Appendix for Degradation-Aware Robust Optimization for Autonomous Electric Ride-Hailing Sy

A dataset supporting a research paper on optimizing autonomous electric ride-hailing fleets. The data likely contains operational records from San Francisco and Austin, used to model vehicle dispatching, routing, charging, and battery health evolution. The dataset is 7.7 MB, authored by Li, and was last updated on May 4, 2026.

Use Cases

• Modeling battery degradation dynamics based on charging intensity and depth of discharge patterns.
• Developing meta-learning algorithms for operational pattern transfer across different urban systems.
• Testing distributionally robust optimization formulations using a Wasserstein ambiguity set.
• Evaluating risk management strategies for fleet operations using CVaR-based risk measures.
• Analyzing trade-offs between short-term service efficiency and long-term battery health in ride-hailing systems.

Strengths

• Based on real-world ride-hailing data from San Francisco and Austin, as stated in the description.
• Supports a method that reportedly achieved a 15%–25% reduction in degradation heterogeneity and over 20% improvement in fleet utilization balance.
• Released under a CC-BY-4.0 license, permitting broad reuse with attribution.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary file format is PDF, which may require extraction to access structured data.

Provenance

Source

figshare

Collection Method

Likely derived from real-world ride-hailing operations, as mentioned in the paper's case studies.

Time Range

null

Freshness

Last updated 2026-05-04 16:22:33; freshness should be verified.

Geography

San Francisco and Austin, as referenced in the description.