Document Type: Original Article

Authors

Industrial Engineering Department, K.N. Toosi University, Tehran, Iran.

Abstract

This paper introduces a Travel Demand Management (TDM) model in order to decrease the transportation externalities by affecting on passengers’travel choices. Thus, a bi-objective bi-modal optimization model for road pricing is developed aiming to enhance environmental and social sustainability by considering to minimize the air pollution and maximize the social welfare as its objectives. This model determines optimal prices (bus fare and car toll) and optimal bus frequency simultaneously in an integrated model. The model is based on discrete choice theory and consideres the modes’ utility functions in its formulation. The proposed model is solved by two meta-heuristic methods (Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objectives Harmony Search (MOHS)) and the numerical results of a case study in Tehran are presented. The main managerial insights resulted from this case study is that its results support the idea of “free public transportation” or subsidizing the public transport as an effective way to decrease the transport related air pollution

Keywords

Barth, M., & Boriboonsomsin, K., (2008). "Real-world carbon dioxide impacts of traffic congestion",
Transportation Research Record, No. 2058, pp. 163–171.

Bigazzi, A.Y., & Mathieu Rouleau, M., (2017). "Can traffic management strategies improve urban air quality? A review of the evidence", Journal of Transport & Health, Vol. 7, Part B, pp. 111-124.

Börjesson, M., Fung, CH.M., & Stef Proost, S., (2017). "Optimal prices and frequencies for buses in Stockholm", Economics of Transportation, Vol. 9, pp. 20-36.

Burguillo, M., Romero-Jordan, D., & Sanz-Sanz, J.F., (2017). "The new public transport pricing in Madrid Metropolitan Area: A welfare analysis", Research in Transportation Economics, Vol. 62, pp. 25-36.

Cats, O., Susilo, Y.O., & Reimal, T., (2017). "The prospects of fare-free public transport: evidence from Tallinn", Transportation, Vol. 44, pp. 1083–1104.

Coello Coello, Ch., Lamont, G.B., & Van Veldhuizen, D.A., (2007). "Evolutionary Algorithms for Solving Multi-Objective Problems", 2nd Edition, New York, Springer.

Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T., (2000). "A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II", Parallel Problem Solving from Nature PPSN VI, pp 849-858.

De Palma, A., Kilani, M., & Proost, S., (2015). "Discomfort in mass transit and its implication for scheduling and pricing", Transportation Research Part B: Methodological, Vol. 71, pp. 1–18.

De Palma, A., & Lindsey, R., (2011). "Traffic congestion pricing methodologies and technologies", Transportation Research Part C, Vol. 19, No. 6, pp. 1377-1399.

Goli, A., Tirkolaee, E. B., Malmir, B., Bian, G.-B., & Sangaiah, A. K., (2019). "A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand", Computing, doi: 10.1007/s00607-018-00692-2. 

Gu, Z., Liu, Zh., Cheng, Q., & Saberi, M., (2018). "Congestion Pricing Practices and Public Acceptance: A Review of Evidence", Case Studies on Transport Policy, Vol. 6, No. 1, pp. 94-101, DOI: 10.1016/j.cstp.2018.01.004

Hajipour, V., Rahmati, S. H. A., Pasandideh, S. H. R., & Niaki, S. T. A., (2014). “A multi-objective harmony search algorithm to optimize multi-server location–allocation problem in congested systems”, Computers & Industrial Engineering, Vol. 72, pp. 187–197, DOI:10.1016/j.cie.2014.03.018

Johansson-Stenman, O., (2006). "Optimal environmental road pricing", Economics Letters, Vol. 90, No. 2, pp. 225–229.

Karlström, A., &Franklin, J. P., (2009). "Behavioral adjustments and equity effects of congestion pricing: Analysis of morning commutes during the Stockholm trial", Transportation Research Part A: Policy and Practice, Vol. 43, No. 3, pp. 283–296.

Knight, F.H., (1924). "Some fallacies in the interpretation of social cost", The Quarterly Journal of Economics, Vol. 38, No. 4, pp. 582-606.

