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Transit Performance Knowledge Base
PRT OTP Analysis
Pipeline, analyses, and source lineage for on-time performance and ridership research.
Built 2026-04-03 20:09 UTC · Commit 7c56b9a
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Pipeline
Scheduled Trips ETL
Loads monthly scheduled trip counts and schedule periods from WPRDC exports.
Weather ETL
Fetches NOAA daily weather and aggregates monthly features for OTP modeling.
Traffic Overlay ETL
Computes route-level traffic exposure metrics by spatially joining GTFS and PennDOT AADT data.
NTD Ridership ETL
Loads national monthly ridership benchmark data from the NTD workbook.
NTD Annual Service ETL
Loads NTD TS2.2 annual VRH, VRM, UPT, and VOMS data into prt.db for service-level comparative analysis across US transit agencies (1991-2023).
Analyses
01 - System-Wide OTP Trend
Tracks the overall PRT on-time performance trend from 2019 through 2025, including COVID impact and recovery.
02 - Mode Comparison
Compares on-time performance across service modes (BUS, RAIL, INCLINE) and route types (local, limited, express, busway).
03 - Route Ranking
Ranks routes by average OTP, trend direction, and volatility to identify best/worst performers and most (in)consistent routes.
04 - Neighborhood Equity
Investigates whether on-time performance varies systematically by neighborhood and municipality.
05 - Anomaly Investigation
Identifies and investigates sharp OTP drops that may indicate route restructuring, detours, or data quality issues.
06: Seasonal Patterns
Decomposes route-level OTP into trend, seasonal, and residual components to identify whether summer or winter months systematically affect performance.
07: Stop Count vs OTP
Tests whether routes with more stops have worse on-time performance, using a scatter plot of stop count against average OTP with mode-based coloring.
08: Hot-Spot Map
Visualizes stop-level on-time performance on a geographic scatter plot to identify corridor-level bottlenecks and clusters of poor performance.
09: Incline Investigation
Audits the Monongahela Incline data across all database tables to determine why it appears in OTP data with zero/null values.
10: Trip Frequency vs OTP
Tests whether high-frequency routes have worse on-time performance, using weekday trip counts as a proxy for service frequency.
11: Directional Asymmetry
Investigates whether routes with a structural imbalance between inbound and outbound trip frequency have worse on-time performance.
12: Route Geographic Span vs OTP
Computes the geographic span (max distance between any two stops) for each route and tests whether longer routes have worse on-time performance, disentangling route length from stop count.
13: Cross-Route Correlation Clustering
Computes pairwise OTP time-series correlations between all routes and uses hierarchical clustering to identify groups of routes whose performance rises and falls together.
14: COVID Recovery Trajectories
Measures how far each route's OTP has recovered relative to its pre-COVID baseline and identifies route characteristics that predict faster or slower recovery.
15: Municipal/County Equity
Aggregates on-time performance by municipality and county to assess service reliability equity at a broader geographic level than neighborhood analysis (Analysis 04).
16: Transfer Hub Performance
Identifies high-connectivity stops (served by many routes) and tests whether passengers at transfer hubs experience worse OTP than those at low-connectivity stops.
17: Weekend vs Weekday Service Profile
Tests whether routes with different weekend-to-weekday service ratios show different OTP patterns, distinguishing commuter-oriented routes from all-day service routes.
18: Multivariate OTP Model
Combines stop count, mode, bus subtype, geographic span, and service profile into a single OLS regression model to quantify relative importance and total explained variance.
19 - Ridership-Weighted OTP
Compute system OTP weighted by actual average daily ridership instead of scheduled trip frequency, to measure the average rider's experience.
20 - OTP → Ridership Causality
Test whether OTP declines predict subsequent ridership losses using lagged correlation and Granger causality tests.
21 - COVID Ridership vs OTP Recovery
Compare ridership recovery trajectories with OTP recovery trajectories post-COVID to identify whether ridership recovery degrades OTP.
22 - Passenger-Weighted Delay Burden
Estimate late rider-trips per route per month by combining ridership with OTP to identify where the most total human impact occurs.
23 - Garage-Level Performance
Compare OTP and ridership trends across PRT garages (Ross, Collier, East Liberty, West Mifflin) to surface operational differences.
24 - Weekday vs Weekend Ridership Trends
Track how weekday, Saturday, and Sunday ridership patterns shifted post-COVID and whether weekend ridership share correlates with OTP.
25 - Ridership Concentration & Equity
Measure what share of total system ridership is carried by the lowest-OTP routes using Lorenz curves and Gini coefficients.
26 - Ridership in Multivariate OTP Model
Add ridership as a predictor to the Analysis 18 OLS model to test whether it adds explanatory power beyond stop count, span, and mode.
27 - Traffic Congestion and OTP
Tests whether PennDOT AADT traffic volume explains OTP variance beyond structural features
28 - Weather Impact
Tests whether weather (precipitation, snow, temperature) explains OTP variance or the counterintuitive seasonal pattern from Analysis 06.
29 - Service Change Impact on OTP
Do schedule changes (pick period transitions) correlate with OTP shifts?
30 - Service Level vs OTP Longitudinal
Within-route panel: does changing trip frequency improve or degrade OTP?
31 - Stop Consolidation Candidates
Identify low-usage stops that could be consolidated to improve OTP, leveraging the finding that stop count is the strongest OTP predictor.
32 - Shelter Equity
Assess whether bus shelters are equitably placed relative to stop-level ridership volume and demographics.
33 - Pandemic Ridership Geography
Map the spatial pattern of stop-level ridership loss and recovery between pre-pandemic and pandemic periods.
34 - Ridership Concentration (Pareto)
Quantify how concentrated ridership is across stops and test whether concentration correlates with route OTP.
35 - Boarding/Alighting Flow Analysis
Analyze net boarding-alighting flows by stop and direction to identify major trip generators and attractors.
36 - National Ridership Growth (2019 vs 2024)
Compare 2019-to-2024 ridership recovery across the 150 largest US transit agencies using NTD data; rank PRT nationally.
37 - Peer City Ridership Comparison
Track indexed monthly ridership for Pittsburgh and 7 peer cities from 2019-2025 using NTD data; compare recovery trajectories and mode splits.
38 - Downtown Recovery Gap
Routes serving downtown recovered less ridership post-COVID, but the gap is driven mainly by commuter/express services (34% recovered) rather than local buses (59% recovered). Within local routes, downtown dependence alone does not predict worse recovery.
39 — National Service Cuts (2019 vs 2024)
PRT cut 13% of vehicle revenue hours between 2019 and 2024, ranking 104th of 150 large agencies. Contrasts supply-side service cuts with demand-side ridership loss to distinguish agencies that cut service from those that lost riders despite maintaining service.
40 - Peer City Dashboard
Pittsburgh lost 41% of riders and 13% of service hours between 2019 and 2024 — roughly middle-of-the-pack among 8 peer cities. But it is the only peer where fare revenue per trip increased ($1.57 to $1.70), suggesting PRT maintained fares while others discounted. Farebox recovery fell everywhere, though Pittsburgh's 12.8% remains second-highest among peers.
41 - Operating Cost Drivers
PRT spends $3.34 per trip on vehicle maintenance — the highest of 8 peer cities and 47% above average. Bus fleet age does not explain the gap (PRT's buses are mid-age at 7.1 years), but the light rail fleet averages 32 years old. General administration is PRT's leanest cost category at $2.23 per trip.