Effect of traffic congestion on infrastructure capacity: modeling commuters’ behavior towards “stick & carrot” strategies to better manage infrastructure network: the case study of Cairo

  • Authors

    • M. Thabet Cairo University - Egypt
    • H. Abdelgawad Cairo University - Egypt
    • H. Osman Cairo University - Egypt
    • M. El-Said Cairo University - Egypt
    https://doi.org/10.14419/ijet.v7i4.28858

    Received date: April 15, 2019

    Accepted date: June 13, 2019

    Published date: July 14, 2019

  • Cairo, Capacity Management, Congestion Pricing, Infrastructure Network, Modeling, Peak-Avoidance Rewards, Traffic Congestion.

  • Abstract

    In Cairo, alike many mega cities, suffers from the consequences of urban sprawl and traffic congestions. Recent studies report the cost of congestion in the Greater Cairo Area amounting to 4% of the nation’s GDP. Traditionally governments have addressed congestion by boosting infrastructure capacity to meet increased demand. Although in some cases this expansion is inevitable, it has proven unsustainable on the long run. An alternative approach is to manage and control the demand given the existing infrastructure using different strategies. One such strategy aims to influence the behavior of transportation users to manage the demand. This study investigates two distinct - but related - approaches to influence traveler behavior. The “stick & carrot” technique applies both congestion pricing and peak-avoidance rewards to travelers. This paper models the change of commuters’ behavior by measuring and comparing their willingness to pay (WTP) and attitude to earn (ATE) during peak hour travel. A mixed stated preference/revealed preference survey is developed and distributed online to investigate the effect of congestion on commuters’ daily travel behavior. The collected responses are analyzed and modeled using a binary logit model for commuters’ behavioral change for both their WTP and ATE potentials. Results indicate that there is a massive congestion problem in Cairo that people really suffer from, and it affects their daily life decisions, also, they seem not to prefer congestion pricing technique, while they prefer to be rewarded instead. Furthermore, a comprehensive development of Cairo’s public transport network should be implemented. The obtained models from collected data emphasizes the previous hypothesis.

