Actuation System For City-Wide Sensing And Ride Distribution Using Managed Vehicular Fleets

Vehicular mobile crowdsensing (MCS) enables many smart cities and urban sensing applications. Managed fleets (such as taxis) can serve as a platform for MCS due to their long operational time and city-scale coverage. The primary goal of these fleets is not sensing, which lead to poor sensing coverage and sensing quality. For example, disadvantaged areas will be rarely covered by taxi fleets. An actuation system is required for MCS to get good (large and balanced) sensing coverage quality. At the same time, this actuation system needs to 1) improve the main mobility goals of the managed fleets and 2) minimize the number of actuation commands due to the scale and human-centric nature of the fleets. The research team proposes a fleet actuation system that actuates vehicular taxi fleets for optimal sensing coverage quality while matching ride requests with taxis. The system integrates 1) a mobility prediction model that guides the selection of taxis to actuate and 2) a ride request prediction model to help match ride request with taxis, lower incentive cost and improve taxi drivers' motivation. Preliminary simulations show up to 40% improvement in sensing coverage quality improvement while also improving ride requests matching. The algorithm developed will be deployed with the team's deployment partner on an electric taxi fleet of 400 taxis. The deployment partner will help with system development and deployment.

Language

  • English

Project

  • Status: Active
  • Funding: $90000
  • Contract Numbers:

    69A3551747111

  • Sponsor Organizations:

    Carnegie Mellon University

    Mobility 21 National UTDOT for Mobility of Goods and People
    ,    

    Office of the Assistant Secretary for Research and Technology

    University Transportation Program
    ,    
  • Managing Organizations:

    Carnegie Mellon Univeristy

    Mobility 21 National UTDOT for Mobility of Goods and People
    ,    
  • Project Managers:

    Schweyer, Lisa Kay

  • Performing Organizations:

    Carnegie Mellon University

    ,    
  • Principal Investigators:

    Zhang, Pei

  • Start Date: 20180807
  • Expected Completion Date: 20190630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01677515
  • Record Type: Research project
  • Source Agency: Technologies for Safe and Efficient Transportation University Transportation Center
  • Contract Numbers: 69A3551747111
  • Files: UTC, RiP
  • Created Date: Aug 7 2018 12:05PM