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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Optimization problem of bike-sharing system rebalancing based on dynamic pricing.

Jianrong Cai1, Qiang Wen2, Tao Chen2

  • 1School of Civil Engineering, Hunan City University, Yiyang, China.

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|April 16, 2026
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Summary
This summary is machine-generated.

Dynamic pricing for bike-sharing systems optimizes resource use and revenue. This approach balances fairness and demand, significantly boosting profits and reducing operational costs compared to traditional methods.

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Area of Science:

  • Operations Research
  • Transportation Science
  • Data Science

Background:

  • Bike-Sharing Systems (BSS) face challenges in optimizing limited resources.
  • Current fixed pricing models fail to capture user price sensitivity and maximize service potential.

Purpose of the Study:

  • To propose a Dynamic pricing-based Stochastic Demand-Response Optimization (DSDRO) method.
  • To enhance bike-sharing resource utilization, service fairness, and economic efficiency.

Main Methods:

  • Developed a DSDRO framework integrating spatiotemporal demand patterns and dynamic pricing.
  • Embedded dynamic pricing into a two-stage allocation-rebalancing formulation.
  • Utilized an improved Particle Swarm Optimization with Large-scale Neighborhood Search (PSO-LNS) for optimization.

Main Results:

  • DSDRO increased expected revenue by 15.51% and net profit by 24.18% compared to a genetic algorithm baseline.
  • Dynamic pricing alone boosted revenue by over 300% versus fixed pricing.
  • The LNS component reduced rebalancing costs by 76.21% compared to pure PSO.

Conclusions:

  • The DSDRO method effectively improves bike-sharing resource utilization and profitability.
  • Dynamic pricing and advanced optimization algorithms are crucial for BSS efficiency.
  • Further validation in diverse operational contexts is recommended.