Circle Line Optimization of Shuttle Bus in Central Business District without Transit Hub

  • Baozhen Yao Dalian University of Technology
  • Qingda Cao Dalian University of Technology
  • Lu Jin Dalian University of Technology
  • Mingheng Zhang Dalian University of Technology
  • Yibing Zhao Dalian University of Technology
Keywords: Central Business District, shuttle bus, transfer, genetic algorithm, bi-objective model,


The building density of Central Business District (CBD) is usually high. Land for a bus terminal is insufficient. In this situation, passengers in CBD have to walk far to take a bus, or take a long time to wait for a taxi. To solve this problem, this paper proposes an indirect approach: the design of a circle line of shuttle bus as a dynamic bus terminal in CBD. The shuttle bus can deliver people to the bus station through a circle line. This approach not only reduces the traffic pressure in CBD, but also saves travel time of the passenger. A bi-objective model is proposed to design a circle line of a shuttle bus for CBD. The problem is solved by non-dominated sorting genetic algorithm (NSGA-II). Furthermore, the Dalian city in China has been chosen as the case study to test the proposed method. The results indicate that the method is effective for circle line optimization of shuttle bus in central business district without a bus terminal.

Author Biographies

Baozhen Yao, Dalian University of Technology
School of Automotive Engineering
Qingda Cao, Dalian University of Technology
School of Automotive Engineering
Lu Jin, Dalian University of Technology
School of Automotive Engineering
Mingheng Zhang, Dalian University of Technology
School of Automotive Engineering
Yibing Zhao, Dalian University of Technology
School of Automotive Engineering


Warade RK. The accessibility and development impacts of new transit infrastructure: the circle line in Chicago. Massachusetts Institute of Technology; 2007.

Zhibin J, Jia G, Ruihua X. Circle rail transit line timetable scheduling using Rail TPM. WIT Transactions on The Built Environment. 2010;114:945-952.

Chen B Y, Yuan H, Li Q, et al. Spatiotemporal data model for network time geographic analysis in the era of big data. International Journal of Geographical Information Science. 2016;30(6):1041-1071.

Wirasinghe SC. Nearly optimal parameters for a rail/feeder-bus system on a rectangular grid. Transportation Research Part A: General. 1980;14(1):33-40.

Bookbinder JH, Desilets A. Transfer optimization in a transit network. Transportation science. 1992;26(2):106-118.

Martins CL, Pato MV. Search strategies for the feeder bus network design problem. European Journal of Operational Research. 1998;106(2):425-440.

Quadrifoglio L, Li X. A methodology to derive the critical demand density for designing and operating feeder transit services. Transportation Research Part B: Methodological. 2009;43(10):922-935.

Ceder AA. Integrated smart feeder/shuttle transit service: simulation of new routing strategies. Journal of Advanced Transportation. 2013;47(6):595-618.

Dikas G, Minis I. Scheduled paratransit transport systems. Transportation Research Part B: Methodological. 2014;67:18-34.

Braekers K, Caris A, Janssens G K. Exact and meta-heuristic approach for a general heterogeneous dial-a-ride problem with multiple depots. Transportation Research Part B: Methodological. 2014;67:166-186.

Lai X, Li J, Li Z. A Subpath-based Logit Model to Capture the Correlation of Routes. Promet – Traffic & Transportation. 2016;28(3):225-234.

Saka AA. Model for Determining Optimum Bus-Stop Spacing in Urban Areas. Journal of Transportation Engineering. 2001;127(3):195-199.

Chien SI, Qin Z. Optimization of bus stop locations for improving transit accessibility. Transportation planning and Technology. 2004;27(3):211-227.

Shafahi Y, Khani A. A practical model for transfer optimization in a transit network: Model formulations and solutions. Transportation Research Part A: Policy and Practice. 2010;44(6):377-389.

Sivakumaran K, Li Y, Cassidy M J, et al. Cost-saving properties of schedule coordination in a simple trunkand-feeder transit system. Transportation Research Part A: Policy and Practice. 2012;46(1):131-139.

Sumalee A, Uchida K, Lam WHK. Stochastic multi-modal transport network under demand uncertainties and adverse weather condition. Transportation Research Part C: Emerging Technologies. 2011;19(2):338-350.

hibin J, Qixiang H. A Service-based Method to Generate Shuttle Bus Timetable in Accordance with Rail Transit Timetable. Procedia-Social and Behavioral Sciences. 2013;96:1890-1897.

