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Attraction area division and freight flow organization optimization of inland railway container terminal

    Chuanzhong Yin Affiliation
    ; Yu Lu Affiliation
    ; Ziru Wang Affiliation
    ; Yang Yan Affiliation
    ; Xinpei Xu Affiliation

Abstract

The attraction area division is the foundation of distribution and organization of freight flow among railway stations. The development of railway container terminal, large railway freight distribution center, is closely related to logistics planning and economy development of local city. In this study, we divide freight flow attraction area of inland railway container terminal by using gravity model, break-point model and weighted-Voronoi-diagram with SPSS and ArcGIS. And then under the target of minimal cost and time window limitations, we develop 0–1 integer programming model for freight flow organization optimization between inland terminal and its attraction area. Finally, this paper takes railway container terminal in Harbin as an example to test model feasibility under different speeds from different transportation modes. The results show that it is necessary to divide attraction area when choosing reasonable transportation mode from feeder nodes to railway container terminal. The improvement of feeder transportation speed is an effective method to improve freight volume, increase railway revenue and realize sustainable development of China Railway (CR) Express.


First published online 18 March 2021

Keyword : freight transportation, China Railway Express, gravity model, break-point model, 0–1 integer programming

How to Cite
Yin, C., Lu, Y., Wang, Z., Yan, Y., & Xu, X. (2021). Attraction area division and freight flow organization optimization of inland railway container terminal. Transport, 36(3), 283-296. https://doi.org/10.3846/transport.2021.14327
Published in Issue
Sep 28, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abellanas, M.; Hurtado, F.; Sacristán, V.; Icking, C.; Ma, L.; Klein, R.; Palop, B. 2003. Voronoi Diagram for services neighboring a highway, Information Processing Letters 86(5): 283–288. https://doi.org/10.1016/s0020-0190(02)00505-7

Banomyong, R.; Beresford, A. K. C. 2001. Multimodal transport: the case of Laotian garment exporters, International Journal of Physical Distribution & Logistics Management 31(9): 663–685. https://doi.org/10.1108/09600030110408161

Bhattacharya, A.; Kumar, S. A.; Tiwari, M. K.; Talluri, S. 2014. An intermodal freight transport system for optimal supply chain logistics, Transportation Research Part C: Emerging Technologies 38: 73–84. https://doi.org/10.1016/j.trc.2013.10.012

Bierwirth, C.; Kirschstein, T.; Meisel, F. 2012. On transport service selection in intermodal rail/road distribution networks, Business Research 5(2): 198–219. https://doi.org/10.1007/bf03342738

Borndörfer, R.; Klug, T.; Schlechte, T.; Fügenschuh, A.; Schang, T.; Schülldorf, H. 2016. The freight train routing problem for congested railway networks with mixed traffic, Transportation Science 50(2): 408–423. https://doi.org/10.1287/trsc.2015.0656

Boukebbab, S.; Boulahlib, M. S. 2015. The spatial interactions using the gravity model: application at the evaluation of transport efficiency at Constantine city, Algeria, Advances in Intelligent Systems and Computing 365: 35–44. https://doi.org/10.1007/978-3-319-19216-1_4

Bowen, J. T. 2012. A spatial analysis of FedEx and UPS: hubs, spokes, and network structure, Journal of Transport Geography 24: 419–431. https://doi.org/10.1016/j.jtrangeo.2012.04.017

Butko, T.; Prokhorov, V.; Kalashnikova, T.; Riabushka, Y. 2019. Organization of railway freight short-haul transportation on the basis of logistic approaches, Procedia Computer Science 149: 102–109. https://doi.org/10.1016/j.procs.2019.01.113

Chang, T.-S. 2008. Best routes selection in international intermodal networks, Computers & Operations Research 35(9): 2877–2891. https://doi.org/10.1016/j.cor.2006.12.025

Cho, J. H.; Kim, H. S.; Choi, H. R. 2012. An intermodal transport network planning algorithm using dynamic programming – a case study: from Busan to Rotterdam in intermodal freight routing, Applied Intelligence 36(3): 529–541. https://doi.org/10.1007/s10489-010-0223-6

