Transit priority systems have the potential to improve transit performance and address capacity constraints by giving priority to transit movements over other traffic. This research focused on the effectiveness of conditional transit priority or the manipulation of traffic signal timing plans to reduce delay of late transit buses. The integration of two transportation subsystems—traffic signals and public transit systems—was studied. These subsystems interact along a congested corridor where they share a common roadway infrastructure and transit signal priority (TSP) regulates the interaction between traffic signals, passenger traffic, and buses. Previous research has focused on the evaluation of bus TSP performance at the route level. In practice, it is important to understand not only TSP performance at the route level but also the impact of TSP at the level of the traffic signal intersection (e.g., to allow progression in major cross streets). TSP can significantly improve performance at specific intersections, even though at the route level TSP shows a more modest impact. This study proposed the integration of several data sets such as bus scheduling and location, passenger flows, and TSP requests to evaluate schedule adherence at the stop level and TSP performance at the level of the signalized intersection. A congested arterial corridor was analyzed and regression analysis was used to determine the key factors that affect bus travel time and schedule recovery for late buses. TSP was found to be most effective at lower-volume intersections where queuing was less problematic. Implications of the findings are analyzed and discussed.

Performance measures allow planners and engineers to monitor and evaluate transportation facilities or projects and to justify the allocation of funds among alternative transportation improvement options. To capture the impact of corridor congestion on freight vehicles, new tools and methodologies are developed to analyze data from commercial vehicles and produce performance measures like travel time, speed and travel time reliability. Since long freight corridors are comprised of segments with potentially different reliability characteristics, the objective of this paper is to develop a programming logic that will use available truck GPS (Geographical Positioning System) data to: (a) identify natural segments or regions in a corridor between urban centers, interstate junctions, or rural areas and (b) estimate corridor wide impacts of travel time unreliability. The case study presented here investigates the Interstate 5 (I-5) corridor in Oregon. After identifying corridor segments, this research applies statistical techniques to compute vehicle travel time and reliability for freight movements within each segment. The proposed methodology has been used successfully to indentify distinct segments and characteristics of travel time reliability in freight corridors. Travel time information was used to compute cost impacts of delays within rural and urban areas along the I-5 corridor. This research presents an advance in the processing and aggregation of GPS truck data to produce succinct yet informative performance measures and segments.

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