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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
01.03.2024
DOI
https://doi.org/10.7307/ptt.v36i1.269
ISSUE
2024 (Vol 36), Issue 1
LICENSE
Copyright (c) 2024 Guoling Jia
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

How Much Do Urban Terminal Delivery Paths Depend on Urban Roads – A Research Based on Bipartite Graph Network

Authors:Guoling Jia

Abstract

The structural deficiencies of the terminal delivery path often make it the main culprit of urban traffic congestion and environmental pollution. Traditional studies of express networks regarded them as an independent entity, ignoring the endogenous role of urban road network morphology and structure. To solve this problem, this paper explored the spatial dependency of terminal delivery routes in Xi'an City based on the idea of a bipartite graph network. A spatial dependency matrix of delivery paths–urban roads was constructed by abstracting delivery paths as node-set A and urban roads as node-set B. In addition, three spatial dependencies indexes, including degree centrality, betweenness centrality and closeness centrality were introduced to analyse the coupling features of these two objects. The results show that these dependency measures can reflect the coupling features of urban terminal delivery paths and urban roads. Firstly, degree centrality demonstrates terminal delivery path coverage and coupling hierarchy and scale-free nature. Secondly, betweenness centrality presents the road utilisation balance of terminal delivery paths. Thirdly, closeness centrality explains how easy it is for delivery paths to connect with others.

Keywords:terminal delivery, delivery path, bipartite graph network, urban logistics, urban road, spatial network