Fu X, Lam WHK, Chen BY. A reliability-based traffic assignment model for multi-modal transport network under demand uncertainty. Journal of Advanced Transportation. 2014;48(1):66-85.

Liu Y, Bunker J, Ferreira L. Transit Users’ Route-Choice Modelling in Transit Assignment: A Review. Transport Reviews. 2010;30(6):753-769.

Ceder A. Optimal design of transit short-turn trips. Transportation Research Record. 1989;(1221):9-22.

Cepeda M, Cominetti R, Florian M. A frequency-based assignment model for congested transit networks with strict capacity constraints: characterization and computation of equilibria. Transportation research part B: Methodological. 2006;40(6):437-459.

İnanlı A, Ünsal B, Türsel Eliiyi D. Route Optimization for the Distribution Network of a Confectionary Chain. Promet – Traffic & Transportation. 2015;27(6):497-503.

Yu B, Kong L, Sun Y, Yao BZ, Gao ZY. A bi-level programming for bus lane network design. Transportation Research Part C: Emerging Technologies. 2015;55:310-327.

Yu B, Peng Z, Wang K, et al. An optimization method for planning the lines and the operational strategies of waterbuses: the case of Zhoushan city. Operational Research. 2015;15(1):25-49.

Yao BZ, Chen C, Cao QD, Jin L, Zhang MH, Zhu HB, Yu B. Short-term traffic speed prediction for an urban corridor. Computer-Aided Civil and Infrastructure Engineering. 2017:32(2):154-169.

Shrivastava P, O’Mahony M. A model for development of optimized feeder routes and coordinated schedules—A genetic algorithms approach. Transport policy. 2006;13(5):413-425.

Martínez LM, Eiró T. An optimization procedure to design a minibus feeder service: an application to the sintra rail line. Procedia-Social and Behavioral Sciences. 2012;54:525-536.

Ibeas Á, dell’Olio L, Alonso B, et al. Optimizing bus stop spacing in urban areas. Transportation research part E: Logistics and Transportation Review. 2010;46(3):446-458.

Ibeas A, Alonso B, dell’Olio L, et al. Bus Size and Headways Optimization Model Considering Elastic Demand. Journal of Transportation Engineering. 2013;140(4):04013021.

Szeto WY, Wu Y. A simultaneous bus route design and frequency setting problem for Tin Shui Wai, Hong Kong. European Journal of Operational Research. 2011;209(2):141-155.

Ruisanchez F, Ibeas A. Design of a tabu search algorithm for assigning optimal bus sizes and frequencies in urban transport services. Journal of Advanced Transportation. 2012;46(4):366-377.

Ngamchai S, Lovell DJ. Optimal time transfer in bus transit route network design using a genetic algorithm. Journal of Transportation Engineering. 2003;129(5):510-521.

Chen BY, Lam WHK, Sumalee A, et al. Reliable shortest path finding in stochastic networks with spatial correlated link travel times. International Journal of Geographical Information Science. 2012;26(2):365-386.

Srinivas N, Deb K. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation. 1994;2(3):221-248.

Deb K, Agrawal S, Pratap A, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Proceedings of the 6th International Conference on Parallel Problem Solving from Nature – PPSN VI; 2000 Sep 18-20; Paris, France. Berlin, Heidelberg: Springer; 2000. p. 849-858.

Shimamoto H, Kurauchi F, Iida Y, et al. Evaluating public transit congestion mitigation measures using a passenger assignment model. Journal of the Eastern Asia Society for Transportation Studies. 2005;6:2076-2091.

Kwan CM, Chang CS. Timetable synchronization of mass rapid transit system using multiobjective evolutionary approach. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2008;38(5):636-648.

Lau HCW, Chan TM, Tsui WT, et al. A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem. Expert Systems with Applications. 2009;36(4):8255-8268.

Song Y, Ma J, Guan W, et al. A multi-objective model for regional bus timetable based on NSGA-II. Proceedings of the 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE); 2012 May 25-27; Zhangjiajie, China; 2012;2:185-188.

Khoo HL, Teoh LE, Meng Q. A bi-objective optimization approach for exclusive bus lane selection and scheduling design. Engineering Optimization. 2014;46(7):987-1007.

How to Cite
Yao B, Cao Q, Jin L, Zhang M, Zhao Y. Circle Line Optimization of Shuttle Bus in Central Business District without Transit Hub. Promet - Traffic&Transportation. 2017;29(1):45-. DOI: 10.7307/ptt.v29i1.2015