Academic

Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Look

arXiv:2604.03928v1 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 six-metric similarity ensemble (Kolmogorov-Smirnov, Wasserstein-1, feature distance, variance ratio, event pattern, 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 subset is strongest on mean wait, while the full ensemble is retained as a robustness-oriented default. 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

I
Indar Kumar, Girish Karhana, Sai Krishna Jasti, Ankit Hemant Lade · · 1 min read · 19 views
#cs.LG #cs.AI #cs.CV

arXiv:2604.03928v1 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 six-metric similarity ensemble (Kolmogorov-Smirnov, Wasserstein-1, feature distance, variance ratio, event pattern, 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 subset is strongest on mean wait, while the full ensemble is retained as a robustness-oriented default. 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-sq = 80.0, p = 4.25e-18; Cohen's d = 7.5-29.9 across scenarios). The improvement extends to the tail: P95 wait drops 37.6% and the Gini coefficient of wait times improves from 0.441 to 0.409 (7.3% relative). The two contributions compose multiplicatively and are independently validated: calibration provides 16.9% reduction; LP repositioning adds a further 15.5%. The approach requires no training, is deterministic and explainable, generalizes to Chicago (23.3% wait reduction via NYC-built regime library), and is robust across fleet sizes (32-47% improvement for 0.5-2x fleet scaling). We provide comprehensive ablation studies, formal statistical tests, and routing-fidelity validation with OSRM.

Executive Summary

This article proposes a regime-calibrated approach to effective ride-hailing dispatch, which involves segmenting historical trip data into demand regimes and matching the current operating period to the most similar historical analogues. The approach reduces mean rider wait times by 31.1% and improves wait time distribution, with the improvement extending to the tail. The two contributions composing this approach – calibration and LP repositioning – are independently validated and provide a 16.9% and 15.5% reduction in wait times, respectively. The approach is deterministic, explainable, and generalizes to other cities, making it a promising solution for ride-hailing companies.

Key Points

  • ▸ Regime-calibrated approach improves ride-hailing dispatch by reducing mean rider wait times and wait time distribution
  • ▸ The approach consists of segmenting historical trip data into demand regimes and matching the current operating period to the most similar historical analogues
  • ▸ LP repositioning policy and batch dispatch with Hungarian matching are used to further improve wait times

Merits

Improved wait times

The approach reduces mean rider wait times by 31.1% and improves wait time distribution, with the improvement extending to the tail.

Deterministic and explainable

The approach does not rely on machine learning models and is therefore deterministic and explainable, making it more transparent and trustworthy.

Generalizability

The approach generalizes to other cities, making it a promising solution for ride-hailing companies.

Demerits

Limited data

The article only evaluates the approach on a dataset of 5.2 million NYC TLC trips, which may not be representative of other cities or countries.

Dependence on historical data

The approach relies on historical data to calibrate demand regimes, which may not accurately reflect changing demand patterns over time.

Expert Commentary

This article makes a significant contribution to the field of ride-hailing optimization by proposing a regime-calibrated approach that improves wait times and reduces costs. The approach is deterministic, explainable, and generalizes to other cities, making it a promising solution for ride-hailing companies. However, the article could be improved by evaluating the approach on a larger dataset and exploring the potential for incorporating machine learning models to improve predictive accuracy. Additionally, the article could benefit from a more detailed discussion of the limitations of the approach and potential areas for future research.

Recommendations

  • ✓ Ride-hailing companies should consider using this approach to improve wait times and reduce costs
  • ✓ Further research should be conducted to evaluate the approach on a larger dataset and explore the potential for incorporating machine learning models

Sources

Original: arXiv - cs.LG

Related Articles