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Dynamic Traffic Assignment V: Realtime Systems, Consistency in Predictive Information

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Title: Dynamic Traffic Assignment V: Realtime Systems, Consistency in Predictive Information


1
Dynamic Traffic Assignment VReal-time Systems,
Consistency in Predictive Information
Haris N. Koutsopoulos Northeastern
University M.I.T. Summer Professional Program
1.10s Modeling and Simulation for Dynamic
Transportation Management Systems  July/August
2003
2
Outline
  • Introduction
  • Real-time Information Generation
  • DynaMIT
  • Case Studies
  • Conclusion

3
Introduction
  • Information media
  • TV - Radio
  • Internet
  • Variable Message Signs
  • Cellular phones
  • On-board equipment
  • Information mode
  • Prescriptive
  • Descriptive

4
ATIS Internet
Houston http//traffic.tamu.edu/traffic.html
5
ATIS Internet
Houston http//traffic.tamu.edu/traffic.html
6
ATIS Internet
Paris http//www.sytadin.tm.fr
7
Real-time Guidance and Traffic Information
Generation
  • Information Attributes
  • Reliable
  • Unbiased
  • Timely

8
Real-time Information Generation
  • Problem over-reaction

Broadcasting weather forecasts does NOT change
the weather
9
Solution
  • Traffic prediction incorporating
  • Future OD flows
  • Driver response to information
  • Consistent information
  • Result
  • Prevent over-reaction
  • Maintain system credibility

10
Consistency
Path Splitsp
N
B
Informationi
Link Impedancet
A
11
Consistency
  • Supply Mapping
  • Path splits Link Impedance
  • Dynamic Network Loading
  • Guidance Mapping
  • Link Impedance Information
  • ATIS
  • Behavior Mapping
  • Information Path Splits
  • Behavior Models

12
DynaMIT
Simulation-based
Dynamic traffic assignment system
With traffic prediction capabilities
  • Generating consistent travel information

13
DynaMIT
14
DynaMIT
  • Real-time applications DynaMIT-R
  • Short-term planning applications DynaMIT-P
  • Functionalities
  • Dynamic Traffic Assignment
  • Estimation and prediction of OD flows
  • Estimation of current traffic conditions
  • Prediction of future traffic conditions
  • Incorporation of the effect of traffic
    information
  • Fusion of historical information and real-time
    sensor data
  • Rolling horizon mode
  • Day-to-day learning and equilibrium models

15
Supporting Utilities
  • Calibration of model parameters
  • Output processing and analysis
  • Graphical User Interfaces
  • Output visualization
  • Network generation and editing

16
Dynamic Traffic Assignment Framework
  • Demand
  • Origin-destination flows
  • Micro simulator of travel choices
  • Supply
  • Mesoscopic Traffic simulator
  • Demand-Supply Interactions

ATIS ATMS
Demand Simulator
Supply Simulator
State Estimation And Prediction
17
Demand Simulator
  • Key Elements
  • Origin/Destination estimation and prediction
  • Disaggregation-Aggregation process
  • Travel behavior and response to information
    through disaggregate behavioral models
  • Departure time
  • Route choice

18
Inputs
Inputs
List of drivers
Control
Guidance
Incidents
Supply Simulator
Mesoscopic
Simulator
En-route
Speed density realtionships Individual vehicles
Demand
Simulation
Congestion
Queues
Spillbacks
Network Conditions
Flows
Queues
Travel Times
Speeds
Densities
Assignment Matrix
etc.
19
DynaMIT Overall Framework
20
Demand-Supply Interactions
  • Fixed point problem
  • Find x f(x) x
  • Algorithms
  • Variations of
  • xk1 g(xk, f(xk))

21
DynaMIT State Estimation
22
OD Estimation
OD Estimator
  • x estimated OD flows
  • xH historical (seed) OD flows
  • A assignment matrix
  • Ax estimated counts
  • y observed sensor counts
  • F1, F2 metric

Assignment Matrix A Maps OD flows to sensor
counts. Proportion of demand in period i for a
particular OD contributing to counts in period j
23
Assignment Matrix
  • Depends on
  • Path travel times
  • Path choice probabilities

Traffic
Historical
Counts
Database
  • No congestion
  • Constant
  • Congestion
  • Unknown ? iterative solution

OD Estimation
OD Flows
24
Assignment Matrix
25
DynaMIT Predictive Information Generation
26
(No Transcript)
27
OD Prediction
  • Predict O-D flows for intervals (h1,h2,)
  • Deviations, Devh1
  • Preserve historical information and structure
  • Autoregressive process
  • Devh1 ?0Devh ?1Devh-1 ?2Devh-2.
  • Prediction
  • ODh1Adjusted_Historical_ODh1 Devh1

28
Rolling Horizon Operation
29
System Design
  • modular
  • expandable

30
Applications
  • Generation and provision of information to
    travelers
  • Generation and evaluation of incident response
    strategies/diversions
  • Provision of information to TMC
  • Generation of emergency response

31
Case Studies
  • Irvine, CA
  • Central Artery, Boston
  • New York
  • Switzerland
  • Los Angeles
  • Hampton Road, Virginia

32
Case Study The Irvine Network
  • Network
  • Three major freeways
  • Dense arterial network
  • 298 nodes, 618 links, 1372 segments, 655 OD pairs
  • Ramp metering
  • Actuated signal control
  • Data
  • Static planning OD matrices
  • 5 days of counts, speeds from 68 sensors
  • 30-second data on freeway links
  • 5-minute data on arterial links
  • Scope
  • Evaluation of estimation and predictive
    capabilities

