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An optimization method for planning the lines and the operational strategies of waterbuses: the case of Zhoushan city

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Abstract

Marine transportation can provide more comfortable transit service compared to road-based public transportation. This paper proposes an optimization method for planning the lines and the operational strategies of waterbuses. In this method, the candidate hub ports are selected first, and then a two-stage optimization model is constructed. The model comprehensively considers the interests of both the passengers and the operators by optimizing the lines and the operational strategies of the waterbuses. To solve the model, a shuffled genetic algorithm is proposed. Furthermore, Zhoushan city in China has been chosen as the case study to test the proposed method. The results show that the optimized waterbus lines and operational strategies are better than those that are currently used by the water transportation system in Zhoushan city.

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References

  • Agrawal J, Mathew TV (2004) Transit route network design using parallel genetic algorithm. Journal of Computing in Civil Engineering 18(3):248–256

    Article  Google Scholar 

  • Baaj MH, Mahmassani HS (1995) Hybrid route generation heuristic algorithm for the design of transit networks. Transp Res Part C 3(1):31–50

    Article  Google Scholar 

  • Barth M, Boriboonsomsin K (2008) Real-World CO2 Impacts of Traffic Congestion. Transp Res Rec 2008(2058):163–171

    Article  Google Scholar 

  • Bell JE, McMullen PR (2004) Ant colony optimization techniques for the vehicle routing problem. Adv Eng Inform 18(1):41–48

    Article  Google Scholar 

  • Bielli M, Caramia M, Carotenuto P (2002) Genetic algorithms in bus network optimization. Transportation Research Part C: Emerging Technologies 10(1):19–34

    Article  Google Scholar 

  • Bin YU, Hanbing ZHU, Wanjun CAI, Ning MA, Baozhen YAO (2013) Two-phase Optimization Approach to Transit Hub Location–the Case of Dalian. J Transp Geogr 33:62–71

    Article  Google Scholar 

  • Ceder A, Wilson NHM (1986) Bus network design. Transp Res Part B 20(4):331–344

    Article  Google Scholar 

  • Chakroborty P (2003) Genetic algorithms for optimal urban transit network design. Computer-Aided Civil and Infrastructure Engineering 18(3):184–200

    Article  Google Scholar 

  • Chakroborty P, Deb K, Sharma RK (2001) Optimal fleet size distribution and scheduling of urban transit systems using genetic algorithms. Transp. Plan. Technol. 24(3):209–225

    Article  Google Scholar 

  • Chien S, Yang Z, Hou E (2001) Genetic algorithm approach for transit route planning and design. Journal of Transportation Engineering 127(3):200–207

    Article  Google Scholar 

  • Cordeau JF, Toth P, Vigo D (1998) A survey of optimization models for train routing and scheduling[J]. Transportation science 32(4):380–404

    Article  Google Scholar 

  • Duan QY, Sorooshian S, Gupta VK (1994) Optimal use of the SCE-UA global optimization method for calibrating watershed models. J Hydraul Eng 158(1):265–284

    Google Scholar 

  • Dubois D, Bel G, Libre M (1979) A set of methods in transportation network synthesis and analysis. Journal of Operational Research Society 30(9):797–808

    Article  Google Scholar 

  • Ferreira LA, Antunes A, Picado-Santos L (2000) Using genetic algorithms on a PMS segment optimization mode. Proc. 1st European Pavement Management Systems, Conf

  • Fu Y, Gao Z, Li K (2009) Optimization method of energy saving train operation for railway network. J Transp Syst Eng Inf Technol, 94

  • Fwa TF, Chan WT, Tan CY (1994) Optimal programming by genetic algorithms for pavement management. Transportation Research Record 1455, Transportation Research Board, Washington, 31–41

  • Guan JF, Yang H, Wirasinghe SC (2006) Simultaneous optimization of transit line configuration and passenger line assignment. Transp Res B 40(10):885–902

    Article  Google Scholar 

  • Haase K, Kolisch R LINGO. Operations-Research-Spektrum 19.1 (1997):1–4

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, Mich

    Google Scholar 

  • Jeremy JB, Tom VM (2011) Intelligent Agent Optimization of Urban Bus Transit System Design. Journal of Computing in Civil Engineering 25(5):357–369

    Article  Google Scholar 

  • Keiji S, Keiki T (2007) Waterbus Route Optimization by Pittsburgh-style Learning Classifier System. PROCEEDINGS OF SICE ANNUAL CONFERENCE 1–8:1150–1154

    Google Scholar 

  • Keiki T, Takahiro M, Daisuke W (2007) Exploring Quantitative Evaluation Criteria for Service and Potentials of New Service in Transportation: Analyzing Transport Networks of Railway, Subway and Waterbus. Lect Notes Comput Sci 4881:1122–1130

    Article  Google Scholar 

  • Kunimatsu T, Hirai C, Tomii N (2012) Train timetable evaluation from the viewpoint of passengers by microsimulation of train operation and passenger flow. Elect Eng Jpn 1814

  • Park SJ (2005) Bus network scheduling with genetic algorithms and simulation. MS thesis, University. of Maryland

