American Journal of Traffic and Transportation Engineering

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Determining Optimal Tolling Rates for Border Regions Using Dynamic Modeling Methods

Received: May 21, 2019    Accepted: Jul. 23, 2019    Published: Aug. 14, 2019
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Abstract

Many toll facilities have been faced with traffic shortfalls due to inaccurate and over-forecasted toll revenue projections. Therefore calculating optimal toll rates can be a difficult process. Toll rates are often set to reflect the revenue needed to pay back bonds issued to finance the roadway. This research provides an alternative approach to calculating toll rates where revenue can be maximized while still considering the socio-demographics of the region. Several different approaches used in the border region were explored and compared to field data on an existing toll facility in El Paso, Texas. An innovative simulation-based modeling approach was used to test both static and dynamic pricing algorithms. Static tolling results showed optimal toll rates of $0.14/mile and $0.08/mile for Border Highway West in the westbound and eastbound directions respectively. The Cesar Chavez Highway has optimal toll rates of $0.12 and $0.10/mile in the west and eastbound directions. The dynamic tolling approach showed a max toll rate of $1.56/mile for Cesar Chavez Highway (westbound) during the morning peak period and then incrementally decreased to the minimum toll rate. However, the eastbound direction never increased above the minimum toll rate of $0.08 mile. Border Highway West never increased above the minimum toll rate in either direction. The dynamic tolling algorithm prediction is more representative of the optimal tolling rates for the border region-with the exception of Cesar Chavez Highway westbound.

DOI 10.11648/j.ajtte.20190404.12
Published in American Journal of Traffic and Transportation Engineering ( Volume 4, Issue 4, July 2019 )
Page(s) 118-131
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Dynamic Traffic Assignment, Toll Revenue Forecasting, Optimal Toll Rates, Value of Time

