Travelers’ Response to Value Pricing: Application of Departure Time Choices to TRANSIMS
Publication: Journal of Transportation Engineering
Volume 136, Issue 9
Abstract
There is a lack of proper travel demand forecasting tools that can evaluate and determine the impacts of pricing on travelers’ decision. The current methods use aggregated and zonal-based approaches that lack the capability of tracing individual travelers through the supply network. Transportation analysis simulation system (TRANSIMS) has unique capabilities of accessing individual records such as socioeconomic and trip characteristics and tracing vehicle as well as individual traveler movements. Although TRANSIMS environment has been significantly improved over the past few years, there are issues that still need to be improved, including the pricing of a high-occupancy toll (HOT) lane and the rescheduling of activities in case a traveler chooses time choice versus route choice. This study extends the previous work on a HOT lane system by developing a departure time choice model. The proposed method is a post-processing of route choice and represents a sequential decision-making process of travelers who want to depart early or late based on congestion, individual attributes, and activity characteristics. The paper presents the results of a departure time choice model and its impacts on a HOT lane system using Portland, Oregon as a case study. The results show that 13.9% of households did change their departure time because of congestion and/or tolls.
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References
Bhat, C., and Steed, J. (2002). “A continuous-time model of departure time choice for urban shopping trips.” Transp. Res., Part B: Methodol., 36(3), 207–224.
Burris, M., and Pendyala, R. (2002). “Discrete choice models of traveler participation in differential time of day pricing programs.” Transp. Policy, 9(3), 241–251.
Cambridge Systematics (2005). “Forecasting person travel by time of day.” FHWA, U.S. Department of Transportation, Washington, D.C.
Daly, A., Hess, S., Polak, J. W., Hyman, G., and Rohr, C. (2005). “Modeling departure time and mode choice.” ERSA conference paper, European Regional Science Association, Amsterdam, The Netherlands.
De Palma, A., and Lindsey, R. (2004). “Congestion pricing with heterogeneous travelers: A general-equilibrium welfare analysis.” Netw. Spatial Econ., 4, 135–160.
Ettema, D., and Timmermans, H. (2003). “Modeling departure time choice in the context of activity scheduling behavior.” Transportation Research Record. 1831, Transportation Research Board, Washington, D.C., 39–46.
Ghosh, A. (2000). “Heterogeneity in value-of-time: Revealed and stated preference estimates from the I-15 congestion pricing project.” Working Paper, Dept. of Economics, Univ. of California, Irvine, Calif.
Hendrickson, C., and Plank, E. (1984). “The flexibility of departure times for work trips.” Transp. Res., Part A, 18(1), 25–36.
Jeihani, M., Sherali, H., and Hobeika, A. (2006). “Computing dynamic user equilibria for large-scale transportation networks.” Transportation, 33(6), 589–604.
Joh, C. -H., Doherty, S., and Polak, J. (2005). “Analysis of factors affecting the frequency and type of activity schedule modification.” Transportation Research Record. 1926, Transportation Research Board, Washington, D.C., 19–25.
Lee, K., and Hobeika, A. (2007). “Application of dynamic value pricing through enhancements to TRANSIMS.” Transportation Research Record. 2003, Transportation Research Board, Washington, D.C., 7–16.
Mahmassani, H., Caplice, C., and Walton, M. (1990). “Characteristics of urban commuter behavior: Switching propensity and use of information.” Transportation Research Record. 1285, Transportation Research Board, Washington, D.C., 57–69.
Mahmassani, H., and Liu, Y. (1999). “Dynamics of commuting decision behavior under advanced traveler information systems.” Transp. Res., Part C: Emerg. Technol., 7(2–3), 91–107.
Mannering, F. (1989). “Poisson analysis of commuter flexibility in changing routes and departure times.” Transp. Res., Part B: Methodol., 23(1), 53–60.
Saleh, W., and Farrell, S. (2005). “Implications of congestion charging for departure time choice: work and non-work schedule flexibility.” Transp. Res., Part A: Policy Pract., 39(7–9), 773–791.
Small, K. (1982). “The scheduling of consumer activities: Work trips.” Am. Econ. Rev., 72(3), 467–479.
Small, K., and Yan, J. (2001). “The value of ‘value pricing’ of roads: Second-best pricing and product differentiation.” J. Urban Econ., 49, 310–336.
Steimetz, S. C., and Brownstone, D. (2005). “Estimating commuters’ ‘value of time’ with noisy data: a multiple imputation approach.” Transp. Res., Part B: Methodol., 39, 865–889.
TRANSIMS Open Source. (2008). TRANSIMS, ⟨http://ttmip.fhwa.dot.gov/community/user_groups/transims⟩ (Oct. 28, 2008).
Wang, J. (1996). “Timing utility of daily activities and its impact on travel.” Transp. Res., Part A: Policy Pract., 30(3), 189–206.
Yamamoto, T., Fujii, S., Kitamura, R., and Yoshida, H. (2000). “Analysis of time allocation, departure time, and route choice behavior under congestion pricing.” Transportation Research Record. 1725, Transportation Research Board, Washington, D.C., 95–101.
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© 2010 ASCE.
History
Received: Dec 18, 2008
Accepted: Dec 14, 2009
Published online: Dec 28, 2009
Published in print: Sep 2010
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