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Dynamic Traffic Assignment Approaches for Statewide
Transportation Planning And Operations: Maryland CaseS. Erdogan1,
K. Patnam2, X. Zhou3, F.D. Ducca4, S. Mahapatra5, Z. Deng6, J.
Liu7
1, 4, 6 University of Maryland, National Center for Smart Growth
Research and Education2 AECOM, Arlington, VA3, 7Arizona State
University, School of Sustainable Engineering and the Built
Environment
15th TRB National Transportation Planning Applications
ConferenceMay 19, 2015, Atlantic City, New Jersey
1Time of day issuesCongestion, queue buildup and dissipationLong
distance trips which span multiple time periods Refined input for
sub-area / corridor analysisScenario analysis capability
2Motivation- Why Statewide DTA2Analytical Approach TRANSIMSVDF
for link MOEs
Simulation-based Approach DTALiteVDF, Point/Spatial-Queue, or
Newells Kinematic Wave Model for link MOEs3Two Methods
Given MSTM CUBE Model
Network:
MTSM 2007 network ~167,000 links, ~68,000 nodes, 1739 zones
Demand:
MSTM 2007 demand input by 6 purposes, 5 incomes categories, 4
TOD2007 HTS survey for diurnal distributions
Generate Nationwide and Subarea Models4Input Data4ANALYTIC
APPROACH:TRANSIMS55
6MSTM TRANSIMS Models
Level 2Statewide21,748 Nodes31,116 Links1,811 ZonesLevel
1Nationwide68,243 Nodes87,785 Links1,739 ZonesTRANSIMS Version 6
Router Applications6DemandNetworkSpeeds/Flows*Paths** =
OptionalDynamic Traffic RoutingAON Routing /En-route Diversion
PathsSpeeds/Flows+
Dynamic User EquilibriumIn-memory
iterationsConvergencePathsSpeeds/FlowsPaths+Speeds/Flows++ =
Converged67
7Validation Link Level
Freeway
Major Arterial
Minor Arterial
LocalI-95 S between MD-24 and MD-543
Volume (vph)I-95 S between MD-24 and MD-543Capital BeltwayInner
Loop between MD-295 and MD-450
Volume (vph)
Time-Dependent Performance MeasuresCongested SegmentChange in
Average Travel Time by Departure Time
10Simulation-based APPROACH:DTA-Lite 1111MSTM DTA-Lite Model
12
Dynamic traffic assignment:Calibrate model parameters e.g. flow
capacity, jam density (for congestion propagation)Traffic
count-based dynamic OD demand calibrationValidate to screenlines,
major corridors, ground counts by facility typeDaily link volume
DTALite-BPR vs. MSTM13Preliminary Daily Link Volume ComparisonDaily
link volume DTALite-PQM vs. MSTM
Visualization ExamplesBottleneckQueue Duration3D
Volume14HighlightsTight integration with CUBE Allow BPR function,
point/spatial queue and simplified kinematic wave model in traffic
simulationDirect interface for reading CUBE shape files and demand
fileTight integration with sensor dataBuilt-in OD demand
calibration model that accepts sensor density, flow, and speed
dataFuture workCalibration of road capacity and dynamic OD demand
matrixTolling scenarios for detour managementActivity based model +
DTA integration, more behavior-driven model for evaluating emerging
traffic management scenarios Automated evaluation tool for a large
number of road capacity improvement strategies Google Transit
capability to add transit capability
15THANKS!Q &A16For more
information:http://www.smartgrowth.umd.edu/
E-mail :[email protected] Fredrick W. Ducca, TPRG
[email protected] Sevgi Erdogan, Research Associate
Address:Suite 1112, Preinkert Field HouseUniversity of
MarylandCollege Park, Maryland 20742301.405.6788
16Time-Varying OD Demand/ Agent Data
Queue-based Traffic Simulation
Shortest Path
Time-Varying Link Travel Times
Path Selection
Link Traversal
Node Transfer
Path Processing
User Decisions