Choong, S. T.; Cole, M. H.; Kutanoglu, E. 2002. Empty container management for intermodal transportation networks, Transportation Research Part E: Logistics and Transportation Review 38(6): 423–438. https://doi.org/10.1016/S1366-5545(02)00018-2

Corry, P.; Kozan, E. 2006. An assignment model for dynamic load planning of intermodal trains, Computers & Operations Research 33(1): 1–17. https://doi.org/10.1016/j.cor.2004.05.013

CR. 2019. China Railway Harbin Group Co., Ltd. Available from Internet: http://www.china-railway.com.cn

CSP. 2016. China Statistical Yearbook 2016. China Statistics Press (CSP). Available from Internet: http://www.stats.gov.cn/tjsj/ndsj/2016/indexeh.htm

Demir, E.; Burgholzer, W.; Hrušovský, M.; Arıkan, E.; Jammernegg, W.; Woensel, T. V. 2016. A green intermodal service network design problem with travel time uncertainty, Transportation Research Part B: Methodological 93: 789–807. https://doi.org/10.1016/j.trb.2015.09.007

DTHP. 2020. Department of Transportation of Heilongjiang Province (DTHP), China. Available from Internet: http://jt.hlj.gov.cn (in Chinese).

El-Geneidy, A.; Grimsrud, M.; Wasfi, R.; Tétreault, P.; Surprenant-Legault, J. 2014. New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas, Transportation 41(1): 193–210. https://doi.org/10.1007/s11116-013-9508-z

Feng, X.; He, S.-W.; Li, Y.-B. 2019. Temporal characteristics and reliability analysis of railway transportation networks, Transportmetrica A: Transport Science 15(2): 1825–1847. https://doi.org/10.1080/23249935.2019.1647308

Galvão, L. C.; Novaes, A. G. N.; De Cursi, J. E. S.; Souza, J. C. 2006. A multiplicatively-weighted Voronoi diagram approach to logistics districting, Computers & Operations Research 33(1): 93–114. https://doi.org/10.1016/j.cor.2004.07.001

Hao, C.; Yue, Y. 2016. Optimization on combination of transport routes and modes on dynamic programming for a container multimodal transport system, Procedia Engineering 137: 382–390. https://doi.org/10.1016/j.proeng.2016.01.272

Hübner, A.; Ostermeier, M. 2018. A multi-compartment vehicle routing problem with loading and unloading costs, Transportation Science 53(1): 282–300. https://doi.org/10.1287/trsc.2017.0775

Jiang, B.; Li, J.; Mao, X. 2012. Container ports multimodal transport in china from the view of low carbon, The Asian Journal of Shipping and Logistics 28(3): 321–343. https://doi.org/10.1016/j.ajsl.2013.01.003

Jiang, C.; Zhang, A. 2016. Airline network choice and market coverage under high-speed rail competition, Transportation Research Part A: Policy and Practice 92: 248–260. https://doi.org/10.1016/j.tra.2016.06.008

Kalinina, M.; Olsson, L.; Larsson, A. 2013. A multi objective chance constrained programming model for intermodal logistics with uncertain time, International Journal of Computer Science Issues 10(6): 35–44.

Kim, K. W.; Lee, D. W.; Chun, Y. H. 2010. A comparative study on the service coverages of subways and buses, KSCE Journal of Civil Engineering 14(6): 915–922. https://doi.org/10.1007/s12205-010-0987-6

Lawson, C. T.; Holguín-Veras, J.; Sánchez-Díaz, I.; Jaller, M.; Campbell, S.; Powers, E. L. 2012. Estimated generation of freight trips based on land use, Transportation Research Record: Journal of the Transportation Research Board 2269: 65–72. https://doi.org/10.3141/2269-08

Long, X.; Zhang, Y.; Chen, Y. 2011. Using Voronoi diagram in construction the scope of logistics park hinterland: an engineering application, Systems Engineering Procedia 2: 69–76. https://doi.org/10.1016/j.sepro.2011.10.009

Lozano, A.; Storchi, G. 2001. Shortest viable path algorithm in multimodal networks, Transportation Research Part A: Policy and Practice 35(3): 225–241. https://doi.org/10.1016/S0965-8564(99)00056-7

Lu, B.; Huo, Y. 2013. Potential model for predicting logistics requirements based on regional economics, ICIC Express Letters: an International Journal of Research and Surveys 7(3): 717–721.

