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Multi-mode vehicle scheduling and routing for surging passenger flow management: from the perspective of urban traffic brain

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Abstract

Some nodes in the transport system, especially the large transport hubs, are prone to encounter surging passenger flow, which seriously affects the stable operation of the transport system. The emergence of urban traffic brain makes it possible to simultaneously schedule multi-mode vehicles for the efficient management of surging passenger flow. From the perspective of urban traffic brain, this paper studies the surging passenger flow management problem through the joint scheduling of multi-mode vehicles, including taxi, online ride-hailing, and customized bus, with consideration of the personalized demand of passengers. Two mathematical models are formulated. The first model devotes to determine the number of scheduled vehicles of each transport mode with the goal of minimizing the scheduling cost and penalty cost due to the unsatisfied personalized demand. The second model optimizes the routes of customized buses, which is solved by a simulated annealing (SA) algorithm. The experiments on the real-life data set of Beijing Capital International Airport show that (1) the gap between SA algorithm and CPLEX solver is barely 0.23% for a small-scale case; (2) for large-scale cases, SA algorithm could find a satisfactory solution within acceptable computing time, and compared with the clustering-based CPLEX, SA algorithm can reduce the travel cost by 19.94%.

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There is no availability of data needed for the manuscript.

References

  • Allahyari S, Salari M, Vigo D (2015) A hybrid metaheuristic algorithm for the multi-depot covering tour vehicle routing problem. Eur J Oper Res 242(3):756–768

    Article  MathSciNet  MATH  Google Scholar 

  • Anjos MF, Vieira M (2019) Mathematical optimization approaches for facility layout problems: the state-of-the-art and future research directions. Oper Res 59(1–2):89–91

    Google Scholar 

  • Chen Y, An K (2021) Integrated optimization of bus bridging routes and timetables for rail disruptions. Eur J Oper Res 295(2):484–498

    Article  MathSciNet  MATH  Google Scholar 

  • Dou X, Meng Q, Guo X (2015) Bus schedule coordination for the last train service in an intermodal bus-and-train transport network. Transp Res C 60:360–376

    Article  Google Scholar 

  • Gu W, Yu J, Ji Y, Zheng Y, Zhang HM (2018) Plan-based flexible bus bridging operation strategy. Transp Res C 91:209–229

    Article  Google Scholar 

  • Guo R, Guan W, Zhang W (2018) Route design problem of customized buses: Mixed integer programming model and case study. Transp Res A 144:1–14

    Google Scholar 

  • Huang Z, Wang D, Yin Y, Li X (2021) A spatiotemporal bidirectional attention based ride-hailing demand prediction model: A case study in Beijing during COVID-19. IEEE Trans Intell Transp. https://doi.org/10.1109/TITS.2021.3122541

    Article  Google Scholar 

  • Jin JG, Teo KM, Odoni AR (2016) Optimizing bus bridging services in response to disruptions of urban transit rail networks. Transp Sci 50:790–804

    Article  Google Scholar 

  • Kang L, Zhu X, Sun H, Wu J, Gao Z, Hu B (2019) Last train timetabling optimization and bus bridging service management in urban railway transit networks. Omega Int J Manag 84:31–44

    Article  Google Scholar 

  • Kang L, Li H, Sun H, Wu J, Cao Z, Buhigiro N (2021) First train timetabling and bus service bridging in intermodal bus-and-train transit networks. Transp Res B 149:443–462

    Article  Google Scholar 

  • Li X (2019) Intelligent transportation systems in big data. J Ambient Intell Human Comput 10:305–306

    Article  Google Scholar 

  • Li Y, Li X, Zhang S (2021) Optimal pricing of customised bus services and ride-sharing based on a competitive game model. Omega Int J Manag 103:102413

    Article  Google Scholar 

  • Liang J, Wu J, Qu Y, Yin HD, Qu XB, Gao ZY (2019) Robust bus bridging service design under rail transit system disruptions. Transp Res E 132:97–116

    Article  Google Scholar 

  • Ma H, Li X, Yu H (2020) Single bus line timetable optimization with big data: a case study in Beijing. Inf Sci 536:53–66

