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Travel itinerary problem

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  • Li, Xiang
  • Zhou, Jiandong
  • Zhao, Xiande

Abstract

In this study, we propose a travel itinerary problem (TIP) which aims to find itineraries with the lowest cost for travelers visiting multiple cities, under the constraints of time horizon, stop times at cities and transport alternatives with fixed departure times, arrival times, and ticket prices. First, we formulate the TIP into a 0–1 integer programming model. Then, we decompose the itinerary optimization into a macroscopic tour (i.e., visiting sequence between cities) selection process and a microscopic number (i.e., flight number, train number for each piece of movement) selection process, and use an implicit enumeration algorithm to solve the optimal combination of tour and numbers. By integrating the itinerary optimization approach and Web crawler technology, we develop a smart travel system that is able to capture online transport data and recommend the optimal itinerary that satisfies travelers’ preferences in departure time, arrival time, cabin class, and transport mode. Finally, we present case studies based on real-life transport data to illustrate the usefulness of itinerary optimization for minimizing travel cost, the computational efficiency of the implicit enumeration algorithm, and the feasibility of the smart travel system.

Suggested Citation

  • Li, Xiang & Zhou, Jiandong & Zhao, Xiande, 2016. "Travel itinerary problem," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 332-343.
  • Handle: RePEc:eee:transb:v:91:y:2016:i:c:p:332-343
    DOI: 10.1016/j.trb.2016.05.013
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    References listed on IDEAS

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    1. Bar-Gera, Hillel & Boyce, David, 2003. "Origin-based algorithms for combined travel forecasting models," Transportation Research Part B: Methodological, Elsevier, vol. 37(5), pages 405-422, June.
    2. Koppelman, Frank S. & Coldren, Gregory M. & Parker, Roger A., 2008. "Schedule delay impacts on air-travel itinerary demand," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 263-273, March.
    3. R. Montemanni & J. Barta & M. Mastrolilli & L. M. Gambardella, 2007. "The Robust Traveling Salesman Problem with Interval Data," Transportation Science, INFORMS, vol. 41(3), pages 366-381, August.
    4. Yvan Dumas & Jacques Desrosiers & Eric Gelinas & Marius M. Solomon, 1995. "An Optimal Algorithm for the Traveling Salesman Problem with Time Windows," Operations Research, INFORMS, vol. 43(2), pages 367-371, April.
    5. Alejandro Toriello & William B. Haskell & Michael Poremba, 2014. "A Dynamic Traveling Salesman Problem with Stochastic Arc Costs," Operations Research, INFORMS, vol. 62(5), pages 1107-1125, October.
    6. Laporte, Gilbert & Chapleau, Suzanne & Landry, Philippe-Eric & Mercure, Hélène, 1989. "An algorithm for the design of mailbox collection routes in urban areas," Transportation Research Part B: Methodological, Elsevier, vol. 23(4), pages 271-280, August.
    7. Graham, Derek & Nuttle, Henry L. W., 1986. "A comparison of heuristics for a school bus scheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 20(2), pages 175-182, April.
    8. Jiancheng Long & Hai-Jun Huang & Ziyou Gao & W. Y. Szeto, 2013. "An Intersection-Movement-Based Dynamic User Optimal Route Choice Problem," Operations Research, INFORMS, vol. 61(5), pages 1134-1147, October.
    9. Ann M. Campbell & Barrett W. Thomas, 2008. "Probabilistic Traveling Salesman Problem with Deadlines," Transportation Science, INFORMS, vol. 42(1), pages 1-21, February.
    10. Harlan Crowder & Manfred W. Padberg, 1980. "Solving Large-Scale Symmetric Travelling Salesman Problems to Optimality," Management Science, INFORMS, vol. 26(5), pages 495-509, May.
    11. H. Donald Ratliff & Arnon S. Rosenthal, 1983. "Order-Picking in a Rectangular Warehouse: A Solvable Case of the Traveling Salesman Problem," Operations Research, INFORMS, vol. 31(3), pages 507-521, June.
    12. Iida, Yasunori & Akiyama, Takamasa & Uchida, Takashi, 1992. "Experimental analysis of dynamic route choice behavior," Transportation Research Part B: Methodological, Elsevier, vol. 26(1), pages 17-32, February.
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    Cited by:

    1. Chakhtoura, Céline & Pojani, Dorina, 2016. "Indicator-based evaluation of sustainable transport plans: A framework for Paris and other large cities," Transport Policy, Elsevier, vol. 50(C), pages 15-28.

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