References
[1] Klein, D., “The Voluntary Provision of Public Goods? The Turnpike Companies of Early America,” Economic Inquiry 28, pp 788 -794, October 1990.
[2] Wardman, M., G. Whelan, B. Vaughan, P. Murphy, G. Hyman, Modeling the Demand for Toll Roads in the UK, Proceedings of the European Transport Conference, Leiden, The Netherlands, Oct 17-19, 2007.
[3] Parkany, E., “Environmental Justice Issues Related to Transponder Ownership and Road Pricing,” Journal of the Transportation Research Record, Vol 1932, pp 97-108, Washington DC, 2005.
[4] Bain, R., Credit Risk Analysis-Toll Road Traffic & Revenue Forecasts: An Interpreter’s Guide, First Edition, Publicaciones Digitales SA, Seville, 2009. ISSB: 978-0-9561527-1-8.
[5] Florida Department of Transportation, Revenue Forecasting Guidebook, July 2018. https://fdotwww.blob.core.windows.net/sitefinity/docs/default-source/content/planning/revenueforecast/revenue-forecasting-guidebook.pdf?sfvrsn=b40e9ddc_0.
[6] Li, Zheng, and David A. Hensher, “Toll Roads in Australia: An Overview of Characteristics and Accuracy of Demand Forecasts,” Transport Reviews 30 541-569, 2010.
[7] Li, Zheng, and David A. Hensher, “Estimating Values of Travel Time Savings for Toll Roads: Avoiding a Common Error,” Journal of Transport Policy, Vol 24 1-10, 2012.
[8] Flyvbjerg, B., M. K. S. Holm, and S. Buhl, “Inaccuracy in Traffic Forecasts,” Transport Reviews 26 (1) 1-24, 2006.
[9] National Cooperative Highway Research Program, “Estimating Toll Road Demand and Revenue: A Synthesis of Highway Practice: Synthesis 364,” Transportation Research Board, Washington DC, pp 19-35, 2006.
[10] Vassallo, J. M., A. Sánchez, “Subordinated Public Participation Loans for Financing Toll Highway Concessions in Spain,” Transportation Research Record 1996, Transportation Research Board, Washington, D. C., pp 1-8, 2007.
[11] Alasad, R., Dynamic Modelling of Demand Risk in PPP Infrastructure Projects: The Case of Toll Roads, Dissertation: Heriot-Watt University, School of Energy, Geoscience, Infrastructure & Society; August 2015.
[12] Bull, M., A. Mauchan, and L. Wilson, Toll-Road PPPs: Identifying, Mitigating and Managing Traffic Risk. Washington DC: homepage on Public Private Infrastructure Advisory Facility and the Global Infrastructure Facility (PPIAP), 2017. [Online]. Available: https://ppiaf.org/documents/5348?ref_site=ppiaf.
[13] Hensher, D., and P. Goodwin, “Using Values of Travel Time Savings for Toll Roads: Avoiding Some Common Errors,” Journal of Transport Policy, Vol 11 171-181, 2004.
[14] Joksimovic, D., Bliemler, M. and Bovy, P. Optimal toll design problem in dynamic traffic networks with joint route and departure time choice. Proceedings of the 84th Annual Meeting of the Transportation Research Board, Washington, DC. 2005.
[15] Chiu, Y-C, J. Bottom, M. Mahut, A. Paz, R. Balakrishna, T. Waller, and J. Hicks, A Primer for Dynamic Traffic Assignment. Transportation Research Board, 2010.
[16] Sloboden, J., V. Alexiadis, Y-C. Chiu, and E. Nava, Traffic Analysis Toolbox Volume XIV: Guidebook on the Utilization of Dynamic Traffic Assignment. Washington, D. C.: Federal Highway Administration, 2012.
[17] Ben-Akiva, M., M. Bierlaire, H. Koutsopoulos, and R. Mishalani, DynaMIT: A Simulation-Based System for Traffic Prediction, DACCORD Short Term Forecasting Workshop, Delft, The Netherlands, Massachusetts Institute of Technology, Intelligent Transportation Systems Program, February 1998.
[18] Zhang, Y., B. Atasoy, M. Ben-Akiva, Calibration and Optimization for Adaptive Toll Pricing, Transportation Research Board 97th Annual Meeting, 2018, Annual Compendium of Papers, Washington DC. URL: https://trid.trb.org/view/1496996.
[19] Wang, S., B. Atasoy, M. Ben-Akiva, Real-Time Toll Optimization Based on Predicted Traffic Conditions, Thesis: Masters of Science in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016. URL: https://dspace.mit.edu/handle/1721.1/104321.
[20] Li, W., D. Cheng, R. Bian, S. Ishak and O. A. Osman, "Accounting for travel time reliability, trip purpose and departure time choice in an agent-based dynamic toll pricing approach, " in IET Intelligent Transport Systems, vol. 12, no. 1, pp. 58-65, 2 2018. DOI: 10. 1049/iet-its.2017.0004. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8267183&isnumber=8267173.
[21] Hensher, D., and J. Rose, Tollroads are only part of the overall trip: The error of our ways in past willingness to pay studies, Springer Science+Business Media 819-837, 2013.
[22] Hensher, D., C. Ho, and W. Liu, “How Much is Too Much for Tolled Users: Toll Saturation and the Implications for Car Commuting Value of Travel Time Savings,” Transportation Research Part A: Transportation Research Board 601-624, 2016.
[23] Daniels, G., W. Stockton, R. Hundley, “Estimating Road User Costs Associated with Highway Construction Projects: Simplified Method,” Transportation Research Record, Journal of the Transportation Research Board, 2000. 1732: P 70—79.
[24] Wardman, M., “A Review of British Evidence on Time and Service Quality Valuations,” Transportation Research Part E: Logistics and Transportation Review, Vol 37, Issues 2-3, pp 107-128, April-July 2001. ISSN 1366-5545, DOI: 10.1016/S1366-5545 (00) 00012-0.
[25] Santos, A., N. McGuckin, H. Nakamoto, D. Gray, S. Liss, Summary of Travel Trends: 2009 National Household Travel Survey, US Department of Transportation, Federal Highway Administration, Report No. FHWA-PL-11-022, 2009.
[26] National Household Travel Survey: Our Nation’s Travel. Oakridge National Labs, 2009. https://nhts.ornl.gov/tools/pt.shtml.
[27] Camino Real Regional Mobility Authority, 2019. https://www.crrma.org/.
[28] Beaty, C., M. Burris, and T. Geiselbrecht, Toll Roads, Toll Rates and Driver Behavior, FHWA/TX-13/0-6737-1, Editor 2012, Texas A&M Transportation Institute: College Station.
[29] Texas Transportation Commission, Meeting Minutes of Public Hearing, Item 8: Toll Roads, Section (a): El Paso County. Austin, Texas, July 17, 2017. URL: https://ftp.dot.tx.us/pub/txdot/commission/2017/0727/minutets.pdf.
[30] Cirillo, C., J. B. Vicente, Evaluating Equity Issues for Managed Lanes” Methods for Analysis and Empirical Results, Final Report, March 2019, US Department of Transportation, Federal Highway Administration, Report No. 69A43551747123. URL: https://rosap.ntl.bts.gov/view/dot/41714.
Cite This Article
  • APA Style

