The aim of this work is to investigate the simplifica-tion of public transport networks (PTNs) for megacities and the optimisation of route planning based on the de-mand density of complex networks. A node deletion rule for network centre areas and a node merging rule for net-work border areas in the PTN are designed using the de-mand density of complex networks. A transit route plan-ning (TRP) model is established, which considers the demands of direct passengers, transfer passengers at the same stop and transfer passengers at different stops, and aims at maximising the transit demand density of a PTN. An optimisation process for TRP is developed based on the ant colony optimisation (ACO). The proposed method was validated through a sample application in Handan City in China. The results indicate that urban PTNs can be simplified while retaining their local attributes to a great extent. The hierarchical structure of the network is more obvious, and the layer-by-layer planning of routes can be effectively used in TRP. Moreover, the operating efficiency and service level of urban PTNs can be en-hanced effectively.
He H, et al. Providing public transport priority in the perimeter of urban networks: A bimodal strategy. Trans-portation Research Part C: Emerging Technologies. 2019;107: 171-192. doi: 10.1016/j.trc. 2019.08.004.
Ceder A, Wilson N. Bus network design. Transporta-tion Research Part B: Methodological. 1986;20(4): 331-344. doi: 10.1016/ 0191-2615(86)90047.
Zhou K, et al. Multi objective optimization meth-od of public transit networks based on travel behav-ior. Journal of Highway & Transportation Research & Development. 2015;9(4): 71-77. doi: 10.1016/ JHTRCQ.0000473.
Carrese S, Gori S. An urban bus network design pro-cedure. Transportation Planning. 2002;1(6): 177-195. doi: 10.1007/ 0-3-6-48220-7_11.
Oloveora Arbex R, Barbieri da Cunha C. Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm. Transportation Research Part B: Methodological. 2015;81: 355-376. doi: 10.1016/ j.trb.2015.06.014.
Lai D, Caliskan Demirag O, Leung J. A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph. Transportation Research Part E: Lo-gistics and Transportation Review. 2016;86: 32-52. doi: 10.1016/ j.tre.2015.12.001.
Dong X, Dong W, Cai Y. Ant colony optimisation for coloured travelling salesman problem by multi-task learning. IET Intelligent Transport Systems. 2018;12(8): 774-782. doi: 10.1016/ iet-its.2016.0282.
Zhang Z, et al. Spatial-temporal traffic flow pattern identification and anomaly detection with dictio-nary-based compression theory in a large-scale urban network. Transportation Research Part C: Emerging Technologies. 2016;7(10): 284-302. doi: 10.1016/j.trc.2016.08.0 06.
Li M, et al. Identifying essential proteins based on sub-network partition and prioritization by integrat-ing subcellular localization information. Journal of Theoretical Biology. 2018;447(3): 65-73. doi: 10.1016/ j. jtbi.2018.03.029.
Zhang W, Xu W. Simulation-based robust optimization for the schedule of single-direction bus transit route: The design of experiment. Transportation Research Part E: Logs and Transportation Review. 2017;106(10): 203-230. doi: 10.1016/ j.tre.2017.08.001.
Yan G, et al. Efficient routing on complex networks. Physical Review E: Statistical Nonlinear & Soft Mat-ter Physics. 2005;73(2): 1-5. doi: 10.1103/ Phys-RevE.73.046108.
Zhang L, et al. An auxiliary optimization method for complex public transit route network based on link prediction. Modern Physics Letters B. 2018;32(5): 1850066. doi: 10.1142/ S0217984918500665.
Jia G, Ma R, Hu Z. Urban transit network properties evaluation and optimization based on complex network theory. Sustainability. 2019;11(7): 1-16. doi: 10.3390/ su11072007.
Clauset A, Newman M, Moore C. Finding communi-ty structure in very large networks. Physical Review E. 2004;70(2): 066111. doi: 10.1103/ PhysRevE.70.06611.
Rolls D, Robins G. Minimum distance estimators of population size from snowball samples using condi-tional estimation and scaling of exponential random graph models. Computational Stats & Data Analysis. 2017;116(12): 32-48. doi: 10.1016/ j.csda.2017.07.004.
Boobalan P, Lopez D. Graph clustering using k-Neigh-bourhood Attribute Structural similarity. Applied Soft Computing. 2016;47(6): 216-223. doi: 10.1016/j.asoc.2016.05.028.
Blagus N, Šubelj L, Bajec M. Assessing the effective-ness of real-world network simplification. Physica A: Statal Mechanics and its Applications. 2014;413(7): 134-146. doi: 10.1016/ j.physa.2014.06.065.
Gallos L, Song C, Makse H. A review of fractality and self-similarity in complex networks. Physica A: Statis-tical Mechanics and its Application. 2016;386 (2): 686-691. doi: 10.1016/ j.physa.2007.07.069.
Šubelj L, Bajec M. Robust network community detec-tion using balanced propagation. European Physical Journal B. 2011;81(3): 353-362. doi: 10.1140/epjb/e2011-10979-2.
Lownes N, Machemehl R. Exact and heuristic methods for public transit circulator design. Transportation Re-search Part B: Methodological. 2010;44(2): 309-318. doi: 10.1016/j.trb.200 9.07.010.
Yu B, et al. Transit route network design-maximizing direct and transfer demand density. Transportation Re-search Part C: Emerging Technologies. 2012;22(12): 58-75. doi: 10.1016/j.trc.2011.12.003.
Ibarra-Rojas OJ, et al. Planning, operation, and control of bus transport systems: A literature review. Transpor-tation Research Part B: Methodological. 2015;77(7): 38-75. doi: 10.1016/j.trb.2015. 03.002.
Wang D, Nayan A, Szeto WY. Optimal bus service design with limited stop services in a travel corridor. Transpor-tation Research Part E: Logs and Transportation Review. 2018;111(3): 70-86. doi: 10.1016/ j.tre.2018.01.007.
Li J, Zheng P, Zhang W. Identifying the spatial distribu-tion of public transportation trips by node and community characteristics. Transportation Planning and Technology. 2020;43(3): 1-16. doi: 10.1080/03081060.2020.1735776.
Manser P, et al. Designing a large-scale public transport network using agent-based microsimulation. Transporta-tion Research Part A: Policy and Practice. 2020;137(7): 1-15. doi: 10.1016/j.tra.2020.04.011.
Yang Z, Yu B, Cheng C. A parallel ant colony algorithm for bus network optimization. Computer-aided Civil and Infrastructure Engineering. 2007;22(1): 44-55. doi: 10.1111/j.146 7-8667.2006.00469.x.
Agrawal J, Mathew T. Transit route network design us-ing parallel genetic algorithm. Journal of Computing in Civil Engineering. 2004;18(3): 248-256. doi: 10.1061/(ASCE)0887-3801(2004)18:3(248).
Dorigo M, Maniezzo V, Colorni A. Ant system: Optimi-zation by a colony of cooperating agents. IEEE Trans-actions on Systems Man and Cybernetics. 1996;26(1): 29-41. doi: 10.1109/3477.484436.
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