Dedicated short-range communication (DSRC) systems, a form of vehicle-to-everything (V2X) systems, were placed on Utah Department of Transportation (UDOT) snowplows to request signal preemption. The study took place along five state routes in the Salt Lake City metropolitan area. Snowplows and intersections were equipped with the technology to communicate and process requests for signal preemption. Signal performance and vehicle performance analysis were performed to understand the impacts that snowplows requesting signal preemption had. Signal performance analysis was done to determine how snowplows with V2X systems using DSRC affected signals. Vehicle performance analysis was done to see if plowing and traffic efficiency and performance were improved, as well as evaluating safety implications of signal preemption. To perform the signal performance analysis, V2X data were collected to understand how often signal preemption was requested by snowplows, how often it was granted by signal controllers, and how long preemption requests affected signal controller timing. Snowplows requested preemption over 50 percent of the time they approached a signalized intersection. Of messages that requested signal preemption, over 80 percent were granted. On average, signal controllers are affected by preemption processing for less than 5 minutes. This shows that the system works as designed, is used often, and does not have adverse effects on signal controller. Data for vehicle performance analysis included analysis of snowplow speed data, general travel speed data, and crash data. These were collected to analyze the effects of snowplows requesting signal preemption on vehicle performance. The analysis showed that snowplow speeds are not changed due to the signal preemption system, but the number of times snowplows stopped was reduced. General travel speeds on equipped routes were more consistently closer to the speed limits than not equipped routes. Crash data showed a greater negative decrease on equipped routes than on not equipped routes. These findings showed minimal changes or impacts to vehicle performance, but anecdotal evidence from snowplow drivers indicates benefits from the system overall. There were various limitations in the analysis. Data granularity differed among datasets, making comparison between the different datasets difficult without reducing data integrity. Some datasets did not have much data, making statistical significance unclear. With these data limitations, conclusions were drawn, but do not fully describe all the potential benefits and impacts of snowplows with V2X systems that use DSRC to request signal preemption. Additional research is needed to better understand the impacts that snowplows requesting signal preemption has on different maintenance metrics, such as fuel usage and time spent plowing. It is also recommended that data used is explored for ways to improve the granularity.

<p>Road networks are significantly affected by traffic signal operations, which contribute from 5% to 10% of all traffic delay in the United States. It is therefore important for agencies to systematically monitor signal performance to identify locations where operations do not function as desired and where mobility could be improved.</p> <p><br></p> <p>Currently, most signal performance evaluations are derived from infrastructure-based Automated Traffic Signal Performance Measures (ATSPMs). These performance measures rely on high-resolution detector and phase information that is collected at 10 Hz and reported via TCP/IP connections. Even though ATSPMs have proven to be a valid approach to estimate signal performance, significant initial capital investment required for infrastructure deployment can represent an obstacle for agencies attempting to scale these techniques. Further, fixed vehicle detection zones can create challenges in the accuracy and extent of the calculated performance measures.</p> <p><br></p> <p>High-resolution connected vehicle (CV) trajectory data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, this data set provides unique opportunities to derive accurate signal performance measures without the need for infrastructure upgrades. This dissertation provides a comprehensive suite of CV-based techniques to generate actionable and scalable traffic signal performance measures.</p> <p><br></p> <p>Turning movements of vehicles at intersections are automatically identified from attributes included in the commercial CV data set to facilitate movement-level analyses. Then, a trajectory-based visualization from which relevant performance measures can be extracted is presented. Subsequently, methodologies to identify signal retiming opportunities are discussed. An approach to evaluate closely-coupled intersections, which is particularly challenging with detector-based techniques, is then presented. Finally, a data-driven methodology to enhance the scalability of trajectory-based traffic signal performance estimations by automatically mapping relevant intersection geometry components is provided.</p> <p><br></p> <p>The trajectory data processing procedures provided in this dissertation can aid agencies make data-driven decisions on resource allocation and signal system modifications. The presented techniques are transferable to any location where CV data is available, and the scope of analysis can be easily varied from the movement to intersection, corridor, region, and state level.</p>