Regime-Calibrated Demand Priors for Ride-Hailing Fleet Dispatch and Repositioning
arXiv:2604.03883v1 Announce Type: new Abstract: Effective ride-hailing dispatch requires anticipating demand patterns that vary substantially across time-of-day, day-of-week, season, and special events. We propose a regime-calibrated approach that (i) segments historical trip data into demand regimes, (ii) matches the current operating period to the most similar historical analogues via a similarity ensemble combining Kolmogorov-Smirnov distance, Wasserstein-1 distance, feature distance, variance ratio, event pattern similarity, and temporal proximity, and (iii) uses the resulting calibrated demand prior to drive both an LP-based fleet repositioning policy and batch dispatch with Hungarian matching. In ablation, a distributional-only metric subset achieves the strongest mean-wait reduction, while the full ensemble is retained as a robustness-oriented default that preserves calendar and event context. Evaluated on 5.2 million NYC TLC trips across 8 diverse scenarios (winter/summer, w
arXiv:2604.03883v1 Announce Type: new Abstract: Effective ride-hailing dispatch requires anticipating demand patterns that vary substantially across time-of-day, day-of-week, season, and special events. We propose a regime-calibrated approach that (i) segments historical trip data into demand regimes, (ii) matches the current operating period to the most similar historical analogues via a similarity ensemble combining Kolmogorov-Smirnov distance, Wasserstein-1 distance, feature distance, variance ratio, event pattern similarity, and temporal proximity, and (iii) uses the resulting calibrated demand prior to drive both an LP-based fleet repositioning policy and batch dispatch with Hungarian matching. In ablation, a distributional-only metric subset achieves the strongest mean-wait reduction, while the full ensemble is retained as a robustness-oriented default that preserves calendar and event context. Evaluated on 5.2 million NYC TLC trips across 8 diverse scenarios (winter/summer, weekday/weekend/holiday, morning/evening/night) with 5 random seeds each, our method reduces mean rider wait times by 31.1% (bootstrap 95% CI: [26.5, 36.6]; Friedman chi-squared = 80.0, p = 4.25e-18; Cohen's d = 7.5-29.9). P95 wait drops 37.6% and the Gini coefficient of wait times improves from 0.441 to 0.409. The two contributions compose multiplicatively: calibration provides 16.9% reduction relative to the replay baseline; LP repositioning adds a further 15.5%. The approach requires no training, is deterministic and explainable, generalizes to Chicago (23.3% wait reduction using the NYC-built regime library without retraining), and is robust across fleet sizes (32-47% improvement for 0.5x-2.0x fleet scaling). Code is available at https://github.com/IndarKarhana/regime-calibrated-dispatch.
Executive Summary
This article proposes a novel approach to ride-hailing fleet dispatch and repositioning, leveraging a regime-calibrated demand prior to drive both fleet repositioning policy and batch dispatch. The method segments historical trip data into demand regimes, matches the current operating period to the most similar historical analogues, and uses the resulting demand prior to inform dispatch decisions. Evaluations on 5.2 million NYC TLC trips demonstrate significant reductions in mean rider wait times (31.1%), P95 wait (37.6%), and Gini coefficient of wait times (16.9%). The approach is deterministic, explainable, and generalizes to other cities. While requiring no training, it relies on historical data, which may not capture novel events or trends. This work contributes meaningfully to the field of ride-hailing optimization, offering a more nuanced understanding of demand patterns and their impact on dispatch decisions.
Key Points
- ▸ Regime-calibrated demand prior approach segments historical trip data into demand regimes and matches current operating period to most similar historical analogues
- ▸ Method evaluates on 5.2 million NYC TLC trips, demonstrating significant reductions in mean rider wait times and other metrics
- ▸ Approach is deterministic, explainable, and generalizes to other cities, requiring no training
Merits
Strength in methodological innovation
The regime-calibrated demand prior approach offers a novel and effective way to segment historical trip data and inform dispatch decisions, leveraging a combination of distance metrics and temporal proximity
Robust evaluation and generalizability
The method is evaluated on a large and diverse dataset, demonstrating significant reductions in wait times and generalizing to other cities, including Chicago
Deterministic and explainable approach
The method avoids reliance on machine learning models, providing a transparent and interpretable approach to ride-hailing optimization
Demerits
Limitation of reliance on historical data
The method may not capture novel events or trends, requiring continuous updating of historical data to maintain effectiveness
Potential for overfitting to specific city or region
The method's reliance on historical data may lead to overfitting to specific city or region, potentially limiting generalizability to other areas
Expert Commentary
This article represents a significant contribution to the field of ride-hailing optimization, offering a novel and effective approach to segmenting historical trip data and informing dispatch decisions. The method's reliance on deterministic and explainable techniques is particularly noteworthy, providing a transparent and interpretable approach to ride-hailing optimization. While the method's limitations should be acknowledged, its potential for reducing wait times and improving dispatch decisions makes it a valuable tool for ride-hailing companies and policymakers alike. As the ride-hailing industry continues to evolve, this work highlights the need for more nuanced understanding of demand patterns and their impact on dispatch decisions, with significant implications for logistics and transportation management.
Recommendations
- ✓ Further research is needed to explore the method's potential for capturing novel events or trends, potentially through the incorporation of real-time data or machine learning models
- ✓ The method's generalizability to other cities and regions should be evaluated in future studies, considering the potential for overfitting to specific city or region
Sources
Original: arXiv - cs.LG