Mesoscopic Simulation Evaluation of Dynamic Congestion Pricing Strategies for New York City Crossings

Congestion pricing charges motorists during peak hours to encourage them to either switch their travel times or to use alternative routes. In recent years, with the help of technological developments such as electronic toll collection systems, pricing can be conducted dynamically; tolls can be changed in real-time according to measured traffic conditions. Dynamic pricing is currently deployed as High Occupancy Toll (HOT) lanes; however the time-dependent pricing idea can be extended to a setting where drivers make route choices that are relatively more complex. In the case of New York City, many of the limited number of crossings to the island of Manhattan are tolled, and function as parallel alternatives. In this paper a simulation-based evaluation of dynamic congestion pricing on these crossings is conducted using a mesoscopic traffic simulation model with a simple step-wise tolling algorithm. One of the key aspects of this study is the estimation of realistic values of time (VOT) for different classes of users, namely, commuters and commercial vehicles. New York region-specific VOT for commercial vehicles is estimated using a logit model of stated preference data. The simulation results are analyzed to measure the change in volumes and toll revenues between potential dynamic pricing and static tolling currently in place.

[1]  K. Ozbay,et al.  Valuation of travel time and departure time choice in the presence of time-of-day pricing , 2008 .

[2]  K. Kawamura,et al.  Commercial Vehicle Value of Time and Perceived Benefit of Congestion Pricing , 1999 .

[3]  W. Vickrey.  Congestion Theory and Transport Investment , 1969 .

[4]  David M Levinson,et al.  Value of Time for Commercial Vehicle Operators , 2010 .

[5]  Hani S. Mahmassani,et al.  A bi-criterion dynamic user equilibrium traffic assignment model and solution algorithm for evaluating dynamic road pricing strategies , 2008 .

[6]  Praveen Edara,et al.  A real-time road pricing system: The case of a two-link parallel network , 2007, Comput. Oper. Res..

[7]  P.H.L. Bovy,et al.  Dynamic road pricing for optimizing network performance with heterogeneous users , 2005, Proceedings. 2005 IEEE Networking, Sensing and Control, 2005..

[8]  Mark W Burris,et al.  Examination of Houston's QuickRide Participants by Frequency of QuickRide Usage , 2004 .

[9]  Yafeng Yin,et al.  Dynamic Tolling Strategies for Managed Lanes , 2009 .

[10]  Carolyn Wolff,et al.  Congestion Pricing as a Traffic Management Tool: Evaluating the Impacts at New York City’s Interstate Crossings , 2007 .

[11]  Heng Wei,et al.  A Feedback-Based Dynamic Tolling Algorithm for High-Occupancy Toll Lane Operations , 2008 .

[12]  José Holguín-Veras,et al.  Evaluation Study of Port Authority of New York and New Jersey's Time of Day Pricing Initiative , 2005 .

[13]  Kaan Ozbay,et al.  Evaluation of Impacts of Time-of-Day Pricing Initiative on Car and Truck Traffic , 2006 .

[14]  Thierry Blayac,et al.  Value of travel time: a theoretical legitimization of some nonlinear representative utility in discrete choice models , 2001 .

[15]  P. Vilain,et al.  Value Pricing and Freight Traffic: Issues and Industry Constraints in Shifting from Peak to Off-Peak Movements , 2000 .

[16]  Byung-Wook Wie,et al.  Dynamic Stackelberg equilibrium congestion pricing , 2007 .

[17]  H. Mahmassani,et al.  Toll Pricing and Heterogeneous Users , 2005 .

[18]  R. Tobin,et al.  Dynamic congestion pricing models for general traffic networks , 1998 .

[19]  A. C. Pigou.  Economics of welfare , 1920 .

[20]  Kara M. Kockelman,et al.  Simulation-Based Heuristic Approach for Dynamic Marginal Cost Pricing , 2008 .