Proposing a New Method for Transportation Planning Considering Traffic Congestion

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran

2 Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.

Abstract

Traffic congestion is one of the issues in transportation planning which imposes environmental consequences and costs. Therefore, decision-makers and policymakers should focus on appropriate transportation planning models. One of the approaches to relieve traffic congestion is imposing tolls on the users. In the present paper, attempts are made to present three transportation planning and traffic congestion management models. The first model assumes that the transportation network and the traffic flows within it are determined. Decision-maker seeks to adjust the transportation network flows so that traffic congestion can be prevented. In the second model, unlike the first one, attempts are made to design urban transportation networks via the development of routes. The third model is a mixture of the first and second models. All models proposed here are bi-objective which were addressed under uncertain conditions and disturbances. According to the results, a decision-making model was extended to rank routes. In the end, a numerical example is considered for analyzing and evaluating the proposed models. The results of the numerical example showed that the first model is the most inefficient and the third model is the most efficient. Since the proposed model can be implemented in road networks in addition to urban transportation networks, the application of the proposed models is demonstrated based on a real-world case study. The case study results showed that the efficiency of road network depends on the time interval.

Keywords


  • [1] Figueiras, P., Gonçalves, D., Costa, R., Guerreiro, G., Georgakis, P., and Jardim-Gonçalves, R. (2019). Novel Big Data-supported dynamic toll charging system: Impact assessment on Portugal’s shadow-toll highways. Computers and Industrial Engineering, 135(June), 476–491.
  • [2] Assadipour, G., Ke, G. Y., and Verma, M. (2016). A toll-based bi-level programming approach to managing hazardous materials shipments over an intermodal transportation network. Transportation Research Part D, 47, 208–221.
  • [3] He, (2016). Optimal Time-Varying Pricing for Toll Roads Under Multiple Objectives : A Simulation-Based Optimization Approach.
  • [4] Tsai, J.-F., and Li, S.-C. (2019). Cordon tolling for mixed traffic flow. Transportmetrica A: Transport Science, 15(2), 1662–1687.
  • [5] Harks, T., Schröder, M., and Vermeulen, D. (2019). Toll caps in privatized road networks. European Journal of Operational Research, 276(3), 947–956.
  • [6] Chen, J., Zhao, F., Liu, Z., Ou, X., and Hao, H. (2017). Greenhouse gas emissions from road construction in China: A province-level analysis. Journal of Cleaner Production, 168, 1039–1047.
  • [7] Xu, C., Zhao, J., and Liu, P. (2019). A Geographically Weighted Regression Approach to Investigate the Effects of Traffic Conditions and Road Characteristics on Air Pollutant Emissions. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2019.118084
  • [8] Rodriguez Roman, D., and Ritchie, S. G. (2017). Accounting for population exposure to vehicle-generated pollutants and environmental equity in the toll design problem. International Journal of Sustainable Transportation, 11(6), 406–421.
  • [9] Sobrino, N., Monzon, A., and Hernandez, S. (2016). Reduced Carbon and Energy Footprint in Highway Operations: The Highway Energy Assessment (HERA) Methodology. Networks and Spatial Economics, 16(1), 395–414.
  • [10] Wang, Z., Deng, X., Wong, C., Li, Z., and Chen, J. (2018). Learning urban resilience from a social-economic-ecological system perspective: A case study of Beijing from 1978 to 2015. Journal of Cleaner Production, 183, 343–357.
  • [11] Patil, G. R. (2016). Emission-based static traffic assignment models. Environmental Modeling and Assessment, 21(5), 629–642.
  • [12] Lv, Y., Wang, S., Gao, Z., Li, X., and Sun, W. (2018). Design of a heuristic environment-friendly road pricing scheme for traffic emission control under uncertainty. (June). https://doi.org/10.1016/j.jenvman.2018.11.042
  • [13] Edrisi, A., and Askari, M. (2019). Probabilistic budget allocation for improving efficiency of transportation networks in pre-and post-disaster phases. International Journal of Disaster Risk Reduction, 101113. https://doi.org/10.1016/j.ijdrr.2019.101113
  • [14] Sathiaraj, D., Punkasem, T. on, Wang, F., and Seedah, D. P. K. (2018). Data-driven analysis on the effects of extreme weather elements on traffic volume in Atlanta, GA, USA. Computers, Environment and Urban Systems, 72(February), 212–220.
