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Centrally Coordinated Schedules and Routes of Airport Shuttles with LAX Terminals as Application Area

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  • Ioannou, Petros
  • Chen, Pengfei

Abstract

Today’s airport terminals face a critical problem of traffic congestion in the terminal area partly caused by uncoordinated shuttle operations. The congestion near pick-up and drop-off points negatively affects passenger traffic leading to unnecessary idling, delays and congestion with negative impact on air quality and mobility. The need for an intelligent shuttle management system becomes more urgent with the development of information technologies, battery electric shuttles and autonomous vehicles. In this project, we developed a centrally coordinated shuttle scheduling and routing management system for mixed fleets of diesel and electric shuttles using a digital twin of LAX to LA downtown traffic road network by optimizing the total combined cost of energy consumption and travel time. A Co-Simulation Optimization method is used to solve the problem. The objective is to reduce congestion at the designated pick up and drop off points due to different shuttles showing up at these points during overlapping time windows which exceed the curb capacity. Another objective is to integrate into the system mixed fleet of shuttles that include diesel and battery operated. The proposed centrally coordinated shuttle scheduling and routing management system takes into account the characteristics of mixed shuttle fleets and is shown to reduce the operational cost such as energy consumption and delays. The results also suggest the deployment of electric shuttles in order to reduce emissions and improve air quality further. View the NCST Project Webpage

Suggested Citation

  • Ioannou, Petros & Chen, Pengfei, 2023. "Centrally Coordinated Schedules and Routes of Airport Shuttles with LAX Terminals as Application Area," Institute of Transportation Studies, Working Paper Series qt6gg7r6c5, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt6gg7r6c5
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    1. Lozano, Angelica & Storchi, Giovanni, 2001. "Shortest viable path algorithm in multimodal networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(3), pages 225-241, March.
    2. Castelli, Lorenzo & Pesenti, Raffaele & Ukovich, Walter, 2004. "Scheduling multimodal transportation systems," European Journal of Operational Research, Elsevier, vol. 155(3), pages 603-615, June.
    3. Gilbert Laporte, 2009. "Fifty Years of Vehicle Routing," Transportation Science, INFORMS, vol. 43(4), pages 408-416, November.
    4. Modesti, Paola & Sciomachen, Anna, 1998. "A utility measure for finding multiobjective shortest paths in urban multimodal transportation networks," European Journal of Operational Research, Elsevier, vol. 111(3), pages 495-508, December.
    5. Gilbert Laporte & François Louveaux & Hélène Mercure, 1992. "The Vehicle Routing Problem with Stochastic Travel Times," Transportation Science, INFORMS, vol. 26(3), pages 161-170, August.
    6. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    7. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    8. Ibarra-Rojas, Omar J. & Rios-Solis, Yasmin A., 2012. "Synchronization of bus timetabling," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 599-614.
    9. Merrill M. Flood, 1956. "The Traveling-Salesman Problem," Operations Research, INFORMS, vol. 4(1), pages 61-75, February.
    10. Chen, Chao, 2003. "Freeway Performance Measurement System (PeMS)," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6j93p90t, Institute of Transportation Studies, UC Berkeley.
    11. Gilbert Laporte, 2007. "What you should know about the vehicle routing problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(8), pages 811-819, December.
    12. Enjian Yao & Zhiqiang Yang & Yuanyuan Song & Ting Zuo, 2013. "Comparison of Electric Vehicle’s Energy Consumption Factors for Different Road Types," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-7, December.
    13. Kang, Liujiang & Wu, Jianjun & Sun, Huijun & Zhu, Xiaoning & Gao, Ziyou, 2015. "A case study on the coordination of last trains for the Beijing subway network," Transportation Research Part B: Methodological, Elsevier, vol. 72(C), pages 112-127.
    14. Ceder, A. & Golany, B. & Tal, O., 2001. "Creating bus timetables with maximal synchronization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(10), pages 913-928, December.
    15. Jacques Guélat & Michael Florian & Teodor Gabriel Crainic, 1990. "A Multimode Multiproduct Network Assignment Model for Strategic Planning of Freight Flows," Transportation Science, INFORMS, vol. 24(1), pages 25-39, February.
    16. Ham, Heejoo & Kim, Tschangho John & Boyce, David, 2005. "Implementation and estimation of a combined model of interregional, multimodal commodity shipments and transportation network flows," Transportation Research Part B: Methodological, Elsevier, vol. 39(1), pages 65-79, January.
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    Keywords

    Engineering; Congestion management systems; Electric vehicles; Routes and routing; Schedules and scheduling; Shuttle buses; Traffic simulation;
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