Model Delivery and Documentation

Summary

Thank You!

• Questions/Feedback?

[email protected]

port everglades area cube avenue mesoscopic model … · port everglades area cube avenue...

Documents