1 Modeling Pricing in the Planning Process Ram M. Pendyala Department of Civil and Environmental Engineering University of South Florida, Tampa U.S. Department of Transportation Alexandria, VA; November 14-15, 2005 Expert Forum on Road Pricing and Travel Demand Modeling

2  Introduction and Motivation  Role of Travel Demand Modeling  Variety of Pricing Mechanisms  Road Pricing Projects: U.S. and Abroad  Pricing and Network Dynamics  Experiences with Toll Road Forecasting  Sources of Errors in Forecasts  Four/Five-Step Travel Demand Models Outline

3  Key Behavioral Processes Underlying Response to Pricing Policies  Advances in Travel Demand Modeling Methods and Paradigms  Conclusions and Future Directions Outline (continued)

4  Pricing and innovative toll strategies  Drivers pay marginal cost of travel – congestion and externalities  Travel demand management strategy  Reduce auto travel – mode & destination shifts  Suppress auto travel – eliminate or combine trips  Reduce peak period congestion – temporal shifts  Revenue generation  Invest in transport infrastructure improvements  Pay off debt  Desire for high volumes of paying users  Conflicting objectives? Introduction and Motivation

5  Sketch planning techniques  Elasticity methods  Peer city comparisons  Similar facility comparisons  Stated preference research  Estimates derived from stated preference data  Travel demand modeling systems  Variations of four-step travel demand modeling methods  Forecast patronage, traffic impacts, and revenue stream into future Planning Methods for Pricing Strategies

6  Traffic and travel demand impacts  VMT, VHT, travel time, delay, traffic volumes  Accessibility impacts  Revenue generation perspective  Patronage or volume of demand by time of day  Market penetration by payment type/technology  Short- and long-run demand elasticities  Social equity and environmental justice  Mobility, accessibility, and economic impacts by market segment (income, car ownership, gender, age, etc.) Pricing-Strategy Related Impacts

7  Public transport pricing systems  Parking pricing  Standard (flat) tolls  Shadow tolls  Area-Based/Distance-Based Congestion Charging  Variable/Dynamic/Value Pricing/Tolls: Facility-Based  HOT Lanes/FAIR Lanes  Credit-based congestion pricing Variety of Pricing Mechanisms

8  FHWA ’ s five types of value-pricing projects  A. Pricing on existing roads  B. Pricing on new lanes  C. Pricing on toll roads  D. Pricing of parking and vehicle use  E. Region-wide studies/initiatives  Several operational and others under study  Considerable international experience  Singapore: 25+ years of experience  Central London: 2-3 years of experience Road Pricing Projects: U.S. and Abroad

9  Optimizing traffic networks using pricing mechanisms  Minimal-revenue congestion pricing to induce system optimal performance  Dynamic traffic network simulation  Variety of electronic toll/pricing technologies  Mix of users changes over time  Modeling impacts of variable pricing requires explicit recognition of network dynamics Pricing and Network Dynamics

10  Several projects described in paper  SR 91 express lanes in California  San Diego I-15 congestion pricing project  Lee County (Florida) variable pricing project  Singapore congestion pricing implementation  Central London congestion charging scheme  All projects report various degrees of success  Decrease in traffic congestion, particularly in peak periods  Substantial patronage/usage of toll facilities Pricing Project Experiences

11  Toll road forecasts with traditional travel demand model systems  Minor variations to incorporate sensitivity to pricing  Analysis of toll road forecast accuracy  Toll road forecasts overestimated traffic by 20-30%  Review of 87 toll road projects: Average ratio of actual/forecast patronage is 0.76  Suggest presence of significant systematic optimism bias  Previous experience with toll facilities helps improve accuracy of forecasts Toll Road Forecasting Experience

12  Errors in socio-economic and land use forecasts that serve as inputs to model system  Errors in input assumptions including model coefficients, costs, rates, value of travel time  Errors in coding networks and node/link attributes by time-of-day  Errors in truck travel forecasts  Errors in estimate of ramp-up period  Errors in behavioral paradigms underlying travel demand forecasts Sources of Errors in Forecasts

13  In response to pricing …  Trips may be eliminated due to additional cost  New trips may be induced due to improved level-of-service  Traditional models unable to account for impacts of accessibility on trip generation (activity participation) Induced/Suppressed Travel

14  In response to pricing …  Trips may be combined/linked into chains/tours  Additional cost may induce desire for efficiency  Shifts in trip timing may lead to trip chain formation  Need to recognize inter-dependencies among trips in a chain (e.g., mode, destination) Trip Chaining and Tour Formation

