2020

Jonathan Waddell, Wayne State University
Stephen Remias, INRIX
Jenna Kirsch, Wayne State University
Stanley Young, National Renewable Energy Laboratory (NREL)

Scalable and actionable performance measures for traffic signal systems provide opportunities for practitioners to measure and improve the transportation network. Historically, traffic signal improvements have relied on scheduled signal retiming based on limited data collection, or on the public to call and alert engineers of an issue. This inefficient method of improving signal timing led to the creation of automated traffic signal performance measures (ATSPMs). These metrics rely on expensive infrastructure including detection and communications, which has produced barriers for numerous agencies to fully adopt. Recently, third party data providers have begun to release vehicle trajectory data, which allows for enhanced signal metrics with no investment in physical equipment. The purpose of this study is to demonstrate the use of these data and summarize the scalability of the created metrics. This work builds on previous efforts to quantify signal performance on nine intersections in Michigan. Ten signalized corridors in Columbus, Ohio were chosen to scale a performance assessment using crowdsourced trajectory data. A total of 136 intersections were assessed in two-hour intervals using data from all weekdays in 2017. High-level corridor summary metrics including average percent of vehicles stopping (18% - 32%), average delay (9.4 – 20.5 seconds), and level of travel time reliability (1.23 – 2.73) were calculated for each corridor direction. Intersection level metrics were also introduced which can be used by practitioners to identify problems, improve signal timings, and prioritize future infrastructure investments.

Pengfei (Taylor) Li, University of Texas, Arlington
Farzana Chowdhury, University of Texas, Arlington
Peirong (Slade) Wang, University of Texas, Arlington
Sayem Imtiaz, Mississippi State University

Traffic conditions are highly dynamic. Therefore, it is necessary to evaluate the arterial performance periodically to determine if a traffic signal system is functioning. The arterial performance has been evaluated based on arterial travel time/speed collected via the probe vehicles. More recently new approaches based on high-resolution traffic signal events are proposed by Dr. Bullock’s group at Purdue University, a.k.a. the “Purdue Coordination Diagram (PCD)”. Both arterial travel times/speeds and the PCDs can effectively reflect the level of arterial progression while some practical questions have been raised about how to synthesize these two methods. Inspired by such questions, we present our recent research efforts in designing a multi-objective evaluation framework for actuated traffic signal systems. The proposed framework integrates two common data sources for traffic signal operations: (i) the passive probe vehicle data and; (ii) high-resolution traffic signal event data. The standard PCD diagram and arterial LOS are synchronously displayed  and we define new multi-intersection traffic signal coordination diagrams to calculate the effective green bandwidth within the study period. New MOEs are defined based on the multi-intersection coordination diagram: Arrivals on Coordination (AoC) and Band Attainability (BA) to evaluate the effectiveness of traffic signal coordination. The proposed multi-intersection coordination diagram is especially suitable for actuated traffic signal coordination system because the effective mainline bandwidth varies from cycle to cycle due to the early return to green. We hope our efforts can provide new insights to practitioners on how to take advantages of multi-source traffic big data for traffic signal operations.

Marija Ostojic, Cubic | Trafficware
Archak Mittal, Leidos, Inc.
Hani Mahmassani, Northwestern University

Connected environments offer more information, improved data availability and quality which can lead to better decision making; new meaningful information adds new functionalities and opportunities to advance operational efficiency. Can traffic signal system efficiency and mobility be measured and enhanced in innovative and meaningful ways by combining two data sources - connected vehicle-generated and controller event logs? To analyzethis high-quality information when assessing the effectiveness of signal timing settings, new performance metrics are required since traditional ones no longer suffice the definition of state-representativeness. To develop and demonstrate the concept, thestudy used simulation data in a format that corresponds to high resolution data (signal status and vehicle positions) on a tenth of a second level. Vehicle trajectory information is processed, fused with control data, synthesized to produce "information" required to develop a signalized approach performance estimation method.The data analysis platform presented in this study is intended to comprehensively characterize the state of the signalized system and help identify causes of inferior intersection performance by defining a set of visual and quantitative success indicators.The practicality of this method is reflected in reducing the time and effort required by the existing signal design/retiming practice, since trajectory-signal signatures distinguish between incidents and retiming opportunities caused by changing traffic conditions

Howell Li, LSM Analytics LLC
Tom Platte, Indiana Department of Transportation
Jijo Mathew, Purdue University
William Smith, Indiana Department of Transportation
Enrique Saldivar-Carranza, Purdue University
Darcy Bullock, Purdue University

The rate of fatalities at signalized intersections involving heavy vehicles is nearly five times higher than for passenger vehicles. Previous studies have found that heavy vehicles are twice as likely to violate a red light compared to passenger vehicles. Current technologies leverage setback detection to extend green time for a particular phase and are based upon typical deceleration rates for passenger cars. Furthermore, dilemma zone detectors are not effective when the max out time expires and forces the onset of yellow. This study proposes the use of Connected Vehicle (CV) technology to trigger Force Gap Out (FGO) before a vehicle is expected to arrive within the dilemma zone limit at max out time. The method leverages position data from Basic Safety Messages (BSMs) to map-match virtual waypoints located up to 1,050ft in advance of the stop bar. For a 55mph approach, field tests determined that using a 6ft waypoint radius at 50ft spacings would be sufficient to match 95% of BSM data within a 5% lag threshold of 0.59s. The study estimates that FGOs reduce dilemma zone incursions by 34% for one approach and had no impact for the other. For both approaches, the total dilemma zone incursions decreased from 310 to 225. Although virtual waypoints were used for evaluating FGO, the study concludes by recommending that trajectory-based processing logic be incorporated into controllers for more robust support of dilemma zone and other emerging CV applications.