    Jeffrey Shelton, Peter Martin. (2019). Determining Optimal Tolling Rates for Border Regions Using Dynamic Modeling Methods. American Journal of Traffic and Transportation Engineering, 4(4), 118-131. https://doi.org/10.11648/j.ajtte.20190404.12

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    ACS Style

    Jeffrey Shelton; Peter Martin. Determining Optimal Tolling Rates for Border Regions Using Dynamic Modeling Methods. Am. J. Traffic Transp. Eng. 2019, 4(4), 118-131. doi: 10.11648/j.ajtte.20190404.12

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    AMA Style

    Jeffrey Shelton, Peter Martin. Determining Optimal Tolling Rates for Border Regions Using Dynamic Modeling Methods. Am J Traffic Transp Eng. 2019;4(4):118-131. doi: 10.11648/j.ajtte.20190404.12

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  • @article{10.11648/j.ajtte.20190404.12,
      author = {Jeffrey Shelton and Peter Martin},
      title = {Determining Optimal Tolling Rates for Border Regions Using Dynamic Modeling Methods},
      journal = {American Journal of Traffic and Transportation Engineering},
      volume = {4},
      number = {4},
      pages = {118-131},
      doi = {10.11648/j.ajtte.20190404.12},
      url = {https://doi.org/10.11648/j.ajtte.20190404.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtte.20190404.12},
      abstract = {Many toll facilities have been faced with traffic shortfalls due to inaccurate and over-forecasted toll revenue projections. Therefore calculating optimal toll rates can be a difficult process. Toll rates are often set to reflect the revenue needed to pay back bonds issued to finance the roadway. This research provides an alternative approach to calculating toll rates where revenue can be maximized while still considering the socio-demographics of the region. Several different approaches used in the border region were explored and compared to field data on an existing toll facility in El Paso, Texas. An innovative simulation-based modeling approach was used to test both static and dynamic pricing algorithms. Static tolling results showed optimal toll rates of $0.14/mile and $0.08/mile for Border Highway West in the westbound and eastbound directions respectively. The Cesar Chavez Highway has optimal toll rates of $0.12 and $0.10/mile in the west and eastbound directions. The dynamic tolling approach showed a max toll rate of $1.56/mile for Cesar Chavez Highway (westbound) during the morning peak period and then incrementally decreased to the minimum toll rate. However, the eastbound direction never increased above the minimum toll rate of $0.08 mile. Border Highway West never increased above the minimum toll rate in either direction. The dynamic tolling algorithm prediction is more representative of the optimal tolling rates for the border region-with the exception of Cesar Chavez Highway westbound.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Determining Optimal Tolling Rates for Border Regions Using Dynamic Modeling Methods
    AU  - Jeffrey Shelton
    AU  - Peter Martin
    Y1  - 2019/08/14
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    DO  - 10.11648/j.ajtte.20190404.12
    T2  - American Journal of Traffic and Transportation Engineering
    JF  - American Journal of Traffic and Transportation Engineering
    JO  - American Journal of Traffic and Transportation Engineering
    SP  - 118
    EP  - 131
    PB  - Science Publishing Group
    SN  - 2578-8604
    UR  - https://doi.org/10.11648/j.ajtte.20190404.12
    AB  - Many toll facilities have been faced with traffic shortfalls due to inaccurate and over-forecasted toll revenue projections. Therefore calculating optimal toll rates can be a difficult process. Toll rates are often set to reflect the revenue needed to pay back bonds issued to finance the roadway. This research provides an alternative approach to calculating toll rates where revenue can be maximized while still considering the socio-demographics of the region. Several different approaches used in the border region were explored and compared to field data on an existing toll facility in El Paso, Texas. An innovative simulation-based modeling approach was used to test both static and dynamic pricing algorithms. Static tolling results showed optimal toll rates of $0.14/mile and $0.08/mile for Border Highway West in the westbound and eastbound directions respectively. The Cesar Chavez Highway has optimal toll rates of $0.12 and $0.10/mile in the west and eastbound directions. The dynamic tolling approach showed a max toll rate of $1.56/mile for Cesar Chavez Highway (westbound) during the morning peak period and then incrementally decreased to the minimum toll rate. However, the eastbound direction never increased above the minimum toll rate of $0.08 mile. Border Highway West never increased above the minimum toll rate in either direction. The dynamic tolling algorithm prediction is more representative of the optimal tolling rates for the border region-with the exception of Cesar Chavez Highway westbound.
    VL  - 4
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    ER  - 

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Author Information
  • Multi-Resolution Modeling, Texas A&M Transportation Institute, El Paso, USA

  • Department of Civil Engineering, New Mexico State University, Las Cruces, USA

  • Section