Li, X., Lv,Y., Sun, W., & Zhou, L., (2019). "Cordon- or Link-Based Pricing: Environment-Oriented Toll Design Models Development and Application", Sustainability, Vol. 11, No.  258, DOI: 10.3390/su11010258

Liu, Z.Y., Wang, T.S., Qu, X.B., & Yan, Y.D., (2014a). "Urban Congestion Pricing: Practices and Future Development", Applied Mechanics and Materials, Vol. 505-506, pp. 787-793.

Liu, Z., Wang, S., & Meng, Q., (2014b). "Optimal Joint Distance and Time Toll for Cordon-based Congestion Pricing", Transportation Research Part B, Vol. 69, pp. 81-97.

May, A., (1992). "Road pricing: an international perspective", Transportation, Vol. 19, No. 4, pp. 313- 333.

Mohring, H., (1972). "Optimization and Scale Economies in Urban Bus Transportation", American Economic Review, Vol. 62, No. 4, pp. 591–604.

Ortuzar, J. D., & Willumsen, L.G., (2011). Modelling transport, Fourth ed. Wiley.

Parry, I.W.H., (2008). "Pricing Urban Congestion", Resources for the future (RFF), discussion paper DP 08-35.

Percoco, M., (2013). "Is road pricing effective in abating pollution? Evidence from Milan", Transportation Research Part D: Transport and Environment, Vol. 25, pp. 112–118.

Pigou, A.C., (1920). The Economics of Welfare. MacMillan, London.

Proost, S., & Dender, K. V., (2008). "Optimal urban transport pricing in the presence of congestion, economies of density and costly public funds", Transportation Research Part A: Policy and Practice, Vol. 42, pp. 1220–1230.

Rahmati, S. H. A., Hajipour, V., & Niaki, S. T. A., (2013). "A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem", Applied Soft Computing, Vol. 13, No. 4, pp. 1728–1740, DOI:10.1016/j.asoc.2012.12.016 

Ricart, J., Huttemann, G., Lima, J., & Baran, B., (2011). "Multiobjective Harmony Search Algorithm Proposals", Electronic Notes in Theoretical Computer Science, Vol. 281, pp. 51–67.

Schott, J. R., (1995). "Fault tolerant design using single and multicriteria genetic algorithms optimization", Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA.

Simeonova, E., Currie, J., Nilsson, P., & Reed Walker, R., (2018). "Congestion pricing, air pollution and children’s health", Working Paper 24410 of NBER working paper series, National Bureau of Economic Research, Cambridge.

Smits, E.-S., (2017). “Strategic Network Modelling For Passenger Transport Pricing”, T2017/3, TRAIL Thesis Series, Delft University of Technology, Netherlands.

Tavakkoli-Moghaddam, R., Noshafagh, S.V., Taleizadeh, A.A., Hajipour, A.A., & Mahmoudi, A., (2016). "Pricing and location decisions in multi-objective facility location problem with M/M/m/k queuing systems", Engineering Optimization, Vol. 49, pp. 136-160.

Tirachini, A., Hensher, D., & Rose, J.M. (2014). "Multimodal pricing and optimal design of urban public transport: The interplay between traffic congestion and bus crowding", Transportation Research PartB: Methodological, Vol. 61, pp. 33–54.

Tirkolaee, E. B., Goli, A., Hematian, M., Sangaiah, A. K., & Han, T., (2019). "Multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms", computing,DOI: 10.1007/s00607-018-00693-1  

Wei, B, Sun, & D.J, (2018). "A Two-Layer Network Dynamic Congestion Pricing Based on Macroscopic Fundamental Diagram", Journal of Advanced Transportation, Vol. 2018, Article ID 8616120, Hindawi, Wiley, DOI: /10.1155/2018/8616120 

Wu, K., Chen, Y., Ma, J., Bai, S., & Tang, X., (2017). "Traffic and emissions impact of congestion charging in the central Beijing urban area: A simulation analysis", Transportation Research Part D: Transport and Environment, Vol. 51, pp. 203–215.

Zhang, K., Batterman, S., & Dion, F., (2011). "Vehicle emissions in congestion: comparison of work zone, rush hour and free-flow conditions", Atmospheric Environment, Vol. 45, No. 11, pp. 1929-1939.