Empirical Performance Analysis of Bus Speed and Delay at Intersections for Emerging Spot Improvement Programs

Abstract

Many North American cities are increasingly interested in implementing small-scale localized spot treatments to surface routes as a simpler approach than top-down, disruptive route change, or redesign. This research seeks to support the identification of effective spot treatments at intersections using a systematic, data-driven approach. By analyzing key bus performance indicators in Toronto, this study developed insights into factors affecting peak-period bus speeds and delays at the segment and intersection levels for a wide variety of route and intersection configurations across eight high-frequency routes. Candidate treatments were then identified to improve bus performance. Data were sourced from the automatic vehicle location system, general transit feed specification, and a specialized ride check and GPS survey. Features of the approaches of 100 signalized intersections along the study routes were analyzed using K-means clustering, ordinary least squares regression, and regression trees, with target variables as their morning and evening peak operating speeds, segment-level delays, and signal delays. The results showed that long signal split is a significant contributor to higher operating speeds and lower delays, suggesting signal timing adjustments are an effective treatment. Clustering analysis suggested turning restrictions, particularly for right turns at intersections with near-side stops, could be effective, since turning volumes of similarly configured intersections were lower at locations with better transit performance. Regression analyses showed that queue jump lanes are an effective treatment if signal timing plans cannot be adjusted. The results from this study are intended to assist in informing transit authorities wishing to implement future spot improvement programs.