Findings: Stop Count vs OTP

Summary

There is a moderately strong negative correlation between the number of stops on a route and its average OTP. This finding holds for both all routes and bus-only analysis, ruling out Simpson's paradox as a confounder.

Key Numbers

• All routes: Pearson r = -0.53 (p < 0.001, n = 92)
• Bus only: Pearson r = -0.50 (p < 0.001, n = 89)
• Bus only: Spearman r = -0.49 (p < 0.001)
• Routes with < 50 stops: typically 80%+ OTP
• Routes with 150+ stops: typically below 60% OTP

Routes with fewer than 12 months of OTP data are excluded to avoid noisy averages from sparse observations.

Observations

• The bus-only correlation (r = -0.50) is nearly as strong as the all-routes correlation (r = -0.53), confirming that the effect is not driven by the BUS/RAIL mode split (Simpson's paradox). Stop count predicts OTP within the bus mode alone.
• The Spearman rank correlation (r = -0.49) is consistent with the Pearson, indicating the relationship is approximately monotonic without being driven by outliers or non-linearity.
• Every stop adds dwell time (boarding/alighting), traffic signal delay, and schedule recovery risk. The cumulative effect is substantial.
• Busway and rail routes tend to have fewer stops and dedicated right-of-way, giving them a double advantage.
• Route 77 (Penn Hills) is an extreme case: 258 stops and among the worst OTP in the system.

Implication

Stop consolidation -- reducing the number of stops on long routes -- is a common transit strategy for improving schedule adherence. This data strongly supports that approach for PRT's worst-performing routes.

Caveats

• Correlation is not causation. Routes with many stops also tend to serve congested corridors, cover longer distances, and carry more passengers -- all of which independently affect OTP.
• Temporal mismatch: Stop counts come from the current route_stops snapshot while OTP is averaged across all historical months (2019--2025). Routes that changed stop configurations during this period have a mismatch between their current stop count and earlier OTP observations. This is inherent to the available data and cannot be corrected without historical stop-count snapshots.

Review History

• 2026-02-11: RED-TEAM-REPORTS/2026-02-11-analyses-01-05-07-11.md — 6 issues (0 significant). Added 12-month minimum filter, temporal mismatch note in METHODS.md, all_n tracking, replaced manual regression with linregress, added min-n guard, updated METHODS.md for Pearson+Spearman.

Methods: Stop Count vs OTP

Question

Do routes with more stops have worse on-time performance? Each stop is another opportunity to fall behind schedule.

Approach

• Count distinct stops per route from route_stops.
• Compute average OTP per route from otp_monthly, requiring at least 12 months of data (HAVING COUNT(*) >= 12) to exclude routes with sparse observations.
• Create a scatter plot of stop count vs average OTP, colored by mode.
• Compute Pearson and Spearman correlation coefficients, both for all routes and for bus-only (to check for Simpson's paradox from mixing modes).
• Fit a simple linear regression line (bus-only, via scipy.stats.linregress).

Note: Stop counts come from the current route_stops snapshot, while OTP is averaged across all historical months. Routes that changed stop configurations over time will have a mismatch between their current stop count and the OTP values from earlier periods.

Data

Output

• output/stop_count_otp.csv -- per-route stop count and average OTP
• output/stop_count_vs_otp.png -- scatter plot with regression line