Reporting Framework for Arterial-Level Traffic Signal Performance Measures Estimated from Connected Vehicle Trajectory Data

Traffic signal performance measures based on connected vehicle (CV) trajectory data can provide practitioners actionable information on the operational state of their systems. Agencies need visualization tools that can enable them to quickly assess arterial-level performance by time-of-day (TOD) to identify challenges and opportunities. This document presents a framework to report four signal performance measures over a corridor for all relevant movements, including traditional Highway Capacity Manual (HCM) level of service (LOS), arrivals on green (AOG), split failures (SF), and downstream blockage (DSB). The reporting framework can provide up to 3,072 performance data points per intersection since it provides information for eight different movements and four performance measures for every 15-minute period over 24 hours. To demonstrate implementation, 14 reports displaying performance estimations for 12 corridors, located in 11 different states, are presented. This reporting approach can facilitate the determination of possible mitigation strategies by contrasting operational conditions between movements by TOD

Connected Vehicle-Centric Dashboards for TMC of the Future

The adoption of dashboards and tools into Traffic Management Centers (TMC) has been growing with advancements in connected vehicle (CV) data. These tools are now being utilized—not only for analyzing work zones, severe crashes, winter operations, and traffic signals—but also to provide measures for characterizing overall system mobility, resiliency, and after-action assessments. Previous studies have extended the concepts to include the enhanced trajectory-based CV data into dashboards that aid agencies in assessing and managing roadways. This study presents the extension of these tools that further improve the value and insights provided. It also highlights the evolution of CV data in Indiana. CV data in Indiana has grown to over 364 billion statewide records. Average overall penetration rate of CV data on interstates has increased to 6.32% in May 2022 with trucks accounting for 1.7%. Sections of this study also present the impact of rain intensity on interstate traffic and incorporation of such weather data into heatmap and other tools. Updates to existing dashboards and a summary of newly developed dashboards are synopsized in this report. Finally, this report presents a case study that highlights the use of these tools to assess and analyze the impact of tornadoes on interstate traffic in Indiana. As interest in these tools has grown, this project facilitated continued improvements and added features to meet the needs of INDOT and their partners

Next Generation Traffic Signal Performance Measures: Leveraging Connected Vehicle Data

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