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Agent-Based Modelling of Locating Public Transport Facilities for Conventional and Electric Vehicles

Author

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  • Chengxiang Zhuge

    (University of Cambridge)

  • Chunfu Shao

    (Beijing Jiaotong University)

Abstract

This paper proposes an agent-based transport facility development model for both Conventional Vehicles (CVs) and Electric Vehicles (EVs), as a key component of an agent-based land use-transport model, SelfSim. The model attempts to simultaneously locate public parking lots, refuelling stations, charging stations and charging posts at parking lots with the consideration of competitions and interactions between the facilities. The facility development model is composed of a link-based model and node-based model that are used to simulate the development of link-based (e.g., replenishing stations) and node-based facilities (e.g., parking lots), respectively, based on the spatial and temporal disaggregate demand. The demand is extracted from the activity-based simulation with MATSim-EV that is an EV extension of MATSim (Multi-Agent Transport Simulation). In the model, facility agents are defined with several specific attributes and behavioural rules, and act the role of locating transport facilities to accommodate the demand. Finally, both global and local sensitivity analyses are applied to fully test the model in several experiments set up based on a Chinese medium-sized city, Baoding. The global SA that is based on Elementary Effect Method is firstly applied to quantify the extent to which the twelve model outputs of interest are sensitive to forty key model parameters, resulting in nine significantly important parameters; Then the Once-At-A-Time (OAT)-based local SA is used to provide further insight into how these important parameters influence the model outputs of interest over years. The SA results are expected to be useful for model calibration, and how the SA results can be used to calibrate the model is discussed.

Suggested Citation

  • Chengxiang Zhuge & Chunfu Shao, 2018. "Agent-Based Modelling of Locating Public Transport Facilities for Conventional and Electric Vehicles," Networks and Spatial Economics, Springer, vol. 18(4), pages 875-908, December.
    • Handle: RePEc:kap:netspa:v:18:y:2018:i:4:d:10.1007_s11067-018-9412-3
    DOI: 10.1007/s11067-018-9412-3
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    References listed on IDEAS

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    Cited by:

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    2. Aleksandr Saprykin & Ndaona Chokani & Reza S. Abhari, 2021. "Uncertainties of Sub-Scaled Supply and Demand in Agent-Based Mobility Simulations with Queuing Traffic Model," Networks and Spatial Economics, Springer, vol. 21(2), pages 261-290, June.
    3. Ye Yang & Zhongfu Tan, 2019. "Investigating the Influence of Consumer Behavior and Governmental Policy on the Diffusion of Electric Vehicles in Beijing, China," Sustainability, MDPI, vol. 11(24), pages 1-20, December.
    4. Zhuge, Chengxiang & Wei, Binru & Shao, Chunfu & Shan, Yuli & Dong, Chunjiao, 2020. "The role of the license plate lottery policy in the adoption of Electric Vehicles: A case study of Beijing," Energy Policy, Elsevier, vol. 139(C).
    5. Chengxiang Zhuge & Chunfu Shao & Xia Li, 2019. "Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China," Energies, MDPI, vol. 12(16), pages 1-21, August.
    6. Chengxiang Zhuge & Chunfu Shao & Xia Li, 2019. "A Comparative Study of En Route Refuelling Behaviours of Conventional and Electric Vehicles in Beijing, China," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
    7. Zulfiqar Ali Lashari & Joonho Ko & Seunghyun Jung & Sungtaek Choi, 2022. "Choices of Potential Car Buyers Regarding Alternative Fuel Vehicles in South Korea: A Discrete Choice Modeling Approach," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    8. Jiri Horak & Jan Tesla & David Fojtik & Vit Vozenilek, 2019. "Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
    9. Mingdong Sun & Chunfu Shao & Chengxiang Zhuge & Pinxi Wang & Xiong Yang & Shiqi Wang, 2022. "Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications," Transportation, Springer, vol. 49(5), pages 1409-1439, October.
    10. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    11. Zulfiqar Ali Lashari & Joonho Ko & Junseok Jang, 2021. "Consumers’ Intention to Purchase Electric Vehicles: Influences of User Attitude and Perception," Sustainability, MDPI, vol. 13(12), pages 1-14, June.

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