Modeling Pricing in the Planning Process

1. Expert Forum on Road Pricing and Travel Demand Modeling 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

2. Outline • 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

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

4. Introduction and Motivation • 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?

5. Planning Methods for Pricing Strategies • 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

6. Pricing-Strategy Related Impacts • 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.)

7. Variety of Pricing Mechanisms • 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

8. Road Pricing Projects: U.S. and Abroad • 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

9. Pricing and Network Dynamics • 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

10. Pricing Project Experiences • 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

11. Toll Road Forecasting Experience • 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

12. Sources of Errors in Forecasts • 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

13. Induced/Suppressed Travel • 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)

14. Trip Chaining and Tour Formation • 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)

15. Time-Space Geography • 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

16. Agent-Based Interactions and Inter-dependencies • 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

17. Secondary/Tertiary Impacts • 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

18. Microsimulation Approaches • 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

19. Dynamic Traffic Assignment • 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

20. Integrated Urban Systems and Activity-Travel Modeling • 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

21. Heterogeneity in Population Attributes • 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

22. Role of Attitudes and Perceptions • 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

23. Towards a New Generation of Modeling Approaches • 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

24. Pricing Considerations • 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

25. Pricing Considerations (continued) • 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

26. Hierarchy of Behavioral Response? • 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

27. Key Opportunities • 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

28. Key Opportunities • 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