References

  1. [1] Hong S, Lv R, Hong P. Cost sharing of terminal joint distribution of express industry. IET Intelligent Transport Systems. 2018;12(7):730-734. DOI: 10.1049/iet-its.2017.0277.
  2. [2] Hagen T, Scheel-Kopeinig S. Would customers be willing to use an alternative (chargeable) delivery concept for the last mile? Research in Transportation Business and Management. 2021;39. DOI: 10.1016/j.rtbm.2021.100626.
  3. [3] Nam D, Park M. Improving the operational efficiency of parcel delivery network with a bi-level decision making model. Sustainability. 2020;12(19). DOI: 10.3390/su12198042.
  4. [4] Caspersen E, Navrud S, Bengtsson J. Act locally? Are female online shoppers willing to pay to reduce the carbon footprint of last mile deliveries? International Journal of Sustainable Transportation. 2021. DOI: 10.1080/15568318.2021.1975326.
  5. [5] Zhao Q, Wang W, de Souza R. A heterogeneous fleet two-echelon capacitated location-routing model for joint delivery arising in city logistics. International Journal of Production Research. 2018;56(15):5062-5080. DOI: 10.1080/00207543.2017.1401235.
  6. [6] Luo Y-H, Li L. A parameterized algorithm for minimum node weighted Steiner tree in logistics networks. Computer Engineering and Science. 2018;40(1):58-65. DOI: 10.3969/j.issn.1007-130X.2018.01.009.
  7. [7] Zhou L, et al. A multi-depot two-echelon vehicle routing problem with delivery options arising in the last mile distribution. European Journal of Operational Research. 2018;265(2):765-778. DOI: 10.1016/j.ejor.2017.08.011.
  8. [8] Cao Y, Chen H. Research on truck and drone joint distribution scheduling based on cluster. Computer Engineering and Application. 2022;58(11):287-294.
  9. [9] Yu Y, Lian F, Yang Z. Pricing of parcel locker service in urban logistics by a TSP model of last-mile delivery. Transport Policy. 2021;114:206-214. DOI: 10.1016/j.tranpol.2021.10.002.
  10. [10] Jiang L, et al. A traveling salesman problem with time windows for the last mile delivery in online shopping. International Journal of Production Research. 2020;58(16):5077-5088. DOI: 10.1080/00207543.2019.1656842.
  11. [11] Nguyen TBT, et al. Optimising parcel deliveries in London using dual-mode routing. Journal of the Operational Research Society. 2019;70(6):998-1010. DOI: 10.1080/01605682.2018.1480906.
  12. [12] Ning T, Wang X, Hu X. Study on disruption management strategy of terminal logistics based on prospect theory. Systems Engineering - Theory & Practice. 2019;39(3):673-681.
  13. [13] Li J, et al. Integrated optimization of electric vehicle allocation & routing for large scale e-commerce terminal logistics distribution. Operations Research and Management Science. 2018;27(10):23-30.
  14. [14] Zhang Y, et al. Order consolidation for the last-mile split delivery in online retailing. Transportation Research Part E - Logistics and Transportation Review. 2019;122:309-327. DOI: 10.1016/j.tre.2018.12.011.
  15. [15] He Y, et al. Dynamic vehicle routing problem considering simultaneous dual services in the last mile delivery. Kybernetes. 2020;49(4):1267-1284. DOI: 10.1108/k-05-2018-0236.
  16. [16] Zhou L, et al. Model and algorithm for bilevel multisized terminal location-routing problem for the last mile delivery. International Transactions in Operational Research. 2019;26(1):131-156. DOI: 10.1111/itor.12399.
  17. [17] Bergmann FM, Wagner SM, Winkenbach M. Integrating first-mile pickup and last-mile delivery on shared vehicle routes for efficient urban e-commerce distribution. Transportation Research Part B - Methodological. 2020;131:26-62. DOI: 10.1016/j.trb.2019.09.013.
  18. [18] Azcuy I, Agatz N, Giesen R. Designing integrated urban delivery systems using public transport. Transportation Research Part E-Logistics and Transportation Review. 2021;156. DOI: 10.1016/j.tre.2021.102525.
  19. [19] Allen J, et al. Understanding the impact of e-commerce on last-mile light goods vehicle activity in urban areas: The case of London. Transportation Research Part D - Transport and Environment. 2018;61:325-338. DOI: 10.1016/j.trd.2017.07.020.
  20. [20] Dupljanin D, et al. Urban crowdsourced last mile delivery: Mode of transport effects on fleet performance. International Journal of Simulation Modelling. 2019;18(3):441-452. DOI: 10.2507/ijsimm18(3)481.
  21. [21] Bi K, et al. A new solution for city distribution to achieve environmental benefits within the trend of green logistics: A case study in China. Sustainability. 2020;12(20). DOI: 10.3390/su12208312.
  22. [22] Caspersen E, Navrud S. The sharing economy and consumer preferences for environmentally sustainable last mile deliveries. Transportation Research Part D - Transport and Environment. 2021;95. DOI: 10.1016/j.trd.2021.102863.
  23. [23] Llorca C, Moeckel R. Assesment of the potential of cargo bikes and electrification for last-mile parcel delivery by means of simulation of urban freight flows. European Transport Research Review. 2021;13(1). DOI: 10.1186/s12544-021-00491-5.
  24. [24] Li W, Li K, Ruan W. Research on the mode of hub and spoke network used in city express. Operations Research and Management Science. 2020;29(4):36-42.
  25. [25] Zhang X, Liu X. A two-stage robust model for express service network design with surging demand. European Journal of Operational Research. 2022;299(1):154-167. DOI: 10.1016/j.ejor.2021.06.031.
  26. [26] Perez JMQ, Lange JC, Tancrez JS. A multi-hub Express Shipment Service Network Design model with flexible hub assignment. Transportation Research Part E - Logistics and Transportation Review. 2018;120:116-131. DOI: 10.1016/j.tre.2018.10.009.
