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Modeling Pricing in the Planning Process

Ram M. PendyalaDepartment of Civil and Environmental EngineeringUniversity of South Florida, Tampa

U.S. Department of TransportationAlexandria, VA; November 14-15, 2005

Expert Forum on Road Pricing and Travel Demand Modeling

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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

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Key Behavioral Processes Underlying Response to Pricing Policies

Advances in Travel Demand Modeling Methods and Paradigms

Conclusions and Future Directions

Outline (continued)

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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)

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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?

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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

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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