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.

Diverging diamond interchanges (DDIs) are an emerging interchange configuration that eliminates the need for left-turn phases in conventional diamonds and may be less expensive to construct than some alternative geometries. This paper examines signal timing for DDIs. DDI signal timing typically has used a two-phase configuration that reflects the two competing movements at the crossover points at each inter section of the DDI. This configuration inherently contains some inefficiency: (a) there is potential for internal queuing under two-phase configuration and (b) it is possible for the inflow demand to exceed outflow capacity of the interchange. This paper uses high-resolution event data to develop performance measures for evaluating operations at a DDI in Salt Lake City, Utah. Alternatives to the existing signal timing within the two-phase configuration are modeled and tested with a field deployment. The field deployment demonstrated the ability to prioritize ramp or through vehicles within the two-phase configuration. Additionally, a new three-phase configuration was developed and deployed to address the internal queuing that occurs with two-phase timing. With this new configuration, the flows from one DDI intersection to the other are balanced, and progression within the DDI is improved. With the implementation of the three-phase configuration, the percentage of vehicles arriving on green at the heaviest internal movement within the DDI increased from 53% to 92%. To illustrate these performance measures and improved DDI operation qualitatively, a video from a tethered unmanned aerial vehicle demonstrated the vehicle arrival characteristics by overlaying vehicle detection and signal state graphics on the video.

The COVID-19 pandemic, the most significant public health crisis since the 1918–1919 influenza epidemic, is the first such event to occur since the development of modern transportation systems in the twentieth century. Many states across the U.S. imposed lockdowns in early spring 2020, which reduced demand for trips of various types and affected transportation systems. In urban areas, the shift resulted in a reduction in traffic volumes and an increase in bicycling and walking in certain land use contexts. This paper seeks to understand the changes occurring at signalized intersections as a result of the lockdown and the ongoing pandemic, as well as the actions taken in response to these impacts. The results of a survey of agency reactions to COVID-19 with respect to traffic signal operations and changes in pedestrian activity during the spring 2020 lockdown using two case study examples in Utah are presented. First, the effects of placing intersections on pedestrian recall (with signage) to stop pedestrians from pushing the pedestrian button are examined. Next, the changes in pedestrian activity at Utah signalized intersections between the first 6 months of both 2019 and 2020 are analyzed and the impact of land use characteristics is explored. Survey results reveal the importance of using technologies such as adaptive systems and automated traffic signal performance measures to drive decisions. While pedestrian pushbutton actuations decreased in response to the implementation of pedestrian recalls, many pedestrians continued to use the pushbutton. Pedestrian activity changes were also largely driven by surrounding land uses.

Second-by-second GPS trajectories, called trip traces, of vehicles moving along an arterial provide the highest fidelity measure of corridor operations. However, large samples of such contiguous trajectories are not always possible because of varying techniques to reset probe vehicle IDs for data privacy, varying probe data penetration rates, and varying vehicle routing. This paper analyzes changes in segment travel time using the Mann–Whitney U test and proposes a method for creating a composite travel time metric using trip trace data. These techniques were applied to a four-corridor signal improvement and upgrade project in southeastern Salt Lake County. The study found that on average three out of the four corridors decreased in composite median travel time, by 32 s, 16 s, and 14 s. Interquartile range (IQR) was used to assess travel time reliability and the IQR travel time reduced (improved) on average by 33 s, 23 s, 18 s, and 1 s. In addition, a rank-sums method for statistically comparing the two composite travel time distributions is applied to the results. The four corridors had a total of 48 links and were evaluated during five time-of-day periods. Out of the 240 link-periods, the rank-sums analysis method found that overall, 68 link-periods improved and 13 link-periods slowed, at a 95% significance level. The annualized user benefit from the improvements was estimated at $2.2 million for the four corridors.

In the town of Moab, Utah, a combination of seasonal tourist traffic, heavy truck traffic, and high pedestrian volumes creates a unique traffic management challenge; Moab’s remote location adds additional challenges for real-time traffic monitoring and maintaining of signal timing plans. The Main Street corridor is a strong candidate for an adaptive traffic control system (ATCS). Peer-to-peer (P2P) communication and user-definable control logic were used to develop and implement a cost-effective ATCS called “P2P adaptive control” that used only the existing local controllers and detection. The adaptive control logic adjusts green time along the mainline in response to detector inputs while keeping the side streets at the minimum time needed for pedestrian service. System performance was evaluated by comparing performance measures generated from high-resolution signal controller data before and after implementation of P2P adaptive control. The P2P adaptive control increased the through bandwidth of the corridor and reduced the number of split failures (i.e., the number of phase occurrences with insufficient green). Future work will include adjusting the algorithm to improve service on side streets and expanding P2P adaptive control to additional signals expected to be constructed in the area.