Abstract
The electric scooter (e-scooter) is an increasingly popular transport mode in urban areas at global level. As an alternative form of shared micromobility in cities, e-scooter sharing systems were first introduced in the United States in 2017. Since then, they have spread in Europe, Asia, and Australia. In order to evaluate their sustainability performance, shared e-scooters should be simulated or/and measure their real-life impacts; however, a universal evaluation process for selecting a simulation platform does not exist. This study seeks to better understand the impacts of micromobility and simulation platforms for urban areas. To achieve this a two-stage evaluation process is performed. At first, the study reviews and analyzes the most common simulation models (i.e., traffic simulation and Agent-based Model (ABM) platforms). Seven ABM platforms are identified as suitable for simulating transportation modes. The seven identified ABM platforms are explored and evaluated based on a set of indicators representing four dimensions: 1) Functionality, 2) Capabilities, 3) Data, and 4) Operational Capacity. In the second-stage evaluation the seven ABM platforms are further evaluated for simulating micromobility against ten proposed criteria. The ABM platform Multi-Agent Transport Simulation Toolkit (MATSim) emerges as the most prevalent for simulating e-scooters in urban areas as it meets nine of the ten introduced criteria and it has the potential to be adapted effectively in the simulation of new innovative transport services.
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Funding
This research has been co-financed by the European Union and Greece, National Strategic Reference Framework 2014–2020 (NSRF), through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE –INNOVATE (project code: T2EDK-02494).
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Stavropoulou, E., Mitropoulos, L., Tzouras, P.G., Karolemeas, C., Kepaptsoglou, K. (2023). An Evaluation of Agent-Based Models for Simulating E-Scooter Sharing Services in Urban Areas. In: Nathanail, E.G., Gavanas, N., Adamos, G. (eds) Smart Energy for Smart Transport. CSUM 2022. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-031-23721-8_79
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