Optimal Bike-Sharing Rebalancing Scheme with Dynamic Pricing

A dataset containing results from numerical experiments validating a Dynamic pricing-based Stochastic Demand-Response Optimization (DSDRO) method for bike-sharing systems. The dataset likely includes performance metrics from real operational data, comparing revenue and profit improvements against baseline algorithms. It was authored by Jianrong Cai and last updated on April 16, 2026.

Use Cases

• Benchmarking bike-sharing rebalancing algorithms based on revenue and profit metrics mentioned in the description
• Analyzing the impact of dynamic pricing on demand stickiness and resource utilization as described in the DSDRO framework
• Evaluating the performance of Particle Swarm Optimization with Large-scale Neighborhood Search (PSO-LNS) for solving allocation-rebalancing problems
• Studying spatiotemporal patterns in bike-sharing demand data referenced in the methodology

Strengths

• The dataset is validated by real operational data, as stated in the description
• Performance metrics are quantified with specific improvements: revenue increased by 15.51%, net profit by 24.18%, and rebalancing cost reduced by 76.21%
• Algorithm stability is confirmed with a coefficient of variation below 5% across 10 independent runs

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The dataset is very small (5.5 KB), suggesting limited scope or aggregated results

Provenance

Source

figshare

Collection Method

Numerical experiments based on real operational data, as described in the research methodology

Freshness

Last updated 2026-04-16 17:40:11