  • Pattnaik SB, Mohan S, Tom VM (1998) Urban bus transit route network using genetic algorithm. Journal of Transportation Engineering 124(4):368–375

    Article  Google Scholar 

  • Schöbel A, Scholl S (2006) Line planning with minimal traveling time. In: 5th Workshop on Algorithmic Methods and Models for Optimization of Railways

  • Shih MC, Mahmassani HS (1995) Vehicle sizing model for bus transit networks. Transp Res Rec 1452:35–41

    Google Scholar 

  • Tom VM, Mohan S (2003) Transit route network design using frequency coded genetic algorithm. J. Transp. Eng. 129(2):186–195

    Article  Google Scholar 

  • Tongchim S, Chongstitvatana P (2002) Parallel genetic algorithm with parameter adaptation. Information Processing Letters. 82:47–54

    Article  Google Scholar 

  • Van Nes R, Hamerslag R, Immerse BH (1998) Design of public transportation networks. Transp Res Rec 1202:74–82

    Google Scholar 

  • Wren A, Wern DO (1995) A genetic algorithm for public driver scheduling. Comput Op Res 22(1):101–110

  • Yan S, Tang C (2008) An integrated framework for intercity bus scheduling under stochastic bus travel times. Transp. Sci. 42(3):318–335

    Article  Google Scholar 

  • Yao BZ, Yang CY, Yao JB (2010) Tunnel Surrounding Rock Displacement Prediction Using Support Vector Machine. International Journal of Computational Intelligence Systems 3(6):843–852

    Article  Google Scholar 

  • Yao BZ, Yang CY, Yao JB, Hu JJ, Sun J (2011) An improved ant colony optimization for flexible job shop scheduling problems. Adv Sci Lett 4(6-7):2127–2131(5)

    Article  Google Scholar 

  • Yao BZ, Hu P, Zhang MH, Wang S (2013) Artificial Bee Colony Algorithm with Scanning Strategy for Periodic Vehicle Routing Problem. SIMULATION: transactions of The Society for Modeling and Simulation. International. 89(6):762–770

    Google Scholar 

  • Yao BZ, Hu P, Lu XH, Gao JJ, Zhang MH (2014a) Transit network design based on travel time reliability. Transp Res Part C 43:233–248

    Article  Google Scholar 

  • Yao BZ, Hu P, Zhang MH, Jin MQ (2014b) A Support Vector Machine with the Tabu Search Algorithm For Freeway Incident Detection. Int J Appl Math Comput Sci 24(2):397–404

    Article  Google Scholar 

  • Yao BZ, Hu P, Zhang MH, Tian XM (2014c) Improved Ant Colony Optimization for Seafood Product Delivery Routing Problem. Promet-Traffic & Transportation 26(1):1–10

    Google Scholar 

  • Ye L, Yang DY, He N (2007) Application of stated preference survey method in waterbus system design. Int Conf Transp Eng, Part B 40:885–902

    Google Scholar 

  • Yu B, Yang ZZ, Yao JB (2010) Genetic Algorithm For Bus Frequency Optimization. Journal of Transportation Engineering 136(6):576–583

    Article  Google Scholar 

  • Yu B, Yang ZZ, Sun XS, Yao BZ, Zeng QC, Jeppesen E (2011) Parallel Genetic Algorithm in Bus Route Headway Optimization. Appl Soft Comput 11(8):5081–5091

    Article  Google Scholar 

  • Yu B, Yang ZZ, Jin PH, Wu SH, Yao BZ (2012a) Transit route network design-maximizing direct and transfer demand density. Transp Res Part C 22:58–75

    Article  Google Scholar 

  • Yu B, Yang ZZ, Shan LI (2012b) Real-Time Partway Deadheading Strategy Based on Transit Service Reliability Assessment. Transp Res Part A 46(8):1265–1279

    Google Scholar 

  • Zhao F (2004) Transit network optimization—minimizing transfers and optimizing route directness. Journal of Public Transportation 7(1):63–82

    Google Scholar 

  • Zhou X, Zhong M (2005) Bicriteria train scheduling for high-speed passenger railroad planning applications [J]. Eur J Oper Res 167(3):752–771

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in National Natural Science Foundation of China 51108053 and 51208079, the Trans-Century Training Program Foundation for Talents from the Ministry of Education of China NCET-12-0752, Liaoning Excellent Talents in University LJQ2012045 and the Fundamental Research Funds for the Central Universities 3013-852019.

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Authors and Affiliations

  1. Transportation Management College, Dalian Maritime University, Dalian, 116026, People’s Republic of China

    Bin Yu, Zixuan Peng, Keming Wang, Lu Kong & Yao Cui

  2. School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Baozhen Yao

Authors
  1. Bin Yu
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  2. Zixuan Peng
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  3. Keming Wang
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  4. Lu Kong
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  5. Yao Cui
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  6. Baozhen Yao
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Correspondence to Baozhen Yao.

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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. Oper Res Int J 15, 25–49 (2015). https://doi.org/10.1007/s12351-015-0168-y

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  • DOI: https://doi.org/10.1007/s12351-015-0168-y

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