A multi-AI-agent framework for vehicle-infrastructure integration and electric vehicle robust charging

Transportation systems and in particular fossil fuels utilized in transportation are responsible for approximately 27% of greenhouse gases (such as Co2 emissions) in the U.S. in 2015. Under the Clean Air Act, more and more states are adopting California’s Zero Emission Vehicle (ZEV) regulations. This results in an increase in the number of electric vehicles (EVs). It is expected that EVs will comprise 30% of all cars globally by 2030. In addition to the pollution caused by the fossil fuels, the transportation system itself has the potential to greatly reduce emissions production and energy consumption through reducing congestion. Traffic congestion not just cause travel delays but also increases fuel consumption and emissions production. One of the major reasons for congestion in urban areas is traffic accidents. These crashes are the leading cause of accidental death in the United States with the major factor in over 90 percent of all fatal crashes being human error. Currently, traffic cameras and video surveillance are one of the ways used to monitor the traffic. However, these methods are capital demanding, and don’t provide real-time trip information to the travelers. New technologies, such as vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) communication, may be able to greatly reduce congestion. This communication allows real-time detection of congestion, which can result in immediate distributing of traffic affected by the congestion and therefore result in a more efficient transportation network. Advances in wireless communication technology, for instance advanced 5G communication networks will enable this interconnection and will allow users to make better decisions regarding the use of the transportation system. In the foreseen transportation infrastructure, vehicles will communicate with other vehicles, traffic control units and traffic management centers, to make more efficient trip decisions. In addition to the communication reducing traffic congestion, it will also help in fast EVs charging, utility and capital cost management. Developing an effective management scheme that maximize the use of limited EV charge stations currently available is essential for transportation infrastructure agents, and utility companies to deliver high-quality service to travelers (availability of chargers, prices, reduced capital investment in new chargers, etc). This project proposes to develop a novel multi-agent artificial intelligence (AI) communication and charging system that will focus on (1) reducing traffic congestion and (2) smart EV charging. In this proposed management system traffic control units, traffic management centers, EV charging stations, utility companies and EVs are AI-powered agents capable of making smart decisions based on real-time traffic conditions, and EV charging demands and prices.


  • English


  • Status: Active
  • Funding: $ 80000
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Transportation Consortium of South-Central States (Tran-SET)

    Louisiana State University
    Baton Rouge, LA  United States  70803
  • Project Managers:

    Mousa, Momen

  • Performing Organizations:

    University of Texas at San Antonio

    One UTSA Circle
    San Antonio, TX  United States  78249
  • Principal Investigators:

    Ahmed, Sara

  • Start Date: 20200801
  • Expected Completion Date: 20220201
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01757549
  • Record Type: Research project
  • Source Agency: Transportation Consortium of South-Central States (Tran-SET)
  • Contract Numbers: 69A3551747106
  • Files: UTC, RIP
  • Created Date: Nov 11 2020 9:53AM