Post on 18-Jan-2016
Modeling Drivers’ Route Choice Behavior, and Traffic Estimation and
Prediction
Byungkyu Brian Park, Ph.D.Center for Transportation Studies
University of Virginia
DriveSense14 Workshop, Norfolk, VA
Drivers’ Route Choice Model
• Existing literature– Considers disaggregate information but ends up
with an aggregate model • Can we consider a model for each driver?
– Seems feasible with connected vehicle and smart phones and driver’s opt-in
Traffic Estimation & Prediction
• Estimates existing network condition using probe vehicles
• Estimates origin destination matrices for next 15-30 minutes
• Predicts future traffic conditions by assigning the OD matrices
• Evaluates multiple operational strategies and recommends best strategy
Motivation
• Weather vs. Route Guidance
Connected Vehicle Technology
• Wireless communications among vehicles and infrastructure
Questions on the Route Guidance
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Will connected vehicle technology improve the quality of route guidance?
What happens if multiple route guidance strategies were implemented? Will they cancel-off benefits?
Route Guidance System• Assumptions
– Every equipped vehicle provides its origin-destination information (opt-in)
– No Communications Loss• Perfect communications V-2-I and V-2-V
– On-Board Equipment (OBE or OBU) Vehicles• Act as probe vehicles
Route Guidance System• Assumptions (cont’d)
– Guided Drivers• Time varying traffic assignment• A link-weighted K-Shortest Path algorithm to create
reasonable path alternatives• Time dependant minimum travel time path
– Unguided Drivers• Static assignment• Fixed shortest distance path
Microscopic Traffic Simulation Model - VISSIM
• Microscopic, Time-step
based simulation model• Simulate traffic operations
in urban streets and freeways• Emphasize multi-modal
transportations (Bus, LRT, Heavy
Rail, etc.)
OverviewOverview
Microscopic Traffic Simulation Model – VISSIM (cont’d)
Traffic Flow ModelTraffic Flow Model Signal Control ModelSignal Control Model
Microscopic Traffic Simulation Model – VISSIM (cont’d)
• Various measures of effectiveness
(e.g., delay, travel time, queue
length, etc.)
• 2D & 3D animations
OutputOutput
Route Guidance Strategies
Guidance Strat-egy
Acro-nym
Major Information from VII
Latest Travel time -based Guidance LTG The latest link travel time
Averaged Travel time-based Guidance ATG The average of link travel
times
Routing Travel time-based Guidance RTG
Individual vehicles’ travel times of directional movements
Predicted Travel time-based Guidance PTG
Individual vehicles’ origin- destination tables
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• Travel time of directional movements at an intersection
• Gathers all individual directional travel time through individual vehicles’ trajectory
Routing Travel time-based Guidance (RTG)
Predicted Travel Time-Based Guidance (PTG)
Simulationfor current state estimation
OD table Extraction
Simulationfor link travel time prediction
Converged?
PredictedLink Travel Time
Generation ofPrediction-based Guidance
• Based on DynaMIT program (i.e., Traffic Estimation and Prediction)
• Travel info (origin-destination) obtained from equipped vehicles
Route Guidance System Evaluation• Simulation Test-Bed
– Microscopic Traffic Simulator: VISSIM– A Hypothetical Urban Network
• 118 Road Segments including
- a freeway - a major arterial
• 21 Signalized Intersections• 9 All-Way-Stop Control• 25 Origin/Destination
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Experimental Design
• Experimental factors and levels
• Experimental setup– Single operation : Total 2175 simulation runs
and 1197 computer hours– Multiple operation : Total 150 simulation runs
and 93 computer hours– Made 5 replications for each simulation
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Benefits of CV-based guidance strategies
• Single operation of guidance strategies
• Multiple operation of guidance strategies
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Benefits of individual strategies• All guidance strategies produced
benefits – Single operation of guidance
strategies
– Multiple operation of guidance strategies
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Impact of Market Penetration RateVH
T (
Vehic
le-H
ours
)
gudanceMP
PTGRTGATGLTGBASE100703051007030510070305100703050
1100
1000
900
800
700
600
500
400
300
Boxplot of VHT
Proposed Research
• Bundle drivers’ route choice behavior model and traffic estimation & prediction system
• How? – Develop each driver’s route choice behavior
model and keep model parameters on his/her smartphone or cloud
– Implement driver’s route choice behavior model in TrEP
Where Are We?
• Just completed IRB training! • Developed survey questionnaire to
understand drivers’ characteristics and their stated preferences
• Evaluate drivers’ route choice behavior using driving simulator