Findings: COVID Ridership vs OTP Recovery

Summary

Zero of 93 routes have recovered to pre-COVID ridership levels (median -43%, all negative). Routes that lost more ridership tended to see better OTP recovery (r = -0.21, p = 0.047), weakly supporting the hypothesis that ridership recovery degrades OTP through crowding and longer dwell times.

Key Numbers

• Ridership recovery: median -43.4%, mean -44.5%; 0/93 routes at or above pre-COVID
• OTP recovery: 35/93 routes improved, 58/93 declined vs pre-COVID
• Correlation (ridership change vs OTP change): Pearson r = -0.21, p = 0.047; Spearman r = -0.29, p = 0.005
• Kruskal-Wallis (OTP delta by subtype): H = 2.89, p = 0.58 (not significant)
• Pre-COVID baseline: Jan 2019 -- Feb 2020; recovery period: Jan 2023 -- Oct 2024
• 93 routes with 6+ months in both periods

Observations

• The scatter plot falls entirely in the left half (all ridership declines). The two populated quadrants are "both declined" (58 routes) and "riders down, OTP up" (35 routes). There are zero routes in the "both improved" or "riders up, OTP down" quadrants.
• Flyers lost the most ridership (median -68%), consistent with the collapse of peak-hour commute demand. Despite this, flyers show the widest spread in OTP recovery (some improved substantially, others declined).
• Busway routes had the best OTP recovery (median +3.3 pp), likely because their dedicated right-of-way insulates them from the traffic impacts that affect other routes.
• Local bus routes (n=64) show the widest spread on both axes, reflecting the diversity of the category. Some locals improved OTP by 10 pp while others declined by 19 pp.
• The routes that improved OTP the most (P7 +18 pp, P69 +10 pp, 41 +10 pp) all lost substantial ridership (-32% to -61%), consistent with the idea that fewer riders means shorter dwell times and better schedule adherence.

Discussion

The weak negative correlation between ridership recovery and OTP recovery is consistent with a crowding mechanism: routes that regained more riders saw their OTP degrade (or fail to improve), while routes that stayed emptier ran closer to schedule. However, the effect is weak (r = -0.21) and the correlation is borderline significant (p = 0.047), so it should be interpreted cautiously.

The most policy-relevant finding is the universal ridership collapse: every single route is still below pre-COVID levels, with a median loss of 43%. This dwarfs any OTP effects. The system is running fewer riders on roughly the same infrastructure, and OTP has still declined for most routes -- suggesting that the OTP decline is driven by operational factors (staffing, vehicle maintenance, traffic congestion) rather than demand-side crowding alone.

The absence of subtype differences (Kruskal-Wallis p = 0.58) confirms Analysis 14's finding: no route type has recovered OTP systematically better or worse than others.

Caveats

• Regression to the mean: baseline OTP is negatively correlated with OTP delta (as found in Analysis 14 for the same OTP data), meaning routes with extreme pre-COVID OTP tend to regress toward the mean regardless of ridership changes. The headline correlation (r = -0.21, p = 0.047) may be partly artifactual due to RTM. The Spearman result (r = -0.29, p = 0.005) is more robust but the RTM caveat still applies.
• The ridership data ends Oct 2024; more recent ridership trends are not captured.
• "Recovery" is defined as a simple average comparison between two periods, not a trajectory analysis. A route could be on an upward trend that the period average does not fully reflect.
• The correlation between ridership change and OTP change does not establish causation; both could be driven by a third factor (e.g., service cuts, schedule changes).
• Flyer and busway subtypes have small sample sizes (n=17 and n=3), limiting subtype-level conclusions.
• Ridership data is weekday only; weekend recovery patterns may differ.

Review History

• 2026-02-27: RED-TEAM-REPORTS/2026-02-27-analyses-19-25.md — 1 significant issue. Added RTM test (baseline OTP vs OTP delta: r=-0.18, p=0.08). Borderline RTM; caveat added.

Methods: COVID Ridership vs OTP Recovery

Question

Did routes that recovered ridership fastest also recover OTP? Or does ridership recovery degrade OTP (e.g., through crowding and longer dwell times)?

Approach

• Define pre-COVID baseline as Jan 2019 -- Feb 2020 for both ridership and OTP.
• Define recovery period as Jan 2023 -- Oct 2024 (post-stabilization).
• For each route, compute ridership recovery ratio (recovery avg / baseline avg) and OTP recovery delta (recovery avg - baseline avg).
• Scatter plot ridership recovery vs OTP recovery, colored by mode/subtype.
• Test correlation (Pearson and Spearman) between ridership recovery and OTP recovery.
• Test for regression to the mean: compute correlation between baseline OTP and OTP delta. A significant negative correlation indicates RTM, which would bias the recovery correlation.
• Stratify by route subtype (local, express, premium, rail) and test for group differences.

Data

Notes: Join on route_id and month; restrict to overlap period. Exclude routes missing from either dataset or with fewer than 6 months in either period.

Output

• output/recovery_scatter.png -- ridership recovery ratio vs OTP delta
• output/recovery_by_subtype.png -- box plots by route subtype
• output/recovery_data.csv -- per-route recovery metrics