    Article  MathSciNet  Google Scholar 

  • Pan G, Li KL, Ouyang AJ, Li KQ (2016) Hybrid immune algorithm based on greedy algorithm and delete-cross operator for solving TSP. Soft Comput 20(2):555–566

    Article  Google Scholar 

  • Pereira VC, Bish DR (2015) Scheduling and routing for a bus-based evacuation with a constant evacuee arrival rate. Transp Sci 49(4):853–867

    Article  Google Scholar 

  • Salehnia N, Salehnia N, Ansari H, Kolsoumi S, Bannayan M (2019) Climate data clustering effects on arid and semi-arid rainfed wheat yield: a comparison of artificial intelligence and k-means approaches. Int J Biometeorol 63(7):861–872

    Article  Google Scholar 

  • Tian S (2021) A short-turning strategy for the management of bus bunching considering variable spatial-temporal running time. J Uncertain Syst 14(03):2150020

    Article  Google Scholar 

  • Tian S, Li X, Liu J, Ma H, Yu H (2021) A short-turning strategy to alleviate bus bunching. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02891-2

    Article  Google Scholar 

  • Wang Y, Guo J, Currie G, Ceder AA, Dong W, Pender B (2014) Bus bridging disruption in rail services with frustrated and impatient passengers. IEEE T Intell Transp 15:2014–2023

    Article  Google Scholar 

  • Wang X, Dong J, Han T, Ruan J (2019) The optimization of cold chain delivery routes considering carbon emission and temporal-spatial distance. J Syst Eng 34(4):555–565

    MATH  Google Scholar 

  • Xu X, Hao J, Zheng Y (2020) Multi-objective artificial bee colony algorithm for multi-stage resource leveling problem in sharing logistics network. Comput Ind Eng 142(4):106338

    Article  Google Scholar 

  • Xu X, Wang C, Zhou P (2021) GVRP considered oil-gas recovery in refined oil distribution: from an environmental perspective. Int J Prod Econ 235:108078

    Article  Google Scholar 

  • Yan Z, Ismail H, Chen L, Zhao X, Wang L (2019) The application of big data analytics in optimizing logistics: a developmental perspective review. J Data Inf Manag 1:33–43

    Article  Google Scholar 

  • Yang M, Liu Y, Yang G (2020) Robust optimization for a multiple-priority emergency evacuation problem under demand uncertainty. J Data Inf Manag 2:185–199

    Article  Google Scholar 

  • Yu D, Liu G, Guo M, Liu X (2018) An improved k-medoids algorithm based on step increasing and optimizing medoids. Expert Syst Appl 92:464–473

    Article  Google Scholar 

  • Yu VF, Jewpanya P, Redi A, Tsao YC (2021) Adaptive neighborhood simulated annealing for the heterogeneous fleet vehicle routing problem with multiple cross-docks. Comput Oper Res 129(2):105205

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang S, Lo HK (2020) Metro disruption management: Contracting substitute bus service under uncertain system recovery time. Transp Res C 110:98–122

    Article  Google Scholar 

  • Zhao X, Ji K, Xu P, Qian WW, Shan XN (2020) A round-trip bus evacuation model with scheduling and routing planning. Transp Res A 137:285–300

    Google Scholar 

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (Nos. 71722007 & 71931001), the Key Program of NSFC-FRQSC Joint Project (NSFC No. 72061127002 and FRQSC No. 295837), the Funds for First-class Discipline Construction (XK1802-5) and the Fundamental Research Funds for the Central Universities (buctrc201926).

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Authors and Affiliations

  1. School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China

    Xiang Li, Shengnan Tian & Hongguang Ma

  2. Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China

    Xiang Li

  3. Beijing Intelligent Transportation Development Center, Beijing, 100161, China

    Haitao Yu

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  1. Xiang Li
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  2. Shengnan Tian
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  3. Hongguang Ma
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  4. Haitao Yu
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Correspondence to Hongguang Ma.

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Li, X., Tian, S., Ma, H. et al. Multi-mode vehicle scheduling and routing for surging passenger flow management: from the perspective of urban traffic brain. J Ambient Intell Human Comput 14, 9781–9791 (2023). https://doi.org/10.1007/s12652-022-03852-7

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  • DOI: https://doi.org/10.1007/s12652-022-03852-7

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