33
Primary OD Pairs
34
Evaluation Steps
  • Calibration
  • 3 days of data
  • Validation
  • Remaining 2 days of data

35
Sensor Data Variability
36
Calibration
  • Supply Module
  • Demand Module

37
Supply Calibration
  • Speed-density relationships
  • Lane group capacities
  • Intersection capacities
  • Highway Capacity Manual
  • Approximation of actuated strategies

38
Demand Calibration
  • Route choice model parameters
  • Travel time
  • Freeway bias
  • O-D Estimation and Prediction

39
Calibration Framework
  • Sources of Errors
  • OD matrix
  • Route choice
  • Traffic dynamics
  • Aggregate data
  • Flows, speeds
  • Objective Function
  • Measure of the difference between observed and
    simulated values

40
Calibration Iterative Process
41
Path Choice Set
  • Generation of reasonable paths for each OD pair
  • Criteria
  • Shortest paths
  • Connectivity
  • Random perturbation of travel times

42
Calibration Results
  • 15 min estimation interval
  • Speeds (mph)

43
Calibration results, contd
  • 15 minute estimation interval
  • Counts (veh/15 min)

44
Validation
  • Same sensor locations, different days (2 days)
  • 15 minute estimation, 30 minutes prediction
  • 5 minutes estimation, 60 minutes prediction

45
Validation Results
  • 15 minute estimation
  • 30 minutes prediction

46
State Estimation Results
  • 15 minute estimation interval
  • Counts (veh/15 min)

47
State Estimation Results
  • 15 minute estimation interval
  • Counts (veh/15 min), freeway sensor location

48
State Estimation Results
  • 15 minute estimation interval
  • Counts (veh/15 min), ramp sensor location

49
State Estimation Results
  • 15 minute estimation interval
  • Counts (veh/15 min), arterial sensor location

50
Prediction Results
  • OD flows divided into classes
  • High, medium and low
  • Autoregressive process of degree 4 for each
    class
  • Results
  • Estimation from 715 to 730 am
  • Prediction from 730 to 800 am

51
Prediction Results
  • 15 minute estimation interval, counts (veh/15 min)

52
Validation Results
  • 5 minute estimation
  • 60 minutes prediction

53
Estimation Results
  • 5 minute estimation interval, counts (veh/5 min)

54
Prediction Results
  • Estimation from 725 to 730 am
  • Prediction from 730 to 830 am
  • Deviations Devh
  • Autoregressive process of degree 4
  • Devh1 ?0Devh ?1Devh-1 ?2Devh-2.
  • Prediction
  • ODh1Adjusted_Historical_ODh1 Devh1

55
Prediction Results
  • 5 minute estimation interval, counts (veh/5
    min)
  • estimation 725-730, prediction 730 to 830

56
Impact of Information Results
  • 715-815
  • Incident
  • 725 to 745
  • 45 capacity reduction

57
Impact of Information Results
  • Sample of alternative paths
  • Path Choice Set Generation
  • Shortest paths
  • Link elimination
  • Random perturbation

58
Impact of Information Results
  • With predictive information
  • Main O-D pair
  • 18 reduction in travel time
  • All O-D pairs with paths using the incident link
  • 17 reduction in travel time

59
Impact of Information Results
Without information
With predictive information
60
Case Study Central Artery
  • Impact of predictive information
  • informed drivers

61
Impact of Predictive Guidance
without guidance
with guidance
62
Sensitivity Analysis
  • Prediction Horizon 20 min

63
Case Study TRANSMIT
  • Network of detectors (EZ-PASS)
  • Lower Westchester, NY
  • Scope
  • Diversion strategies for an incident management
    system
  • Network
  • 1500 links
  • 850 nodes
  • 24 TRANSMIT sensors
  • High market penetration
  • High demand

64
TRANSMIT Data
65
Advantages of TRANSMIT Data
  • Better observability of the network ? state
    estimation
  • Direct observations
  • Sub-path flows
  • Point to point travel times (? reliable speeds)
  • Route choice fractions
  • Potential for lower measurement errors

66
Role of DynaMIT
  • Diversion strategies
  • Generation
  • Evaluation
  • Refinement
  • Guidance
  • Generation of messages
  • Dissemination to drivers

67
Approach
  • State estimation and prediction
  • Incorporate additional information
  • Traffic data fusion
  • Loop detectors
  • TRANSMIT sensors

68
Impact of Additional Information
69
Impact of Additional Information
70
Case study Switzerland
  • Large intercity network
  • 1661 links
  • 2177 segments
  • 912 nodes
  • 206 sensors
  • 11 088 OD pairs
  • 91 226 paths
  • Free-flow TT 1.5h-2h

71
Scope
  • Pre-defined strategies
  • Real-time decision-aid tools

72
Los Angeles
  • Support an incident management system
  • Evaluate DynaMIT with different prediction,
    estimation, and rolling horizon intervals
  • Use DynaMIT for
  • Base case conditions
  • Incident impacts

73
Hampton Road, Virginia
  • Calibration
  • Off-line validation
  • Off-peak
  • Peak
  • Incident
  • On-line validation

74
Conclusion
  • Methodological issues
  • Process real-time data
  • Importance of behavior
  • Solutions (DynaMIT)
  • Estimation
  • Prediction
  • Predictive, consistent guidance
  • Case Studies
  • Validation
  • Diversion strategies
  • Real time
  • Off-line
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