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A crowdsourced dynamic repositioning strategy for public bike sharing systems

The first author is supported by MOST grant 110-2221-E-006-191-MY3

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  • Public bike sharing systems have become the most popular shared economy application in transportation. The convenience of this system depends on the availability of bikes and empty racks. One of the major challenges in operating a bike sharing system is the repositioning of bikes between rental sites to maintain sufficient bike inventory in each station at all times. Most systems hire trucks to conduct dynamic repositioning of bikes among rental sites. We have analyzed a commonly used repositioning scheme and have demonstrated its ineffectiveness. To realize a higher quality of service, we proposed a crowdsourced dynamic repositioning strategy: first, we analyzed the historical rental data via the random forest algorithm and identified important factors for demand forecasting. Second, considering 30-minute periods, we calculated the optimal bike inventory via integer programming for each rental site in each time period with a sufficient crowd for repositioning bikes. Then, we proposed a minimum cost network flow model in a time-space network for calculating the optimal voluntary rider flows for each period based on the current bike inventory, which is adjusted according to the forecasted demands. The results of computational experiments on real-world data demonstrate that our crowdsourced repositioning strategy may reduce unmet rental demands by more than 30% during rush hours compared to conventional trucks.

    Mathematics Subject Classification: Primary: 90B06, 90B10; Secondary: 90C10.


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  • Figure 1.  Illustrative VRFM example

    Figure 2.  Comparison of the prediction performances for crowdsourced repositioning

    Figure 3.  Comparison of the repositioning performances of crowdsourcing versus various numbers of trucks

    Figure 4.  Comparison of repositioning strategies in 100 simulated daily rentals

    Figure 5.  Comparison of the repositioning performances of crowdsourcing and trucks

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