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Flow assignment model for quantitative analysis of diverting bulk freight from road to railway

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  • Chang Liu
  • Boliang Lin
  • Jiaxi Wang
  • Jie Xiao
  • Siqi Liu
  • Jianping Wu
  • Jian Li

Abstract

Since railway transport possesses the advantage of high volume and low carbon emissions, diverting some freight from road to railway will help reduce the negative environmental impacts associated with transport. This paper develops a flow assignment model for quantitative analysis of diverting truck freight to railway. First, a general network which considers road transportation, railway transportation, handling and transferring is established according to all the steps in the whole transportation process. Then general functions which embody the factors which the shippers will pay attention to when choosing mode and path are formulated. The general functions contain the congestion cost on road, the capacity constraints of railways and freight stations. Based on the general network and general cost function, a user equilibrium flow assignment model is developed to simulate the flow distribution on the general network under the condition that all shippers choose transportation mode and path independently. Since the model is nonlinear and challenging, we adopt a method that uses tangent lines to constitute envelope curve to linearize it. Finally, a numerical example is presented to test the model and show the method of making quantitative analysis of bulk freight modal shift between road and railway.

Suggested Citation

  • Chang Liu & Boliang Lin & Jiaxi Wang & Jie Xiao & Siqi Liu & Jianping Wu & Jian Li, 2017. "Flow assignment model for quantitative analysis of diverting bulk freight from road to railway," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-22, August.
    • Handle: RePEc:plo:pone00:0182179
    DOI: 10.1371/journal.pone.0182179
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    References listed on IDEAS

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    1. Rich, J. & Holmblad, P.M. & Hansen, C.O., 2009. "A weighted logit freight mode-choice model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(6), pages 1006-1019, November.
    2. Kreutzberger, Ekki D., 2008. "Distance and time in intermodal goods transport networks in Europe: A generic approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(7), pages 973-993, August.
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    Cited by:

    1. Jansuwan, Sarawut & Chen, Anthony & Xu, Xiangdong, 2021. "Analysis of freight transportation network redundancy: An application to Utah’s bi-modal network for transporting coal," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 154-171.
    2. Boliang Lin & Jingsong Duan & Jiaxi Wang & Min Sun & Wengao Peng & Chang Liu & Jie Xiao & Siqi Liu & Jianping Wu, 2018. "A study of the car-to-train assignment problem for rail express cargos in the scheduled and unscheduled train services network," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-18, October.
    3. Yan Sun & Xinya Li, 2019. "Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncerta," Mathematics, MDPI, vol. 7(8), pages 1-40, August.
    4. Boliang Lin & Xingkui Li & Zexi Zhang & Yinan Zhao, 2019. "Optimizing Transport Scheme of High Value-Added Shipments in Regions without Express Train Services," Sustainability, MDPI, vol. 11(21), pages 1-21, November.
    5. Hangfei Huang & Keping Li & Paul Schonfeld, 2018. "Real-time energy-saving metro train rescheduling with primary delay identification," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-22, February.

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