National Academy of Sciences Irvine - April 8 , 2010

1. SHRP 2 Project L04Incorporating Reliability Performance Measures in Operations and Planning Modeling ToolsReliability Technical Coordinating CommitteeBriefing in partnership with & National Academy of Sciences Irvine - April 8, 2010

2. Agenda • Project Overview – Methodology • Data and Candidate Networks • Anticipated products of the research • Work Program Discussion

3. Methodology Framework: Three Components to Incorporate Reliability in Network Simulation Models

4. Mode, Departure Time and Route Choice Traffic Assignment Stochastic Network Simulation Model Reliability Measures Integration in Planning Models • Reliability-sensitive network equilibrium models • Reliability affects traveler’s mode, departure time and route choice. • Reliability measures are produced from the simulation models and fed back to the demand models. • Iterate between demand models and network simulation until convergence to UE (or SUE). • Output performance measures for policy evaluation and network planning/design.

5. Model Exogenous Sources: Scenario Manager • Scenario-based approach • Construct discrete scenarios • Conduct single-point estimation to produce results for each “what if” scenario • Monte Carlo sampling • Randomize demand and/or supply side parameters and establish the corresponding probability distribution functions. • Conduct Monte Carlo simulation with regard to these random parameters • Scenarios involving equilibrium traffic assignment • Perform iterative equilibrium assignment for scenarios involving medium to long term changes in demand or capacity

6. Value of time distribution Value of reliability distribution Traffic simulation model Value of time of driver n Value of reliability of driver n Travel time, toll and reliability information Least generalized cost path for driver n Model Endogenous Sources:Route Choice Behavior • Route choice behavior and travel time reliability interact • Reliability is a result of travel decisions • Reliability affects route choice behavior • Reliability in generalized cost function • Heterogeneity in route choice behavior

7. Model Endogenous Sources:Heterogeneity in Driving Behavior • Microscopic simulation models • Vehicle-related parameters, e.g. length, maximum acceleration/ deceleration, reaction time, safety distance, desired speed, desired acceleration/deceleration, Maximum give-way time • Link-related characteristics, e.g. speed limits, visibility distance at junctions, maximum turning speed, slope (grade), reaction time variation • Heterogeneity in car-following and lane-changing behavior, especially in the presence of heavy vehicles • Mesoscopic simulation models • Heterogeneity in vehicle types • Varying and context-dependent impact on traffic performance

8. t=t+1 Stochastic network simulation model Prevailing flow rate (q) Probability of flow breakdown p(q) Random number generator r r < p(q)? No Yes Flow breakdown in the next time interval Flow sustain in the next time interval Hazard model Breakdown duration Model Endogenous Sources:Flow Breakdown and Incidents • Characterize flow breakdown as a collective phenomenon • Probability of breakdown • Breakdown duration • Characterize flow breakdown and incident through individual decisions • Describe driver behavior under extreme and incident conditions

9. Model Endogenous Sources:State-Dependent Traffic Control • State-dependent traffic controls - dynamically adjust the control variables based on the prevailing (or predicted) traffic conditions, for more effective management. • State-dependent controls may introduce another source of unreliability/unpredictability to the system. • Actuated signal control • Ramp metering • Variable message signs • Dynamic pricing

10. Vehicle Trajectories: Unifying Framework for Micro and Meso Simulation • Vehicle (particle) trajectories in the output of a simulation model enable • construction of the path and O-D level travel time distributions of interest • extraction of link level distributions • Vehicle trajectories could be obtained from both micro- and meso-level simulation models • Trajectories also obtained from direct measurement in actual networks, enabling consistent theoretical development in connection with empirical validation.

