As a preferential treatment at signalized intersections, Transit Signal Priority (TSP) remains a key technology for enhancing transit performance. Recently, TSP systems based on General Transit Feed Specification (GTFS) Realtime have gained traction in the market, mainly because of their low implementation and maintenance costs. However, leveraging GTFS Realtime messages for TSP presents significant challenges, particularly because of two types of message delays: (1) high latency; and (2) long update intervals. Building on previous work that introduced regression models to compensate for message latency, three new machine learning models are proposed to more accurately predict future vehicle locations while mitigating these delays. To overcome the limitations of earlier regression approaches, a long short-term memory architecture for single-step prediction was developed, a long short-term memory architecture for multistep prediction was developed, and a Transformer-based architecture for multistep time series prediction was developed, which can address interval updating issues. The experimental results show that all three proposed models significantly outperform both previous regression models and five baseline statistical methods. These advancements improve the reliability and accuracy of GTFS-based Automatic Vehicle Location, reinforcing its role as a dependable data source for cloud-based TSP systems.

Transit signal priority (TSP) is a signal timing strategy to give priority to transit by adjusting the signal operation with the goal of reducing transit delay and improving reliability. While TSP can be a powerful tool, TSP deployments in the U.S. have often resulted in marginal improvements. The primary reasons for limited TSP effectiveness are short detection horizons for TSP requests (e.g., 10 s), near-side bus stops (i.e., located before crossing an intersection) that influence arrival times at the downstream traffic signal, and restrictive signal timing strategies (e.g., lock-out policies that inhibit TSP for a specified amount of time, coordinated control that offers little flexibility for TSP). This paper documents the impacts of a “next-generation” TSP system that couples with custom signal control logic for TSP through a field deployment in Portland, Oregon, U.S., using emerging data sources. The system uses cloud-based, predictive logic for estimating time of arrival, with predictions of bus arrivals available up to 2 min ahead of each intersection and updated continuously every 1 s. The custom signal control logic includes advanced TSP strategies that can take advantage of early prediction. Using data from high-resolution automatic vehicle location, analysis results show the custom signal controller logic with advanced prediction resulted in an average bus delay reduction of 29 s per intersection at major intersections (a reduction of 69% compared with baseline). Analyses using automated traffic signal performance measures and vehicle probe data showed these bus delay improvements were achieved with marginal impacts on motorists and without additional delay to pedestrians and bicycles.

Transit signal priority (TSP) is a strategy that provides preferential treatment at signalized intersections. TSP reallocates green time to reduce the delay of transit vehicles at traffic signals. To be effective, a transit vehicle (bus) must communicate its location to the traffic signal to make the reallocation of time beneficial. In GTFS (General Transit Feed Specification) Realtime, latency poses a significant challenge for the implementation of GTFS-based TSP. Using data from four transit agencies, this research identifies issues with current GTFS Realtime feeds and proposes a solution using machine learning algorithms to address latency compensation. Experimental results demonstrate that the performance of two machine learning models surpasses the baseline approach, which relies on hourly means for bus speeds and dwell times. This paper tackles multiple issues related to existing GTFS data, enhancing the practicality and feasibility of GTFS-based adaptive TSP. In contrast to conventional approaches focusing on estimation of bus arrival time, this paper emphasizes estimation of bus location and presents an effective method to compensate for latency and improve estimation of bus location and dwell time.

Automated traffic signal performance measures (ATSPMs) have been developed to organize data within a traffic signal, which will then be used to evaluate and monitor signal timing strategies. Past research has summarized the use of ATSPMs for performance-based management of a traffic signal system. The National Cooperative Highway Research Program Research Report 954 focused on many measures useful for general traffic, but did not consider transit agencies as specific stakeholders. ATSPMs do provide insights into measuring the number of preemption events, and the percentage of transit vehicles arriving on green, but additional information is needed to determine the effectiveness of transit signal priority or other transit preferential treatment strategies. This paper proposes a method for evaluating transit performance at signalized intersections more fully using the high-resolution data available within modern traffic signal controllers. The addition of transit-specific inputs within the ATSPM enumerations is used to produce transit-specific signal performance measures designed to be useful to practitioners. The methodology can be followed by other agencies seeking to enhance the capabilities of existing automatic vehicle location systems to further describe the performance at each signalized intersection. By incorporating transit into the ATSPM system, an agency can better assess the delay of buses, which can contribute to schedule adherence, on-time performance, and overall bus service. The methodology is presented as a prototype for further development and adaptation to individual agency objectives and data sources.

