DSDRO: Bike-Sharing Demand and Rebalancing Optimization Results

Jianrong Cai's dataset presents results from a Dynamic pricing-based Stochastic Demand-Response Optimization (DSDRO) method for bike-sharing systems. The dataset likely contains numerical experiment results validating the DSDRO framework, which uses spatiotemporal demand patterns and an improved Particle Swarm Optimization algorithm. The data was uploaded to figshare on 2026-04-16.

Use Cases

• Benchmarking optimization algorithms based on reported revenue and profit increases.
• Analyzing the impact of dynamic pricing on bike-sharing revenue based on the described 300% increase.
• Evaluating the cost reduction of rebalancing operations based on the 76.21% improvement.
• Studying spatiotemporal patterns in bike-sharing demand as referenced in the DSDRO framework.

Strengths

• Results are validated with real operational data.
• Performance metrics are reported with specific numerical improvements: revenue +15.51%, profit +24.18%, rebalancing cost -76.21%.

Limitations

• Dataset size is 5.5 KB, indicating a very limited scope.
• Row count and column-level documentation are unknown.
• The description focuses on methodology results; the underlying operational data may not be included.

Provenance

Source

Jianrong Cai

Collection Method

Numerical experiments based on real operational data.

Freshness

Last updated 2026-04-16 17:40:10.