Enriching Smart Cities by Optimizing Electric Vehicle Ride-Sharing through Game Theory

Darko Radakovic, Anuradha Singh, Aparna S. Varde, Pankaj Lal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Pillars of smart cities include smart environment, mobility and economy. We explore impacts on these to enhance smart cities, heading towards a smart planet. Our motivation emerges from the need to decarbonize transportation. In this context, ride-sharing companies deploy electric vehicles (EVs). These should be managed by various factors: battery demand, EV charging station location, service availability, and charging time. Ride-sharing EV s aim to maximize profits via more rides. Our paper explores game theory in AI here. We propose E-Ride-Minimax, adapting the Minimax algorithm, treating EV ride-sharing companies as players. We hypothesize one player choosing its next move via total passenger-travel distance (longer the distance, larger the profit); and another player via battery usage (ratio of total passenger-travel distance to vehicle-passenger distance: optimizing this ratio enables more travel without recharging). Experimental results reveal that rising passenger numbers yield maximum battery savings (e.g. rush hours / major events); followed by stable and falling numbers. Our findings indicate that E-Ride-Minimax can reduce battery usage in some circumstances by 64%, losing 1 % profit. This is vital, given global emphasis on climate change. It increases cost-effectiveness, consumer participation and passengers per mile; reduces energy use and greenhouse gas emissions; and thus helps decarbonize transportation.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022
EditorsMarek Reformat, Du Zhang, Nikolaos G. Bourbakis
PublisherIEEE Computer Society
Pages755-759
Number of pages5
ISBN (Electronic)9798350397444
DOIs
StatePublished - 2022
Event34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022 - Virtual, Online, China
Duration: 31 Oct 20222 Nov 2022

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2022-October
ISSN (Print)1082-3409

Conference

Conference34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022
Country/TerritoryChina
CityVirtual, Online
Period31/10/222/11/22

Keywords

  • AI in smart cities
  • climate change
  • decarbonizing
  • energy
  • game theory
  • Minimax
  • ride-sharing EV
  • smart economy
  • smart environment
  • smart mobility
  • traffic
  • transportation

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