11. Vehicle trajectories Preferred arrival time Travel time by lane, link, path and trip (O-D) Experienced vehicle travel time and actual departure time Travel time distribution Performance indicators: • Travel time variance • 95th percentile travel time • Buffer index • Planning time index • Frequency that congestion exceeds some threshold User-centric measures: • Probability of on time arrival • Schedule delay • Volatility Vehicle Trajectory Processor

12. Modeling Platform Requirements – Model Types & Roles

13. Planning Model Requirements • Ability of planning model to use quantitative measures of travel time variability in demand forecasting processes (i.e., beyond the common practice of using average travel time and cost) • expected travel time • schedule delay • travel time standard deviation (inferred vs experienced) • Ability to achieve at least some consistency between simulation-generated reliability measures and those used in mode / route / departure time choice models • Preference for activity-based planning models in order to incorporate schedule delay and other micro-level, reliability-related measures

14. Operations (simulation) Model Requirements • Ability to address most typical urban/suburban type of traffic conditions • vehicle/particle-based computational approach & fidelity • uninterrupted and interrupted flow with various types of facilities (incl. managed lanes) and control (signalized, stop/yield, etc.) • multi-vehicle classes (auto, truck, bus), preferably with varying characteristics • multi-simulation periods • Ability of underlying submodels (route choice, lane choice, etc.) to “endogenize” certain variability sources * • route choice and driver behavior heterogeneity • incident and flow breakdown characteristics • state-dependent traffic control • Ability to generate vehicle/particle-based trajectories *may require open-source models or access to code

15. Software Code Access / Modification Requirements • Ability to access / tweak programming code for endogenizing time variability sources / factors • some software developers would be keen to assist (depending on level of effort involved) • Open source sub-models (e.g., NGSim-developed lane change and other models) • already available in some software packages (Dynasmart, Aimsun, Vissim) • Various forms of intervention through programming tools (API) • available for most commonly used simulation platforms in North America(Paramics, Vissim, Aimsun, Transmodeler, Dynasmart, Dynameq, Vista, etc.)

16. Data Requirements • Traffic data for model adaptation / re-validation • Ancillary data for parameterization of time variability sources (endogenous & exogenous)e.g., special events, incidents, weather … • Travel time data for • reliability analysis / concept confirmation • model output verification / checking

17. Travel Time Data • Trajectory-based by vehicle trip(X, Y coordinates and time stamp) • Capturing both recurring and non-recurring congestion on a range of road facilities (from freeways to arterial roads and possibly managed lanes) • Sufficient sampling and time-series to allow statistically meaningful analysis • Ability to tie travel time data to “ancillary data” for time variability sources (to allow parameterization for simulation testing purposes)

18. Potential Data Sources / Inquiries made to date GPS- and Cell-probe data provide most promising prospects for large scale spatial and temporal coverage • INRIX (national) • NAVTEQ (national) • MyGistics (Chicago region) • Google (national) -no response • ITIS and FCD for validation (Missouri) • Calmar truck data (California, New York, etc.) • Intellione (Toronto) -prelim. tests undertaken • major navigation services provider -prelim. tests undertaken

19. Preliminary Data Tests to date

20. Demo Site Selection Considerations • large urban/suburban area • typical congestion-related travel time variability characteristics • existing models that meet L04 technical approach / simulation functional requirements • network size /configuration for meaningful measurement of time variability • vehicle trajectories / time distributions • data availability • primarily trajectory travel times • other considerations • willingness of jurisdictional authority to participate in the project and/or provide data and base model • familiarity of research team staff with candidate network, data and model…

21. Potential Sites - (best candidates so far noted with *) • Atlanta(trajectory data availability concerns) • Baltimore - Washington DC area • California (San Francisco / Bay Area) • Chicago(cost considerations may be prohibitive) • New York City / Metro Area ** (most model requirements already met, wide-area GPS data from various sources) • Toronto *(most models already in place or close to completion, wide-area GPS & cell probe data) • Montreal(models in place, GPS data can be arranged, institutional/jurisdictional concerns) • other areas (Seattle, Phoenix, Detroit, Austin)

22. Project Products • Reports • Phase I reviews in detail fundamental approach, includes supporting data, candidate networks, reliability measures • Phase II reports the results of model calibration and validation, includes guidelines and materials for full replication of phase II • Phase III report incorporates reliability into travel models • Outreach • Pilot demonstrations of the simulation model • Brochure, website, “how to” CD • Information sessions and demonstrations • Visualization tools

23. Product Audience for SHRP 2 L04 • Practitioners and researchers • Software vendors and developers • Operations managers, planners in transportation agencies interested in practical implications