![]() ![]() Using Spearman’s rank-order correlation, results indicated a strong correlation between hard-braking events and rear-end crashes occurring more than 400 ft upstream of an intersection. This study compared rear-end crash data over a period of 4.5 years at 8 signalized intersections with weekday hard-braking data from July 2019. In a typical month, over six million hard-braking events are logged in the state of Indiana. Enhanced probe data that provides date, time, heading, and location of hard-braking events has recently become available to agencies. Typical safety improvements at signalized intersections are identified and prioritized using crash data over 3–5 years. The paper concludes by commenting on current probe data penetration rates, indicating that these techniques can be applied to corridors with annual average daily traffic of ~15,000 vehicles per day for the mainline approaches, and discussing cloud-based implementation opportunities. A case study is presented that summarizes the performance of an eight-intersection corridor with four different timing plans using over 160,000 trajectories and 1.4 million GPS samples collected during weekdays in July 2019 between 5:00 a.m. These waypoints are then converted into trajectories that are relative to the intersection. Geo-fences are created at specific signalized intersections to filter vehicle waypoints that lie within the generated boundaries. This paper proposes using high-fidelity vehicle trajectory data to produce traffic signal performance measures such as: split failure, downstream blockage, and quality of progression, as well as traditional Highway Capacity Manual level of service. Over 400 billion vehicle trajectory points are generated each month in the United States. Currently, most traffic signal performance measures are obtained from high-resolution traffic signal controller event data, which provides information on an intersection-by-intersection basis and requires significant initial capital investment. Operations-oriented traffic signal performance measures are important for identifying the need for retiming to improve traffic signal operations. The study demonstrates the utility of CV trajectory data for obtaining high-level details as well as drilling down into the details. Comparing visualizations of only end-to-end journeys on the corridor with all journeys on the corridor reveals several features that are only visible with the latter. Next, the details of the operation are examined with the use of two visualization tools: a cyclic time space diagram, and an empirical platoon progression diagram. A series of high-level performance measures are used to evaluate overall performance by time of day and direction, with differing results by metric. ![]() Results for real-world operation of an eight-intersection signalized arterial are presented. With the use of CV data, it is possible to assess not only the movement of traffic on the corridor but also to consider its origin-destination (O-D) path through the corridor, and the tools can be applied to select O-D paths or to all O-D paths in the corridor. ![]() These include both performance measures for high-level analysis as well as visualizations to examine details of coordinated operation. ![]() This paper presents a several tools using CV data to evaluate the quality of signal progression. Emerging connected vehicle (CV) data sets have recently become commercially available that enable analysts to develop a variety of powerful performance measures without a need to deploy field infrastructure. ![]()
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