Design of a Hybrid Rebalancing Strategy to Improve Level of Service of Free-Floating Bike Sharing Systems

It is known that for bike sharing systems, the flow of customers can completely change the temporal and spatial distribution of the bikes and cause an imbalance of demand and supply in the system. Thus rebalancing/redistribution of bikes is critical to ensure the efficiency of bike sharing systems. Rebalancing of bikes can be done either by users with incentive program or by operator with a fleet of rebalancing vehicles. In an operator-based rebalancing method, the operator collects and repositions bikes in order to balance certain number of bikes to predetermined locations. The rebalancing can be static or dynamic or a combination of the static and dynamic. Static rebalancing means that the bikes are rebalanced without the interference of users' activities. Such rebalancing is usually operated during the night when no customers borrow or return bikes. In contrast, dynamic rebalancing is operated periodically in the day when the borrowing and returning of bikes continuously occur. Recently, a new type of bike sharing systems, the dockless/free-floating bike sharing system, has emerged which does not need docking stations, and therefore, it cuts a large percentage of start-up investment. With the built-in global positioning system (GPS) device, the free-floating bike sharing system allows users to leave a bike almost anywhere which beside the flexibility makes the rebalancing of these systems more challenging than typical station-based ones. In light of the above, a hybrid rebalancing method is developed in this project by combining user-based incentive program and operator-based rebalancing to take the advantage of both in free floating bike sharing systems. This method has been featured by a multi-objective technique to optimize the system based on two objectives, cost and service level, which helps decision makers have a better knowledge about the trade-off between these two objectives caused by their decision. In addition, capability of used tools in this method guarantees its applicability on real world scale problems. This technique has been successfully applied on the data collected from ShareABull system at USF.


  • English


  • Status: Completed
  • Funding: $264386
  • 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:


    Washington DC,   United States 
  • Project Managers:

    Kline, Robin

  • Performing Organizations:

    University of South Florida, Tampa

    Department of Civil and Environmental Engineering
    4202 E. Flowler Avenue, ENB 118
    Tampa, FL  United States  33620-5350
  • Principal Investigators:

    Zhang, Yu

    Charkhgard, Hadi

  • Start Date: 20181001
  • Expected Completion Date: 20190930
  • Actual Completion Date: 20190930
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01714341
  • Record Type: Research project
  • Source Agency: Center for Transportation, Environment, and Community Health
  • Contract Numbers: 69A3551747119
  • Files: UTC, RiP
  • Created Date: Aug 20 2019 12:38PM