Next Generation Traffic Signal Performance Measures: Leveraging Connected Vehicle Data

Abstract

High-resolution connected vehicle (CV) trajectory and event data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, these data sets provide unique opportunities to build on and expand previous advances on traffic signal performance measures and safety evaluation. This report is a synthesis of research focused on the development of CV-based performance measures. A discussion is provided on data requirements, such as acquisition, storage, and access. Subsequently, techniques to reference vehicle trajectories to relevant roadways and movements are presented. This allows for performance analyses that can range from the movement- to the system-level. A comprehensive suite of methodologies to evaluate signal performance using vehicle trajectories is then provided. Finally, uses of CV hard-braking and hard-acceleration event data to assess safety and driver behavior are discussed. To evaluate scalability and test the proposed techniques, performance measures for over 4,700 traffic signals were estimated using more than 910 million vehicle trajectories and 14 billion GPS points in all 50 states and Washington, D.C. The contents of this report will help the industry transition towards a hybrid blend of detector- and CV-based signal performance measures with rigorously defined performance measures that have been peer-reviewed by both academics and industry leaders.

Comparison at Scale of Traffic Signal Cycle Split Failure Identification from High-Resolution Controller and Connected Vehicle Trajectory Data

“…Over the last few years, crowdsourced connected vehicle (CV) trajectory data has emerged as a new dataset capable of producing and expanding existing traffic signal and arterial performance measures [7,[22][23][24][25][26][27][28][29][30][31][32][33]. With over 500 billion records generated each month in the United States, CV data enable the development of highly scalable techniques since no detection or communication equipment is required.…”

“…Various traffic signal performance measures have been developed in the last two decades that enable the assessment of progression and capacity utilization at intersections [6][7][8]. A popular metric used to evaluate intersection congestion is the occurrence of split failures, also known as cycle failures.…”

“…A split failure event occurs when a signal cycle does not provide enough green time to serve vehicles waiting on a particular movement [9]. Since split failures can be estimated from different data sources [6,7], it is crucial to evaluate the differences of using either approach to provide practitioners with insights on how to effectively interpret results.…”

Using Connected Vehicle Data to Evaluate National Trip Trends

“…Emerging connected vehicle (CV) data can act as an alternative data source to potentially deliver a more scalable method to measure temporal and spatial usage of passenger vehicles in near real-time for a variety of applications, including trip origin-destinations, quantifying traffic congestion, travel mode choice, traffic safety, and infrastructure planning [16][17][18][19][20]. Early studies have shown that these CV datasets capture around 500 billion vehicle records in a month nationwide [21] for the United States, with a rough market penetration of 5-7% [22].…”

“…This data has already shown wide-ranging applicability to prioritize traffic signal retiming, monitor mobility in varying weather conditions, and assess work zone mobility [20,23,24]. Including additional probe, CV data sources provide unique opportunities to evaluate transportation infrastructure and prioritize investments, including pavement markings, pavement quality, and the deterioration of roadway conditions during winter storms using friction data [21,25].…”