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

2. 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

3. Background Ranked 19 th in Population (5.8 million, 2010) Ranked 5 th in Population Density By 2040, Maryland will have 1.1 million more people, and 0.4 million more jobs

4. Agencies Involved StateCounties MD24 VA19 PA9 WV8 DE3 DC1 Total64

5. Toll Facilities

6. 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 Model Reconciliation Multiclass Assignment Disaggregation Trucks Person Travel Flow Estimation EI/IE/EE trips II trips Person Long-Distance Travel Model NHTS FAF 3

7. Toll Choice Model Design

8. Toll Share Toll Share = 1/ (1 + e α*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

9. Toll Probability Function by Trip Purposes

10. 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

11. Toll Trip Origins Scenario-I Scenario-II 20% Increase 50% Increase

12. Toll Trip Destinations Scenario-I Scenario-II 20% Increase 50% Increase

13. Demand Elasticity Income Quintile Volume Class QuartileLowerLower-middleMiddleUpper-middleUpper Scenario IINC1INC2INC3INC4INC5 < 5,0000.5720.5110.5340.5010.495 5,000 - 10,0000.4050.3660.4490.4090.479 10,000 - 20,0000.6280.5640.4300.5910.625 > 20,0000.5380.5690.5150.6400.712 Scenario IILowerLower-middleMiddleUpper-middleUpper < 5,0000.4900.4510.4450.4580.479 5,000 - 10,0000.3730.3330.3530.3400.364 10,000 - 20,0000.5680.5110.4090.5240.544 > 20,0000.4940.5170.4830.5640.615

14. 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

15. Thank You Acknowledgement