DKS Associates

A Hybrid Meso-Microsimulation Model for Corridor System Management PlansA Hybrid Meso-Microsimulation Model for Corridor System Management Plans

Session 10: Corridor Studies

May 19, 20091:30 – 5:00 PM

Surachet Pravinvongvuth (Chet)

Bill Loudon

DKS Associates

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• Corridor System Management Plan (CSMP)

• Travel Demand vs. Simulation Models• Micro vs. Meso Simulation Models• US-101 Corridor• Modeling Methodology and Techniques

Outline

A Hybrid Model for CSMPA Hybrid Model for CSMP

CSMP – A New Approach

A new approach of taking planning through operations• Using simulation as a planning tool• Taking a new look at existing conditions• Creating a living document that will

extend the life of the corridor plan


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Corridor System Management Plans (CSMP)


Summarize Corridor

Conditions

Summarize Corridor

Conditions Develop PreliminaryPerformance

Sta

keh

old

er In

volv

emen

t –

Co

nse

nsu

s B

uild

ing

ComprehensiveCorridor Performance

Assessment

Evaluate SystemManagement Approach Ensure

Adequate Detection

Ensure Adequate Detection

Identify Causality and Degradation

Identify Causality and Degradation

Test Scenarios

Forecast Future TravelDevelop CSMP

Develop CSMP

Acceptance by MPO and CaltransDistrict – 5, District – 7 and HQ

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• Simulating a very large corridor (50*4 miles2)• Dealing with multiple stakeholders • Integrating information from three existing

travel demand models• Bridging the gap between travel demand

models and simulation models • Developing future-year simulation models• Assessing operational strategies• Making the processes livable in supporting

future CSMPs.

Key Challenges


Santa Barbara County

Ventura County

Goleta Santa BarbaraCarpinteria

Ventura

Oxnard

US-101 at the CMIA Project AreaUS-101 at the CMIA Project Area


Reality

Travel Demand Model

Simulation Model

Travel Demand vs. Simulation Models


(Static) Travel Demand ModelA Hybrid Model for CSMPA Hybrid Model for CSMP

(Dynamic) Simulation ModelA Hybrid Model for CSMPA Hybrid Model for CSMP

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• Simulates the movement of individual vehicles based on car-following and lane-changing logics.

• Has the highest detailed assessment (but the size of network is limited due to its computationally expensive and sensitive to gridlocks).

MICROscopic Simulation


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• Relatively less detailed assessment but can cover a larger network.

• Unit of traffic flow is the individual vehicle; however, their movements are governed by the basis of aggregate speed/volume relationship instead of car-following and lane-changing theories.

MESOscopic Simulation


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Hybrid Simulation

• Take advantages of microsimulation to model roadway segments of interest and yield relatively less detailed in modeling side streets.

• Both parts are simulated simultaneously within the same run--no discrepancy between the two parts.


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US-101 Hybrid Simulation Model

in Santa Barbara


CoreCoreInnerInner

OuterOuter

Outer

Inner

Core

US-101

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US-101 Hybrid Simulation Model

in Ventura


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• 247 intersection counts• 24 mainline manual counts• All on/off ramp counts for the all freeways

(50*4 miles2)• 30 arterial loop counts• Tach runs for 2 days (8 probes, 15-minute

headway)• Speed detectors, camcorders, etc.

Data Collection


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Base-year Modeling Methodology


2008 Travel Demand Model

Network Refinement

Subarea Extraction

Observed Counts O-D Trip Table Estimation

Subarea Network

Subarea O-D Trip Table

2000 SBCAG Model/2005 VCTC Model


Model UpdateNetwork Assumptions(2008)

Interface Model

Refined Network(with simulation zones)

O-D Trip Tables

Add Details of Roadways(meso or micro levels)

Peak Spreading/O-D Shifting

Temporal Origin Count Profiles

Base-year Hybrid Simulation Model

Simulation NetworkO-D Trip Tables (with temporal loading profiles)

Other Observed Data

Hybrid Simulation Model Calibration

Calibrated Simulation Network

Calibrated O-D Trip Tables

2008 Seed O-Ds(based on simulation zones)



2008Signal Timing Plans

Selected Roadways

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Base-year Modeling Methodology (1)


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Base-year Modeling Methodology (2)


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Future-year Modeling Methodology


Future-year Hybrid Simulation Model

Base-year Hybrid Simulation Model

Calibrated O-D Trip Tables

Network Assumptions(CSMP Future Analysis Year)

