Findings: Neighborhood Equity

Summary

There is a 25 percentage-point spread in OTP between the best- and worst-served neighborhoods (all modes pooled). When restricted to bus-only, the spread narrows to 20 pp but the bottom neighborhoods remain the same. The disparity is structural and stable over time -- all neighborhoods rise and fall together with the system. OTP is now computed from route-level averages (each route weighted once regardless of how many months of data it has), weighted by trip frequency.

Worst-Served Neighborhoods

The bottom neighborhoods are served entirely by bus, so their pooled and bus-only OTP are identical.

Best-Served Neighborhoods

Bus-Only Stratification (Simpson's Paradox Check)

Restricting to BUS-mode routes reveals that some neighborhoods' high pooled OTP is driven by rail:

Bon Air is a clear case of Simpson's paradox: it appears well-served in the pooled analysis (rank 13) but drops to rank 53 (bus-only) because its high pooled OTP is driven almost entirely by rail service. Beechview similarly drops 9 positions.

The bus-only spread (20 pp) is narrower than the pooled spread (25 pp), confirming that rail inflates the apparent equity of neighborhoods it serves.

Frequency-Weighting Effect

Comparing trip-weighted OTP to unweighted (equal weight per route) reveals where high-frequency service diverges from the route average:

• Mean gap: -0.27% (small on average -- the two measures broadly agree).
• Range: -6.1% to +5.7% (meaningful divergence for individual neighborhoods).

• Negative gap (Carrick, Regent Square): high-frequency routes underperform relative to infrequent ones. Riders experience worse OTP than the simple route average suggests.
• Positive gap (Troy Hill): high-frequency routes are more reliable, so the rider experience is better than the route average implies.

Observations

• Best-performing neighborhoods are served by light rail (Overbrook, Beechview are on the T line) or short bus routes.
• Worst-performing neighborhoods depend on long local bus routes with many stops (e.g., routes 61C, 71B, 77).
• The quintile gap (Q5 - Q1) tracks roughly in parallel over time, meaning the equity disparity is baked into route structure rather than worsening.
• 89 neighborhoods analyzed; 3,760 of 6,466 stops were excluded because they lacked a neighborhood assignment in the data.
• Routes with fewer than 12 months of OTP data are excluded from the analysis.

Caveats

• Sample size varies widely: neighborhoods have between 1 and 74 routes. The 25 pp spread should be interpreted with this context -- neighborhoods with only 1-2 routes have OTP estimates driven by a single route's performance, while those with 10+ routes have more stable estimates. A neighborhood served by one bad route will look worse than a neighborhood with a diversified mix.
• Panel balance (time series): The quintile time series uses an unbalanced panel. Neighborhoods gaining or losing routes over time may show OTP changes from composition shifts (a route entering or exiting), not performance changes. This could create artificial trends in the quintile chart, though the overall pattern of parallel movement suggests this effect is small.
• OTP is weighted by current trips_7d, not historical ridership. Neighborhoods where service was cut would show current-frequency weights, not past ones.
• The hood field had some invalid values (e.g., "0") that were filtered out.
• A neighborhood's OTP reflects the routes that pass through it, not the experience of residents who may transfer between routes.
• The unweighted measure treats each route equally regardless of how many trips it runs, which can overstate the importance of infrequent routes.

Review History

• 2026-02-11: RED-TEAM-REPORTS/2026-02-11-analyses-01-05-07-11.md — 7 issues (1 significant). Fixed time-pooled weighting (pre-aggregate OTP to route level before joining), added bus-only stratification revealing Simpson's paradox in Bon Air and Beechview, added NULL trips_7d filter, added minimum-month filter, documented panel balance caveat, added sample-size caveat, and clarified METHODS.md weighting description.

Methods: Neighborhood Equity

Question

Are certain neighborhoods or municipalities systematically underserved by on-time performance?

Approach

• Pre-aggregate OTP to route-level averages (AVG(otp) GROUP BY route_id, HAVING COUNT(*) >= 12), then join to route_stops and stops. This ensures each route contributes one weight regardless of how many months of data it has.
• Filter out route_stops rows with NULL trips_7d to avoid null-weight contamination.
• For each neighborhood, compute two OTP measures:
  • Weighted OTP: route-level average OTP weighted by trips_7d (weekly trip count per route-stop). Answers: "What OTP does the average trip in this neighborhood experience?"
  • Unweighted OTP: simple average across unique routes per neighborhood (deduplicated by route to avoid inflating routes with many stops). Answers: "What is the average reliability of routes serving this area?"
• Compute the gap (weighted - unweighted) per neighborhood to identify where high-frequency service over- or under-performs relative to the route average.
• Bus-only stratification: Repeat the weighted OTP analysis using only BUS-mode routes to check for Simpson's paradox (neighborhoods appearing well-served due to rail rather than bus performance).
• Rank neighborhoods by service quality.
• Examine whether the gap between best- and worst-served areas is widening or narrowing over time via rolling quintile assignment on monthly data.

Data

Output

• output/neighborhood_otp.csv -- OTP aggregated by neighborhood (weighted, unweighted, gap, and bus-only weighted)
• output/neighborhood_otp_bus_only.csv -- bus-only OTP by neighborhood
• output/neighborhood_equity.png -- top/bottom neighborhoods bar chart and quintile time series
• output/weighted_vs_unweighted_otp.png -- scatter plot comparing the two OTP measures and bar chart of the frequency-weighting effect per neighborhood