Fleet Management for Vehicle Sharing Operations

Transit, touted as a solution to urban mobility problems, cannot match the addictive flexibility of the automobile. 86.5% of all trips in the U.S. are in personal vehicles (USDOT 2001). A more recent approach to reduce automobile ownership is through the use of vehicle sharing programs (VSPs). A VSP involves a fleet of vehicles located strategically at stations across the transportation network. In its most flexible form, users are free to check out vehicles at any station and return them to stations close to their destinations. Vehicle fleets can be comprised of bicycles, low emission cars or electric vehicles. Such systems offer innovative, low-cost, and flexible solutions to the larger mobility problem and can have positive impacts on the transportation system as a whole by reducing urban congestion. To match automobile flexibility, users are free to determine all trip characteristics (where to checkout and return vehicles, duration of travel and time of travel). This places exceptional logistical challenges on operators who must ensure demand in the near future is met. Since flow from one station to another is seldom equal to flow in the opposing direction, the VSP fleet can become spatially imbalanced. To meet near-future demand, operators must then redistribute vehicles to correct this asymmetry. The focus of this report is to provide efficient, cost-effective operational strategies for fleet management. A stochastic, mixed-integer program (MIP) involving joint chance constraints is developed that generates least-cost vehicle redistribution plans for shared-vehicle systems such that a proportion of all near-term demand scenarios are met. The model aims to correct short term demand asymmetry in shared-vehicle systems, where flow from one station to another is seldom equal to the flow in the opposing direction. The model accounts for demand stochasticity and generates partial redistribution plans in circumstances when demand outstrips supply. This stochastic MIP has a non-convex feasible region that poses computational challenges. To solve the proposed program two solution procedures are developed. The first procedure is based on enumerating p-efficient points, used to transform the problem into a set of disjunctive, convex MIPs. A novel divide-and-conquer algorithm for generating p-efficient points that handles dual-bounded chance constraints is developed. This technique has a smaller memory and computational footprint than previously proposed methods. Since this method can be computationally prohibitive for large shared-vehicle systems, the authors develop a faster cone-generation method that assumes that the random demand at each station is independent. Finally, using an equal-failure apportionment assumption, the authors develop a bound on the problem that can also be used to generate redistribution strategies.

  • Record URL:
  • Corporate Authors:

    University of Maryland, College Park

    Department of Civil and Environmental Engineering
    College Park, MD  United States  20742

    Federal Highway Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590

    Mid-Atlantic Universities Transportation Center

    Pennsylvania State University
    201 Transportation Research Building
    University Park, PA  United States  16802-4710

    Research and Innovative Technology Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Authors:
    • Miller-Hooks, Elise
    • Nair, Rahul
  • Publication Date: 2010-5


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; References; Tables;
  • Pagination: 35p

Subject/Index Terms

Filing Info

  • Accession Number: 01485009
  • Record Type: Publication
  • Report/Paper Numbers: UMD-2008-02
  • Contract Numbers: DTRT07-G-0003 (Grant)
  • Created Date: Jun 25 2013 9:55AM