15  Behavioral response to pricing strategies influenced by …  Spatio-temporal flexibility and constraints  Defining time-space prisms  Time allocation and time use behavior (activity episode duration)  Scheduling/timing of activities and trips  Time of day modeling along the continuous time axis Time-Space Geography

16  Traveler response to pricing strategies dependent on host of interactions  Interactions among household members – activity allocation and joint activity engagement behavior  Activity scheduling and re-scheduling behavior  Inter-dependencies among activities and trips in a complete activity-travel pattern  History dependency and inter-temporal relationships  In-home – out-of-home activity substitution and complementarity Agent-Based Interactions and Inter-dependencies

17  Primary impact on specific trip(s) subjected to pricing strategy  Interactions/inter-dependencies result in host of secondary/tertiary impacts  Complete activity-travel pattern subject to change as trips are …  rescheduled and chained  shifted in time, mode, destination, route  Impacts on other household members Secondary/Tertiary Impacts

18  Simulation of complete activity-travel patterns for each individual in population  Modeling at the level of the individual decision-maker  Represent behavioral decision-making processes  Capture differences (taste-variation) across individuals  Synthesize and evolve population over time  Reflect population dynamics  Ramp-up period represents evolutionary period of learning and adaptation Microsimulation Approaches

19  Pricing policies increasingly variable/ dynamic in nature  Travel times, costs, paths, and speed-flow patterns constantly updated  Dynamic traffic assignment algorithms to reflect network dynamics  Integrate with activity-based models  Appropriate feedback loops – network impacts on activity patterns Dynamic Traffic Assignment

20  Host of medium and longer term choices potentially impacted by pricing policies  Residential and work location choice  Vehicle ownership choice  Business location choice  Changes in property values and land accessibility  Evolution of urban system  Feedback between activity-travel demand model and land use simulation model Integrated Urban Systems and Activity-Travel Modeling

21  Heterogeneity in population attributes  Attitudes and perceptions towards pricing strategies  Preferences for and values attributed to alternative behavioral responses  Values of travel time savings and travel time reliability  Learning and adaptation strategies  Recent advances in econometric model formulation and estimation  Presence of heterogeneity in value of travel time savings proven Heterogeneity in Population Attributes

22  Attitudes and perceptions shape behavior (and vice-versa)  Nature and magnitude of response to pricing policy  Adaptation strategies adopted  New activity-travel pattern considered “ acceptable ” or “ satisfactory ” or “ optimal ”  Adoption of electronic toll collection technologies  Habitual vs. occasional use of tolled facility  Help inform model framework, behavioral paradigm, and model specification Role of Attitudes and Perceptions

23  Tour-based and activity-based microsimulation model systems  Advanced econometric model estimation methods  Reflect behavioral decision-making processes  Cause-and-effect relationships  Integrated modeling of land use – activity/travel demand – traffic network continuum with feedback  Long-term to short-term choices  Not necessarily unique to pricing policies – many other emerging behavioral, policy, technology, and environmental issues Towards a New Generation of Modeling Approaches

24  Unique nature of pricing schemes that amplify issues with models  Direct cost/monetary implications  Direct travel time/reliability implications  Direct infrastructure finance implications  Absence of incorporation of monetary constraints (expenditures vis- à -vis income)  Some decrease in VMT growth, but generally little (short-term) impact of fuel price rise Pricing Considerations

25  What should toll reflect/accomplish?  Value of travel time savings  Value of travel time reliability  Facility construction/maintenance costs  Congestion/externality costs (full cost pricing)  Network-wide ripple effects  Shifts to facility due to improved LOS  Shifts away from facility due to added cost  Shifts to improved toll-free facilities Pricing Considerations (continued)

26  Modify attribute of least impact first?  Route shift  Temporal shift  Trip chaining shifts  Destination shifts  Mode shifts  Activity (re)allocation  Activity participation (elimination/addition)  Auto ownership  Workplace/residential location  Implications for behavioral modeling Hierarchy of Behavioral Response?

27  Widespread interest in implementation of innovative pricing schemes/technology systems  Toll road forecasts coming under intense scrutiny  Determine contribution of various sources of error  Input data/assumptions/variable forecasts  Model specifications/parameters/variables  Behavioral paradigm/framework  Heterogeneity in traveler perceptions and values Key Opportunities

28  Several real-world projects offering data on observed behavior  Conduct longitudinal surveys of behavior in conjunction with ongoing projects  Test and validate advanced travel demand modeling methods  Controlled studies involving comparisons of forecasts offered by different modeling methods  Special experiments to understand behavioral adaptation, heterogeneity, and attitudes/perceptions Key Opportunities