  • References

    1. Lieu, H., Gartner, N., Messer, C. J., & Rathi, A. K. (1999). “Traffic flow theory”. Public Roads, Volume 62, pp.45-47.
    2. Knoop, V. L., & Daamen, W. (2017). “Automatic fitting procedure for the fundamental diagram”. Transport metrica B: Transport Dy-namics, Volume 5 (2), pp.129-144. https://doi.org/10.1080/21680566.2016.1256239.
    3. http://www.worldbank.org/en/country/egypt/publication/cairo-traffic-congestion-study-executive-note - access date 15.3.2018
    4. Small, K. A., & Gómez-Ibáñez, J. A. (1997). “Road pricing for con-gestion management: the transition from theory to policy”, Transport Economics, pp.373-403.
    5. Tillema, T., Ben-Elia, E., Ettema, D., & van Delden, J. (2013). “Charging versus rewarding: A comparison of road-pricing and re-warding peak avoidance in the Netherlands”, Transport Poli-cy, Volume 26, pp.4-14. https://doi.org/10.1016/j.tranpol.2012.01.003.
    6. Tillema, T. (2007). Road pricing: a transport geographical perspec-tive, Geographical Accessibility and Short and Long-Term Behav-ioral Effects, NWO/Connekt VEV project, Urban and Regional re-search centre Utrecht, Faculty of Geosciences, Utrecht University.
    7. Ettema, D., & Timmermans, H. (2006). “Costs of travel time uncer-tainty and benefits of travel time information: conceptual model and numerical examples”, Transportation Research Part C: Emerging Technologies, Volume 14 (5), pp.335-350. https://doi.org/10.1016/j.trc.2006.09.001.
    8. Swamy, H. S., & Sinha, S. (2014). Urban Transport Developments in India under NUTP and JnNURM.
    9. Börjesson, M. (2008). “Joint RP–SP data in a mixed logit analysis of trip timing decisions”, Transportation Research Part E: Logistics and Transportation Review, Volume 44 (6), pp.1025-1038. https://doi.org/10.1016/j.tre.2007.11.001.
    10. Ubbels, B. J. (2006). Road Pricing. Effectivenss, Acceptance and Institutional Aspects, NWO/Connekt VEV project, Urban and Re-gional research centre Utrecht, Faculty of Geosciences, Utrecht Uni-versity.
    11. Ben-Elia, E., Bierlaire, M., & Ettema, D. (2010). “A behavioural departure time choice model with latent arrival time preference and rewards for peak-hour avoidance”. In European Transport Confer-ence.
    12. Ben-Elia, E., & Ettema, D. (2009). “Carrots versus sticks: Reward-ing commuters for avoiding the rush-hour—a study of willingness to participate”, Transport policy, Volume 16 (2), pp.68-76. https://doi.org/10.1016/j.tranpol.2009.03.005.
    13. Bie, J., van Arem, B., & Igamberdiev, M. (2010, January). “Using economic incentives to influence drivers' route choices for safety en-hancement: A cost-benefit analysis and the results from an empirical study”, In Compendium of Papers TRB 89th Annual Meeting. DVD, Mira Digital Publishing.
    14. Peer, S., Knockaert, J., & Verhoef, E. T. (2016). “Train commuters’ scheduling preferences: Evidence from a large-scale peak avoidance experiment”, Transportation Research Part B: Methodologi-cal, Volume 83, pp.314-333. https://doi.org/10.1016/j.trb.2015.11.017.
    15. Leblanc, R., & Walker, J. L. (2013). “Which is the biggest carrot? comparing nontraditional incentives for demand management”, In Proceedings of the Transportation Research Board 92nd Annual Meeting (No. 13-5039).
    16. Kumar, V., Bhat, C. R., Pendyala, R. M., You, D., Ben-Elia, E., & Ettema, D. (2016). “Impacts of incentive-based intervention on peak period traffic: experience from the Netherlands”, Transportation Re-search Record: Journal of the Transportation Research Board, Vol-ume 2543, pp.166-175. https://doi.org/10.3141/2543-20.
    17. Santos, G. (2008). London congestion charging. In G. Burtless and J. Rothenberg Pack (eds.), Brookings Wharton Papers on Urban Af-fairs: 2008, The Brookings Institution, pp.177–207.
    18. http://thisbigcity.net/five-cities-with-congestion-pricing - access date 17.3.2018
    19. Chu, J. (2011, October 28). Searching for balloons in a social net-work. Retrieved June 2016, from MIT News: http://news.mit.edu/2011/red-balloons-study-102811
    20. SETS North Africa (2016). “Towards wise cities: A data-driven ap-proach for sustainable mobility”, progress report, Dec, 2016, EG1609_Rep_En_00_Rev.1
    21. Abdulhai, B. (2013). Congestion Management in the GTHA: Bal-ancing the Inverted Pendulum, an independent study, residential and civil construction alliance of Ontario (RCCAO)
    22. ECMT (2004), Assessment and Decision Making for Sustainable Transport, European Conference of Ministers of Transportation, Or-ganization of Economic Coordination and Development
    23. Litman, T. (2007). “Developing indicators for comprehensive and sustainable transport planning”, Transportation Research Record: Journal of the Transportation Research Board, pp.10-15. https://doi.org/10.3141/2017-02.
    24. Saleh, W., & Farrell, S. (2005). “Implications of congestion charging for departure time choice: work and non-work schedule flexibil-ity”, Transportation Research Part A: Policy and Practice, Volume 39(7-9), pp.773-791. https://doi.org/10.1016/j.tra.2005.02.016.
    25. Small, K. A. and Verhoef, E. T. (2007). The Economics of Urban Transportation. Abingdon, Oxon, England: Routledge. https://doi.org/10.4324/9780203642306.
    26. Hensher, D.A. and Button, K.J. (2000). “Handbook of Transport Modelling”, Elsevier Science Ltd., Oxford, U.K., pp. 1-10.
    27. Khan, O. A. (2007). Modelling passenger mode choice behaviour using computer aided stated preference data, Doctoral dissertation, Queensland University of Technology.
    28. Ben-Akiva, M. E., Lerman, S. R., & Lerman, S. R. (1985). “Discrete choice analysis: theory and application to travel demand”, MIT press, Massachusetts, U.S.A., Volume 9.
    29. Louviere, J.J., Hensher, D.A., Swait, J.D., (2000). Stated Choice Methods: Analysis and Application. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511753831.
    30. http://thisbigcity.net/five-cities-with-congestion-pricing - access date 17.3.2018
    31. http://inrix.com/press-releases/scorecard-2018-us - access date 19.3.2019
    32. Ettema, D., Knockaert, J., & Verhoef, E. (2010). “Using incentives as traffic management tool: empirical results of the" peak avoidance" experiment”, Transportation Letters, Volume 2(1), pp.39-51. https://doi.org/10.3328/TL.2010.02.01.39-51.

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  • How to Cite

    Thabet, M., Abdelgawad, H., Osman, H., & El-Said, M. (2019). Effect of traffic congestion on infrastructure capacity: modeling commuters’ behavior towards “stick & carrot” strategies to better manage infrastructure network: the case study of Cairo. International Journal of Engineering and Technology, 7(4), 6884-6898. https://doi.org/10.14419/ijet.v7i4.28858