  • [15] Li, , Kappas, M., and Li, Y. (2018). Exploring the coastal urban resilience and transformation of coupled human-environment systems. Journal of Cleaner Production, 195, 1505–1511.
  • [16] Wang, J., Hu, X., and Li, C. (2018). Optimization of the freeway truck toll by weight policy, including external environmental costs. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2018.02.228
  • [17] Elluru, S., Gupta, H., Kaur, H., and Singh, S. P. (2017). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research, 1–26. https://doi.org/10.1007/s10479-017-2681-2
  • [18] Shirazi, M., Aashtiani, H. Z., and Quadrifoglio, L. (2017). Estimating the minimal revenue tolls in large-scale roadway networks using the dynamic penalty function method. Computers and Industrial Engineering, 107, 120–127.
  • [19] Shirazi, M., and Aashtiani, H. Z. (2015). Solving the minimum toll revenue problem in real transportation networks. Optimization Letters, 9(6), 1187–1197.
  • [20] Stefanello, F., Buriol, L. S., Hirsch, M. J., Pardalos, P. M., Querido, T., Resende, M. G. C., and Ritt, M. (2017). On the minimization of traffic congestion in road networks with tolls. Annals of Operations Research, 249(1–2), 119–139.
  • [21] Xu, M., Wang, G., Grant-Muller, S., and Gao, Z. (2017). Joint road toll pricing and capacity development in discrete transport network design problem. Transportation, 44(4), 731–752.
  • [22] Cheng, Q., Liu, Z., and Szeto, W. Y. (2019). A cell-based dynamic congestion pricing scheme considering travel distance and time delay. Transportmetrica B: Transport Dynamics, 7(1), 1286–1304.
  • [23] Liu, Z., and Song, Z. (2019). Strategic planning of dedicated autonomous vehicle lanes and autonomous vehicle/toll lanes in transportation networks. Transportation Research, Part C: Emerging Technologies, 106(July), 381–403.
  • [24] Odeck, J., and Welde, M. (2017). The accuracy of toll road traffic forecasts: An econometric evaluation. Transportation Research Part A: Policy and Practice, 101, 73–85.
  • [25] Laurent, A. B., Vallerand, S., van der Meer, Y., and D’Amours, S. (2019). CarbonRoadMap: A multicriteria decision tool for multimodal transportation. International Journal of Sustainable Transportation, 8318. https://doi.org/10.1080/15568318.2018.1540734
  • [26] Alasad, R., and Motawa, I. (2015). Dynamic demand risk assessment for toll road projects. Construction Management and Economics, 33(10), 799–817.
  • [27] Wang, D. (2019). Performance assessment of major global cities by DEA and Malmquist index analysis. Computers, Environment and Urban Systems, 77(July), 101365. https://doi.org/10.1016/j.compenvurbsys.2019.101365
  • [28] Figueiras, P., Gonçalves, D., Costa, R., Guerreiro, G., Georgakis, P., and Jardim-Gonçalves, R. (2019). Novel Big Data-supported dynamic toll charging system: Impact assessment on Portugal’s shadow-toll highways. Computers and Industrial Engineering, 135(June), 476–491.
  • [29] Klimberg, R. K., and Ratick, S. J. (2008). Modeling data envelopment analysis (DEA) efficient location/allocation decisions. Computers and Operations Research, 35(2), 457–474.
  • [30] Cheng, Y., and Gao, H. L. (2015). Matrix-Type Network DEA Model with Its Application Based on Input-Output Tables. Mathematical Problems in Engineering, 2015. https://doi.org/10.1155/2015/505941
  • [31] Wang, Y., and Zeng, Z. (2018). Data-Driven solutions to transportation problems. Elsevier.
  • [32] Ghasemi, M.-R., Ignatius, J., Emrouznejad, A. (2014). A bi-objective weighted model for improving the discrimination power in MCDEA. European Journal of Operational Research, 233, 640–650.
  • [33] Bootaki, B., Mahdavi, I., and Paydar, M. M. (2015). New bi-objective robust design-based utilisation towards dynamic cell formation problem with fuzzy random demands. International Journal of Computer Integrated Manufacturing, 28(6), 577–592.
  • [34] Tang, L., Jiang, W., and Saharidis, G. K. D. (2013). An improved Benders decomposition algorithm for the logistics facility location problem with capacity expansions. Annals of Operations Research, 210(1), 165–190.
  • [35] Osman, H., and Demirli, K. (2010). A bilinear goal programming model and a modified Benders decomposition algorithm for supply chain reconfiguration and supplier selection. International Journal of Production Economics, 124(1), 97–105.
  • [36] Hadi-Vencheh, A., Hatami-Marbini, A., Ghelej Beigi, Z., and Gholami, K. (2015). An inverse optimization model for imprecise data envelopment analysis. Optimization, 64(11), 2441–2454.