Findings: Transfer Hub Performance

Summary

Higher-connectivity stops appear to have modestly worse OTP in stop-level data, but this finding does not survive correction for non-independence. At the route level (independent observations), the correlation between stop connectivity and OTP is not significant (r = -0.15, p = 0.16). The apparent "hub penalty" is a composition effect driven by inflated sample size at the stop level.

Key Numbers

Stop-level (n=6,209 -- non-independent, inflated power):

• Pearson r = -0.17 (p < 0.001)
• Spearman rho = -0.32 (p < 0.001)

Route-level (n=93 -- independent observations):

• Pearson r = -0.15 (p = 0.16)

Route-level, bus only (n=90):

• Pearson r = -0.09 (p = 0.39)

Observations

• The stop-level correlations (r = -0.17, rho = -0.32) are statistically significant but misleading: the 6,209 "stops" are not independent observations. Stops on the same route share the same underlying OTP, so the effective sample size is closer to ~90 (the number of distinct routes). With n_eff ~ 90, a correlation of r = -0.15 yields p = 0.16, which is not significant.
• The route-level analysis confirms this: average stop connectivity per route has no significant relationship with route OTP (r = -0.15, p = 0.16). Within bus routes only, the relationship is even weaker (r = -0.09, p = 0.39).
• The 3.5 pp tier gap (simple 69.5% vs hub 66.4%) is real in the raw data but reflects a composition effect: hubs are served by many routes including poor-performing local bus routes, which drag down the average. The hub location itself is not causing worse OTP.
• The busiest hub (East Busway Penn Station, 27 routes) actually outperforms the system average (72.1%) because it sits on dedicated right-of-way.

Implication

Being a transfer hub does not independently predict worse OTP. The apparent hub penalty is driven by which routes converge there. Policy should focus on improving the poorly-performing routes themselves, not on the hub locations.

Caveats

• This analysis uses route-level OTP projected onto stops (ecological fallacy). We don't have stop-level OTP data; a route's on-time performance may vary along its length.
• The route-level analysis uses "average stop connectivity per route," which is itself an approximation. A more direct test would require stop-level arrival data.

Review History

• 2026-02-10: RED-TEAM-REPORTS/2026-02-10-analyses-12-18.md — 3 issues (2 significant). "Hub penalty" finding retracted; route-level analysis now primary.

Methods: Transfer Hub Performance

Question

Do passengers at major transfer hubs -- stops served by many routes -- experience worse reliability than passengers at simpler stops? This matters because transfer hub passengers are disproportionately transit-dependent and a missed connection at a hub cascades into longer wait times.

Approach

• Count distinct routes per stop from route_stops to measure connectivity (number of routes serving each stop).
• For each stop, compute a trip-weighted average OTP across all routes serving it.
• Classify stops as hubs (5+ routes), medium (2-4 routes), or simple (1 route).
• Compare OTP distributions across these tiers.
• Identify the busiest hubs and their OTP.
• Scatter plot of connectivity vs stop-level OTP.

Data

Output

• output/hub_performance.csv -- per-stop connectivity, OTP, and classification
• output/connectivity_vs_otp.png -- scatter plot of routes-per-stop vs OTP
• output/hub_tier_comparison.png -- box plot of OTP by hub tier