Fleet management for vehicle sharing operations.

Details

• Creators:
  Miller-Hooks, Elise ; Nair, Rahul
• Corporate Creators:
  University of Maryland (College Park, Md.)
• Corporate Contributors:
  Mid-Atlantic Universities Transportation Center
• Subject/TRT Terms:
  • Fleet management
  • Vehicle sharing
  • Motor vehicles
  • Bicycles
  • Mixed integer programming
  • Stochastic programming
  • Algorithms
• Publication/ Report Number:
  UMD-2008-02
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  United States. Dept. of Transportation. Research and Innovative Technology Administration
• NTL Classification:
  NTL-PLANNING AND POLICY-PLANNING AND POLICY ; NTL-HIGHWAY/ROAD TRANSPORTATION-HIGHWAY/ROAD TRANSPORTATION
• Abstract:
  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 dualbounded ; chance constraints is developed. Our technique has a smaller memory and computational footprint than ; previously proposed methods. Since this method can be computationally prohibitive for large shared-vehicle systems, we ; develop a faster cone-generation method that assumes that the random demand at each station is independent. Finally, ; using an equal-failure apportionment assumption we develop a bound on the problem that can also be used to generate ; redistribution strategies.
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• Format:
  PDF
• Funding:
  DTRT07-G-0003
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/25986/dot_25986_DS1.pdf
• File Type:
  [PDF - 1.25 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:4ea1ca73d33524a2aa84a6cab7a888c2d2fa60676a9e1158039f7db774953965142d33ccbcb6fa454b9eba9605cf7d7cbab279f6d976e3054473c24601ee1fb6