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2024, Volume 32, Issue 9: 5377-5391. doi: 10.3934/era.2024249
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Research article

Data-driven optimization for rebalancing shared electric scooters

  • 1.
    Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
  • 2.
    College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China
  • 3.
    Department of Architecture and Civil Engineering, Chalmers University of Technology, Chalmers, Sweden
  • 4.
    Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
  • 5.
    State Key Laboratory of Mechanical Transmission/Automotive Collaborative Innovation Center, Chongqing University, Chongqing, China
  • Received: 08 June 2024 Revised: 08 September 2024 Accepted: 14 September 2024 Published: 24 September 2024

  • Shared electric scooters have become a popular and flexible transportation mode in recent years. However, managing these systems, especially the rebalancing of scooters, poses significant challenges due to the unpredictable nature of user demand. To tackle this issue, we developed a stochastic optimization model (M0) aimed at minimizing transportation costs and penalties associated with unmet demand. To solve this model, we initially introduced a mean-value optimization model (M1), which uses average historical values for user demand. Subsequently, to capture the variability and uncertainty more accurately, we proposed a data-driven optimization model (M2) that uses the empirical distribution of historical data. Through computational experiments, we assessed both models' performance. The results consistently showed that M2 outperformed M1, effectively managing stochastic demand across various scenarios. Additionally, sensitivity analyses confirmed the adaptability of M2. Our findings offer practical insights for improving the efficiency of shared electric scooter systems under uncertain demand conditions.

    Citation: Yanxia Guan, Xuecheng Tian, Sheng Jin, Kun Gao, Wen Yi, Yong Jin, Xiaosong Hu, Shuaian Wang. Data-driven optimization for rebalancing shared electric scooters[J]. Electronic Research Archive, 2024, 32(9): 5377-5391. doi: 10.3934/era.2024249

    Related Papers:

  • Shared electric scooters have become a popular and flexible transportation mode in recent years. However, managing these systems, especially the rebalancing of scooters, poses significant challenges due to the unpredictable nature of user demand. To tackle this issue, we developed a stochastic optimization model (M0) aimed at minimizing transportation costs and penalties associated with unmet demand. To solve this model, we initially introduced a mean-value optimization model (M1), which uses average historical values for user demand. Subsequently, to capture the variability and uncertainty more accurately, we proposed a data-driven optimization model (M2) that uses the empirical distribution of historical data. Through computational experiments, we assessed both models' performance. The results consistently showed that M2 outperformed M1, effectively managing stochastic demand across various scenarios. Additionally, sensitivity analyses confirmed the adaptability of M2. Our findings offer practical insights for improving the efficiency of shared electric scooter systems under uncertain demand conditions.



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  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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