Fratar Method

CSMP Future Analysis Year O-D Trip Tables

CSMP Future Analysis Year Network

Identify Changes of Trip Productions and Trip Attractions (at each simulation zone)

Future-yearTravel Demand Model



Model Update

Subarea Extraction

Subarea O-D Trip Table

Future-year Seed O-Ds(based on simulation zones)

Calibrated Simulation Network

Peak Spreading/O-D Shifting

2008 Seed O-Ds(based on simulation zones)

Adjusted O-D Trip Tables(CSMP Future Analysis Year)

Signal Timing Plans(optimized in Synchro)

Selected Roadways(same coverage as base-year)

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Future-year Modeling Methodology (1)


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Future-year Modeling Methodology (2)


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Modeling Arterials

Real Network (with Intersection Counts)Real Network (with Intersection Counts)


Roadways in Reality > Travel Demand Model > Simulation ModelRoadways in Reality > Travel Demand Model > Simulation Model

Reality:- Too many intersections- Traffic counts are unbalancedSo, what’s best for simulation world?

Reality:- Too many intersections- Traffic counts are unbalancedSo, what’s best for simulation world?

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Modeling Arterials (2)


Subarea NetworkSubarea Network

SISI

SISI

TDM LinkTDM Link

Sim ZoneSim Zone

Sim ZoneSim Zone

Sim ZoneSim ZoneDIDI

DIDIDIDI

DIDI

DZDZ

DZDZDZDZ

DZDZ

DZDZSimulation Link

Simulation Link DZDZ

DZ

DZ

DZ

DZ

TDM = Travel Demand ModelSI = Study IntersectionDI = Dummy IntersectionSim Zone = Simulation ZoneDZ = Dummy Zone

TDM = Travel Demand ModelSI = Study IntersectionDI = Dummy IntersectionSim Zone = Simulation ZoneDZ = Dummy Zone

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Modeling Arterials (3)


Interface NetworkInterface Network

SISI

SISI

Sim ZoneSim Zone

Sim ZoneSim Zone

Sim ZoneSim ZoneDIDI

DIDI

DZDZ

DZDZ

Simulation Link

Simulation Link

DZ

DZ

DZ

DZ

SI = Study IntersectionDI = Dummy IntersectionSim Zone = Simulation ZoneDZ = Dummy Zone

SI = Study IntersectionDI = Dummy IntersectionSim Zone = Simulation ZoneDZ = Dummy Zone

Need only one “Dummy Intersection” between two “Study Intersections”Need only one “Dummy Intersection” between two “Study Intersections”

Combine O-Ds to remove

unnecessary Dummy Zones

Combine O-Ds to remove

unnecessary Dummy Zones

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Travel Demand Model vs. Interface Model

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Travel Demand Model vs. Interface Model (2)


Combined DemandsCombined Demands

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Interface Model

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Study Intersection

Study Intersection

Dummy Intersection

Dummy Intersection

A zone at each boundary link (one-to-one mapping)A zone at each boundary link (one-to-one mapping)


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Simulation Model


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Path-based Routing


809 { 1031 1032 1033 1034 1035 31209 1037 1038 1039 1040 1041 1042 1043 1134 1135 1136 1137 1138 1165 1139 36582 1166 1140 1141 28840 28838 28744 36591 28851 28941 31381 }

809 { 1031 1032 1033 1034 1035 31209 1037 1038 1039 1040 1041 1042 1043 1134 1135 1136 1137 1138 1165 1139 36582 1166 1140 1141 28840 28838 28744 36591 28851 28941 31381 }

Path IDPath ID { Sequence of Link IDs }{ Sequence of Link IDs }

Trip Data TableTrip Data TablePath TablePath Table

• Allows more control to vehicle routing.

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• There is a big gap between the travel demand world and the simulation world.

• We hope that our approach can tighten this gap.

• There is a need for a dynamic ODME to help calibration.

• We believe that the path-based routing is the right trend for traffic simulation.

Guidance


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• Base Case• Dynamic User Equilibrium

• Short-term Impacts• Fixed paths (from Base Case)

• Long-term Impacts• New Dynamic User Equilibrium

Guidance (2)


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• Hybrid simulation is a new tool to model corridors.

• The proposed approach is quite a unique technique to integrate travel demand models with simulation models.

• Understanding traffic assignment is crucial for corridor studies; we need a good user-manual.

Conclusions


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Thank YouThank You

[email protected]@dksassociates.com

DKS Associates

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