Moore, B. 1981. Principal component analysis in linear systems: controllability, observability, and model reduction, IEEE Transactions on Automatic Control 26(1): 17–32. https://doi.org/10.1109/TAC.1981.1102568

O’Sullivan, S.; Morrall, J. 1996. Walking distances to and from light-rail transit stations, Transportation Research Record: Journal of the Transportation Research Board 1538: 19–26. https://doi.org/10.3141/1538-03

Qu, L.; Chen, Y.; Mu, X. 2008. A transport mode selection method for multimodal transportation based on an adaptive ANN system, in 2008 Fourth International Conference on Natural Computation, 18–20 October 2008, Jinan, China, 436–440. https://doi.org/10.1109/ICNC.2008.165

Sánchez-Díaz, I.; Holguín-Veras, J.; Wang, X. 2016. An exploratory analysis of spatial effects on freight trip attraction, Transportation 43(1): 177–196. https://doi.org/10.1007/s11116-014-9570-1

Seo, Y. J.; Chen, F.; Roh, S. Y. 2017. Multimodal transportation: the case of laptop from Chongqing in China to Rotterdam in Europe, The Asian Journal of Shipping and Logistics 33(3): 155–165. https://doi.org/10.1016/j.ajsl.2017.09.005

Talley, W. K.; Ng, M. W. 2018. Hinterland transport chains: a behavioral examination approach, Transportation Research Part E: Logistics and Transportation Review 113: 94–98. https://doi.org/10.1016/j.tre.2018.03.001

Verma, M.; Verter, V. 2010. A lead-time based approach for planning rail–truck intermodal transportation of dangerous goods, European Journal of Operational Research 202(3): 696–706. https://doi.org/10.1016/j.ejor.2009.06.005

Verma, M.; Verter, V.; Zufferey, N. 2012. A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials, Transportation Research Part E: Logistics and Transportation Review 48(1): 132–149. https://doi.org/10.1016/j.tre.2011.06.001

Wang, Z.; Shi, P. 2017. Analyses of metro station service area in shanghai downtown based on traffic networks, Journal of the Indian Society of Remote Sensing 45(2): 337–352. https://doi.org/10.1007/s12524-016-0595-0

Wei, H.; Dong, M. 2019. Import-export freight organization and optimization in the dry-port-based cross-border logistics network under the belt and road initiative, Computers & Industrial Engineering 130: 472–484. https://doi.org/10.1016/j.cie.2019.03.007

Wei, W.; Xuejun, F.; Li, H. 2008. Research on regional logistics system layout optimization based on weighted Voronoi diagram and gravitational model, in 2008 IEEE International Conference on Automation and Logistics, 1–3 September 2008, Qingdao, China, 2078–2083. https://doi.org/10.1109/ICAL.2008.4636506

Xia, W.; Jiang, C.; Wang, K.; Zhang, A. 2019. Air–rail revenue sharing in a multi-airport system: effects on traffic and social welfare, Transportation Research Part B: Methodological 121: 304–319. https://doi.org/10.1016/j.trb.2018.10.002

Yang, X.; Low, J. M. W.; Tang, L. C. 2011. Analysis of intermodal freight from China to Indian Ocean: A goal programming approach, Journal of Transport Geography 19(4): 515–527. https://doi.org/10.1016/j.jtrangeo.2010.05.007

Yao, X.-S.; Huang, H.-Z.; Zhou, Z.-R. 2002. Study on multi-objective optimization based on generalized satisfactory degree theory for transportation capability of railway-network, in International Conference on Traffic and Transportation Studies (ICTTS) 2002, 23–25 July 2002, Guilin, China, 947–952. https://doi.org/10.1061/40630(255)132

Yu, B.; Shu, S.; Liu, H.; Song, W.; Wu, J.; Wang, L.; Chen, Z. 2014. Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China, International Journal of Geographical Information Science 28(11): 2328–2355. https://doi.org/10.1080/13658816.2014.922186

Zografos, K. G.; Androutsopoulos, K. N. 2008. Algorithms for itinerary planning in multimodal transportation networks, IEEE Transactions on Intelligent Transportation Systems 9(1): 175–184. https://doi.org/10.1109/TITS.2008.915650