Transcript BLUE RIBBON PANEL FOR THE FUTURE DIRECTION OF

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
1
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
2
Outline



(continued)
Key Behavioral Processes Underlying
Response to Pricing Policies
Advances in Travel Demand Modeling
Methods and Paradigms
Conclusions and Future Directions
3
Introduction and Motivation

Pricing and innovative toll strategies


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




Drivers pay marginal cost of travel – congestion and externalities
Invest in transport infrastructure improvements
Pay off debt
Desire for high volumes of paying users
Conflicting objectives?
4
Planning Methods for Pricing
Strategies

Sketch planning techniques




Stated preference research


Elasticity methods
Peer city comparisons
Similar facility comparisons
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
5
Pricing-Strategy Related Impacts

Traffic and travel demand impacts



Revenue generation perspective




VMT, VHT, travel time, delay, traffic volumes
Accessibility impacts
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.)
6
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
7
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
8
Pricing and Network Dynamics

Optimizing traffic networks using pricing
mechanisms



Variety of electronic toll/pricing technologies


Minimal-revenue congestion pricing to induce system
optimal performance
Dynamic traffic network simulation
Mix of users changes over time
Modeling impacts of variable pricing requires
explicit recognition of network dynamics
9
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
10
Toll Road Forecasting Experience

Toll road forecasts with traditional travel demand
model systems


Analysis of toll road forecast accuracy




Minor variations to incorporate sensitivity to pricing
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
11
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
12
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)
13
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)
14
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
15
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
16
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
17
Microsimulation Approaches

Simulation of complete activity-travel patterns for
each individual in population




Synthesize and evolve population over time


Modeling at the level of the individual decision-maker
Represent behavioral decision-making processes
Capture differences (taste-variation) across individuals
Reflect population dynamics
Ramp-up period represents evolutionary period of
learning and adaptation
18
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
19
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
20
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
21
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
22
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


Integrated modeling of land use – activity/travel demand –
traffic network continuum with feedback


Cause-and-effect relationships
Long-term to short-term choices
Not necessarily unique to pricing policies – many other
emerging behavioral, policy, technology, and environmental
issues
23
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
24
Pricing Considerations

What should toll reflect/accomplish?





(continued)
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
25
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
26
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
27
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
28