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Multi-agent simulation for planning and designing new shared mobility services

Author

Listed:
  • Inturri, Giuseppe
  • Le Pira, Michela
  • Giuffrida, Nadia
  • Ignaccolo, Matteo
  • Pluchino, Alessandro
  • Rapisarda, Andrea
  • D'Angelo, Riccardo

Abstract

Limiting private cars' use while promoting sustainable modes of transport is one of the main challenges of urban transport planning. In this context, characterized by scarce resources and increasing demand for mobility, Demand Responsive Shared Transport (DRST) services can bridge the gap between shared low-quality public transport and unsustainable individual private transport. Taking advantage of Information and Communication Technologies (ICT), they can supply transport solutions ranging from flexible transit to ride sharing services, providing real-time “on demand” mobility through fleets of vehicles shared by different passengers. The optimal design of a DRST service requires a trade-off among efficiency (from the operators' point of view), service quality (from the users' point of view) and sustainability (from the community's point of view). In this paper, an agent-based model (ABM) fed with GIS data is used to explore different system configurations of a specific type of DRST service, i.e. flexible transit, and to estimate the transport demand and supply variables that make the service feasible and convenient. The model reproduces a mixed fixed/flexible route transit service with different fleet size and vehicle capacity in the city of Ragusa (Italy) with the aim to: (i) make a first test of the ABM model with GIS-based demand and road network models; (ii) explore different vehicle dispatching strategies; (iii) find appropriate indicators to monitor the service quality and efficiency. Simulation results show the impact of fleet composition and route choice strategy on the system performance. In particular, they show an optimal range of operating vehicles that minimizes a total unit cost indicator, accounting both for passenger travel time and vehicle operation cost. By reproducing the microinteraction between demand and supply agents (i.e. passengers and vehicles), it is possible to monitor the macroscopic behaviour of the system, and derive useful suggestions for the correct planning, management and optimization of DRST services.

Suggested Citation

  • Inturri, Giuseppe & Le Pira, Michela & Giuffrida, Nadia & Ignaccolo, Matteo & Pluchino, Alessandro & Rapisarda, Andrea & D'Angelo, Riccardo, 2019. "Multi-agent simulation for planning and designing new shared mobility services," Research in Transportation Economics, Elsevier, vol. 73(C), pages 34-44.
    • Handle: RePEc:eee:retrec:v:73:y:2019:i:c:p:34-44
    DOI: 10.1016/j.retrec.2018.11.009
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    5. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    6. Lisa Dang & Widar von Arx & Jonas Frölicher, 2021. "The Impact of On-Demand Collective Transport Services on Sustainability: A Comparison of Various Service Options in a Rural and an Urban Area of Switzerland," Sustainability, MDPI, vol. 13(6), pages 1-27, March.
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    More about this item

    Keywords

    Sustainable mobility; Flexible transit; Demand responsive transport; On-demand mobility; Mobility as a service; Agent-based model;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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