  27. [27] Zhou J, et al. Logistics network structure of express delivery companies and their self-organization effect under the background of e-commerce: Taking ZTO Express as an example. Economic Geography. 2021;41(2):103-112.
  28. [28] Tang C, Ma X. Spatial pattern and structure of networked logistics connection of cities in China based on express logistics branch data. Progress in Geography. 2020;39(11):1809-1821.
  29. [29] Li J, Wang Z. Network structure and proximity mechanism of urban agglomeration in the Yangtze River Delta: From the perspective of express industry linkage. Areal Research and Development. 2021;40(5):58-63.
  30. [30] Li Y, et al. Structure of e-commerce express logistics network from the perspective of flow space: Take the Pearl River Delta urban agglomeration as example. Areal Research and Development. 2021;40(2):20-26.
  31. [31] Li Y, et al. Spatial structure of China's e-commerce express logistics network based on space of flows. Chinese Geographical Science. 2022. DOI: 10.1007/s11769-022-1322-0.
  32. [32] Tang C, Ma X. Spatial pattern and structure of networked logistics links in Chinese cities -- A study based on courier network data. Progress in Geography. 2020;39(11):1809-1821.
  33. [33] Jingjing X, Mingwei Z. Traffic characteristics of Shanghai courier enterprises' transshipment and distribution center layout. Traffic and Transportation. 2021;34(S1):18-22.
  34. [34] Gonzalez-Varona JM, et al. Reusing newspaper kiosks for last-mile delivery in urban areas. Sustainability. 2020;12(22). DOI: 10.3390/su12229770.
  35. [35] Rosenberg LN, et al. Introducing the shared micro-depot network for last-mile logistics. Sustainability. 2021;13(4). DOI: 10.3390/su13042067.
  36. [36] Kizil KU, Yildiz B. Public transport-based crowd-shipping with backup transfers. Transportation Science. 2022. DOI: 10.1287/trsc.2022.1157.
  37. [37] Pina-Pardo JC, et al. Design of a two-echelon last-mile delivery model. Euro Journal on Transportation and Logistics. 2022;11. DOI: 10.1016/j.ejtl.2022.100079.
  38. [38] Seghezzi A, Mangiaracina R. Investigating multi-parcel crowdsourcing logistics for B2C e-commerce last-mile deliveries. International Journal of Logistics - Research and Applications. 2022;25(3):260-277. DOI: 10.1080/13675567.2021.1882411.
  39. [39] Snoeck A, Winkenbach M. A discrete simulation-based optimization algorithm for the design of highly responsive last-mile distribution networks. Transportation Science. 2022;56(1):201-222. DOI: 10.1287/trsc.2021.1105.
  40. [40] Tejada C, Conway A. What happens before the last mile? Exploring a package's journey. Transportation Research Record. 2022; DOI: 10.1177/03611981221128804.
  41. [41] Ben Ticha H, et al. A branch-and-price algorithm for the vehicle routing problem with time windows on a road network. Networks. 2019;73(4):401-417. DOI: 10.1002/net.21852.
  42. [42] Escobar-Gomez E, et al. A linear programming model with fuzzy arc for route optimization in the urban road network. Sustainability. 2019;11(23):18. DOI: 10.3390/su11236665.
  43. [43] Luo CW, et al. Delivery route optimization with automated vehicle in smart urban environment. Theoretical Computer Science. 2020;836:42-52. DOI: 10.1016/j.tcs.2020.05.050.
  44. [44] Fengjie X, Wentian C. Weighted express network robustness analysis and optimization. Systems Engineering Theory and Practice. 2016;36(09):2391-2399.
  45. [45] Jin Z, Dong Q. Research on the robustness of express delivery network and its improvement countermeasures. Journal of Transportation Engineering and Information. 2018;16(03):7-13+58.
  46. [46] Nengzhi M, Qiuping K. Destructiveness measurement of urban express networks based on node importance network structure entropy. Integrated Transportation. 2020;42(03):108-113.
  47. [47] Mu N, Kang Q, Jia C. Vulnerability assessment of urban express networks under influence of emergencies. China Safety Science Journal (CSSJ). 2020;30(12):125-132.
  48. [48] Chun F, et al. Express network vulnerability analysis based on complex network theory. Journal of Transportation Engineering and Information. 2020;18(01):9-15.
  49. [49] Changjiang Z, Huan H, Muqing D. Network structure design of multimodal express transportation considering hub failure. Journal of Jilin University (Engineering Edition). 2022:1-10. DOI: 10.13229/j.cnki.jdxbgxb20211128.
  50. [50] Ozarik SS, et al. Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence. Transportation Research Part E - Logistics and Transportation Review. 2021;148. DOI: 10.1016/j.tre.2021.102263.
  51. [51] Pu X, Li X. Multi-objective Low carbon MDVRP optimization based on e-commerce commitment delivery mechanism. Chinese Journal of Management Science. 2021;29(8):57-66.
  52. [52] Simonia MD, Kutanoglub E, Claudela CG. Optimization and analysis of a robot-assisted last mile delivery system. Transportation Research Part E - Logistics and Transportation Review. 2020;142. DOI: 10.1016/j.tre.2020.102049.
  53. [53] Ren CX, et al. Urban regional logistics distribution path planning considering road characteristics. Discrete Dynamics in Nature and Society. 2020;2020:15. DOI: 10.1155/2020/2413459.
  54. [54] Poku-Boansi M. Path dependency in transport: A historical analysis of transport service delivery in Ghana. Case Studies on Transport Policy. 2020;8(4):1137-1147. DOI: 10.1016/j.cstp.2020.07.003.
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