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Algorithms to Manage Congestion in Large-Scale Mobility-on-Demand Schemes that Use Electric Vehicles

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Abstract

This paper studies the use of Electric Vehicles (EVs) in a Mobility-on-Demand (MoD) scheme. In this scheme, customers that act as cooperative agents request a set of alternative trips, where each trip provides a utility to the agent. The EVs are distributed across a number of stations. We propose congestion management algorithms which take as input the trip requests and calculate the EV-to-customer assignment, considering the number of executed trips and the utility obtained by the agents. Initially, we solve the problem offline and optimally using Mixed-Integer-Programming (MIP) techniques and then we solve it online using an equivalent greedy algorithm. The online algorithm uses three alternative heuristic functions to decide on the execution of a customer request: (a) The sum of squares of all EVs in all stations, (b) the percentage of trips’ destination location fullness and (c) a random choice of trip execution. To further improve the performance of the online algorithms, we also propose an agent-based negotiation scheme where alternative offers in terms of trip starting time are made to the agents when the initial EV assignment problem is unsolvable due to insufficient resources. Through a detailed evaluation, we observe that (a) provides an increase of up to \(3.2\%\) compared to (b) and up to \(3.7\%\) compared to (c) in terms of average trip execution, while giving the higher utility to the agents. All variations achieve close to the optimal performance. At the same time, the negotiation scheme improves the performance of the algorithms by up to \(15.7\%\).

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Notes

  1. https://global.toyota/en/detail/3962091/.

  2. https://www.daimler.com/case/shared-and-services/en/.

  3. This is usually done by adding two extra decision variables and two extra constraints.

  4. For example if we consider two stations each having three parking spots, and three EVs. If all three EVs are parked in one station (when a task/request will be accomplished), the outcome would be: \(3^2+0^2=9\). However, if two EVs were parked at one station and one at the other, the outcome would be: \(2^2+1^2=5\).

  5. https://www.zipcar.com/washington-dc.

  6. ,http://opendata.dc.gov/datasets/.

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Acknowledgements

This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme “Human Resources Development,Education and Lifelong Learning” in the context of the project “Reinforcement of Postdoctoral Researchers - 2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (IKY).

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Emmanouil S. Rigas

  2. Department of Computing, The University of Northampton, Northampton, NN15PH, UK

    Konstantinos S. Tsompanidis

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Correspondence to Emmanouil S. Rigas.

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This article is part of the topical collection “Advances in Multi-Agent Systems Research: EUMAS 2020 Extended Selected Papers” guest edited by Nick Bassiliades and Georgios Chalkiadakis.

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Rigas, E.S., Tsompanidis, K.S. Algorithms to Manage Congestion in Large-Scale Mobility-on-Demand Schemes that Use Electric Vehicles. SN COMPUT. SCI. 2, 292 (2021). https://doi.org/10.1007/s42979-021-00685-7

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