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AMoDSim: An Efficient and Modular Simulation Framework for Autonomous Mobility on Demand

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11253)

Abstract

Urban transportation of next decade is expected to be disrupted by Autonomous Mobility on Demand (AMoD): AMoD providers will collect ride requests from users and will dispatch a fleet of autonomous vehicles to satisfy requests in the most efficient way. Differently from current ride sharing systems, in which driver behavior has a clear impact on the system, AMoD systems will be exclusively determined by the dispatching logic. As a consequence, a recent interest in the Operations Research and Computer Science communities has focused on this control logic. The new propositions and methodologies are generally evaluated via simulation. Unfortunately, there is no simulation platform that has emerged as reference, with the consequence that each author uses her own custom-made simulator, applicable only in her specific study, with no aim of generalization and without public release. This slows down the progress in the area as researchers cannot build on each other’s work and cannot share, reproduce and verify the results. The goal of this paper is to present AMoDSim, an open-source simulation platform aimed to fill this gap and accelerate research in future ride sharing systems.

Keywords

  • Smart mobility
  • Smart city
  • Shared mobility
  • Autonomous vehicles
  • Simulation

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Notes

  1. 1.

    https://github.com/admaria/AMoDSim.

  2. 2.

    A particular version of SUMO, called SUMO MESO [2], is intended to reduce the details in vehicle movement simulation. However, we are not aware of any published study on AMoD systems based on SUMO MESO.

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Acknowledgement

This work is supported by the CLARA - CLoud plAtform and smart underground imaging for natural Risk Assessment - project, funded by the Italian Ministry of Education, Universities and Research, within the Smart Cities and Communities and Social Innovation initiative.

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Correspondence to Andrea Araldo .

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Di Maria, A., Araldo, A., Morana, G., Di Stefano, A. (2018). AMoDSim: An Efficient and Modular Simulation Framework for Autonomous Mobility on Demand. In: Skulimowski, A., Sheng, Z., Khemiri-Kallel, S., Cérin, C., Hsu, CH. (eds) Internet of Vehicles. Technologies and Services Towards Smart City. IOV 2018. Lecture Notes in Computer Science(), vol 11253. Springer, Cham. https://doi.org/10.1007/978-3-030-05081-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-05081-8_12

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