Bicyclists are vulnerable to door zone conflicts on urban roadways with on-street parallel parking adjacent to bicycle lanes; however, this conflict type is not robustly understood. This research sought to determine 1) the correlation between bicyclist experience and perceived risk of door zone conflicts, 2) bicyclist behavior in the door zone using velocity and lateral position, and 3) how time to the open door (TTD), defined as the time between the vehicle door opening and the bicyclist passing the door, and effective bicycle lane width, defined as the remaining lane width beyond the open door, contribute to the bicyclists' decision to come to a complete stop before an open vehicle door or to maneuver around it. A bicycling simulator was used to test TTD (short and long), and effective bicycle lane width (1, 2, and 3 ft). The survey results indicated that perceived risk of door zone crashes was high despite relatively few reports of door zone crashes. The survey results additionally indicated that whereas door zone crash involvement was reportedly low, door zone near-misses were more common. The results from the bicycling simulator showed that TTD did influence bicyclist velocity, and both TTD and effective bicycle lane width influenced bicyclist lateral position within the door zone. Lateral position data showed that more bicyclists chose to depart the bicycle lane in long TTD scenarios when the effective bicycle lane width was 2 ft. The results of this study suggest that some common bicycle lane practices give bicyclists few safe options to avoid door zone crashes.

The midblock pedestrian signal (MPS) operates as a coordinated actuated vehicular traffic signal that enables pedestrians to cross at midblock. The MPS has been used in multiple locations, including for more than 40 years in Los Angeles. It differs from a typical pedestrian crossing signal by allowing the red display for vehicular traffic to flash at the same time as the pedestrian timing for the crossing, reducing delay to vehicular traffic. The research team built a database of crash and roadway characteristics data for treated and control sites located in three states (California, Utah, and Texas). Three control groups were considered: all control sites (included intersections with two, three, or four legs and traffic control signals); two-leg sites with any type of pedestrian traffic control other than the MPS; and two-leg sites with nonactive or not present pedestrian traffic control devices. It was found that the MPS is associated with a reduction in the number of crashes involving pedestrians and a reduction in the number of all fatal and injury crashes when the control group is two-leg sites with nonactive or not present pedestrian traffic control devices. The following crash modification factors for the MPS were identified: 0.554 for crashes involving pedestrians; and 0.660 for all crashes.

There were many changes in 2020 that impacted our communities. One of the most significant was the call for racial justice across the nation. The Portland Bureau of Transportation (PBOT) in Oregon, USA, holds advancing equity and addressing structural racism as an overarching principle, and the daily gatherings and public's call for justice pushed the Signals & Street Lighting (SSL) Division to think more critically about our role in the city and how we can apply our work to this important mission.(1)

ITE International Past President and chair of the judge's panel for the Micromobility Competition Bruce Belmore, P.Eng., PTOE, VMA (F) shares his perspective on the value of the competition, how judges were selected, and what the projects presented ultimately say about the diversity and talent that exists within ITE.

Automated traffic signal performance measures (ATSPMs) are designed to equip traffic signal controllers with high-resolution data-logging capabilities which may be used to generate performance measures. These measures allow practitioners to improve operations as well as to maintain and operate their systems in a safe and efficient manner. While they have changed the way that operators manage their systems, several shortcomings of ATSPMs, as identified by signal operators, include a lack of data quality control and the extent of resources required to use the tool properly for system-wide management. To address these shortcomings, intelligent traffic signal performance measurements (ITSPMs) are presented in this paper, using the concepts of machine learning, traffic flow theory, and data visualization to reduce the operator resources needed for overseeing data-driven ATSPMs. In applying these concepts, ITSPMs provide graphical tools to identify and remove logging errors and data from bad sensors, to determine trends in demand intelligently, and to address the question of whether or not coordination may be needed at an intersection. The focus of ATSPMs and ITSPMs on performance measures for multi-modal users is identified as a pressing need for future research.

Walking as a transportation mode is associated with a number of benefits ranging from reductions in congestion and emissions levels to improvements in personal health. Therefore, many communities across the United States are eager to increase walk mode shares. Yet, the traffic signal timing and optimization models we use continue to focus only on automobile traffic. These legacy signal timing policies at intersections have prioritized vehicular movements, leading to large and sometimes unnecessary delays for pedestrians. Because pedestrian trips are short, delays at signalized intersections can affect pedestrians disproportionately and are a key factor in pedestrian non-compliance.(1)