A Toll Choice Probability Model Application to Examine Travel Demand at Express and Electronic Toll Lanes in

Maryland

14th TRB Planning Applications ConferenceMay 5-9, 2013

Columbus, Ohio

BySabyasachee Mishra (University of Memphis)Birat Pandey (Baltimore Metropolitan Council)

Timothy Welch (University of Maryland)Charles Baber (Baltimore Metropolitan Council)

Subrat Mahapatra (Maryland State Highway Administration)

Motivation

Enhance existing work Previous toll diversion models: all-or-nothing path choice

decision Disaggregate VOT in mode choice and traffic assignment Binary choice logit (probabilistic) model Analytical tool capable of producing detailed tolled

facility use Better decision support tool

Background

Ranked 19th in Population(5.8 million, 2010)

Ranked 5th in Population Density

By 2040, Maryland will have 1.1 million more people, and 0.4 million more jobs

Agencies Involved

State CountiesMD 24VA 19PA 9WV 8DE 3DC 1Total 64

Toll Facilities

Travel Model Structure

Regional Model Statewide Model

National/State/MPO Land Use Forecasts

SE Data Reconciliation

Trip Generation

Trip Distribution

Mode Choice

Trip Generation

Trip Distribution

Time of day split

Urban ModelReconciliation

Multiclass Assignment

Disaggregation

TrucksPerson Travel

Flow Estimation

EI/IE/EE tripsEI/IE/EE trips

II trips II trips

PersonLong-Distance Travel Model

NHTS FAF 3

Toll Choice Model Design

Trip Generation

Trip Distribution

Mode Choice

Traffic Assignment

MSTM Model Structure

Auto TripsModification

Toll Choice Calculation

Traffic Assignment

MSTM Toll Model Structure

Toll Share

Toll Share = 1/ (1 + eα*ΔT + β*Cost/ln(Inc) + c + etcbias) Where e = Base of natural logarithm (ln)

ΔT = time saving between toll road and non-toll road travel, in minutes

Cost = toll cost in dollars 

Inc = household annual income (in thousands)

α = time coefficient

β = cost coefficient

c = toll road bias constant

etcbias = bias towards selecting toll routes with ETC payment

Toll Probability Function by Trip Purposes

Scenarios

Two scenarios are also examined. 20% increase of 2030 50% increase of 2030

Comparison is presented in Toll trip origins Toll trip destinations Elasticity of income classes

Toll Trip OriginsScenario-I

Scenario-II

20%

In

crea

se

50%

Increase

Toll Trip DestinationsScenario-I

Scenario-II

20%

Incr

eas

e

50%

Increase

Demand Elasticity

Income Quintile

Volume Class Quartile Lower Lower-middle Middle Upper-middle Upper

Scenario I INC1 INC2 INC3 INC4 INC5< 5,000 0.572 0.511 0.534 0.501 0.4955,000 - 10,000 0.405 0.366 0.449 0.409 0.47910,000 - 20,000 0.628 0.564 0.430 0.591 0.625> 20,000 0.538 0.569 0.515 0.640 0.712Scenario II Lower Lower-middle Middle Upper-middle Upper< 5,000 0.490 0.451 0.445 0.458 0.4795,000 - 10,000 0.373 0.333 0.353 0.340 0.36410,000 - 20,000 0.568 0.511 0.409 0.524 0.544> 20,000 0.494 0.517 0.483 0.564 0.615

Summary

An enhancement over previous toll diversion models

The proposed model recognizes variations in traveler’s decision to utilize a toll road by incorporating a probabilistic model.

Estimated likely toll road users are assigned to assess the toll traffic as a path choice decision between toll road and non-tolled roads.

The estimated toll traffic on several toll facilities is slightly lower than observed higher sensitivity to toll cost

Thank You

Acknowledgement