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.

No description available.

<p>  </p> <p>Driving simulators have been introduced by some American states’ department of transportation (DOT) as a supplementary tool to train their snowplow drivers. This is a costly investment that requires careful planning. Yet, there is a current lack of recent studies that holistically evaluate factors affecting the decision-making process on adopting the driving simulator in winter snowplow driver training. The current research aims to fill this gap by employing the theoretical framework of Technology-Organization-Environment (T-O-E) to explore factors affecting state DOTs’ decision-making process of adopting snowplow driving simulator in driver training. Relevant factors were identified first using a scoping review of literature, and then validated by interviews with DOT stakeholders. Subsequent findings from the state DOT survey suggests that perceived long-term effect on public safety, cost related to simulator training, and ease of the relocation of simulator are the top contributors when it comes to the factors affecting decision-making on adoption of snowplow driving simulator in training. The resulted T-O-E framework contains 11 factors cross-verified from various sources, in which most factors such as simulator fidelity and relative advantage were categorized to technology context under the T-O-E framework, while the main environment factor relates to normative and mimetic pressure. The paper contributes to academic research by applying T-O-E to offer decision making support for using simulator technology for training of snowplow drivers; and to practitioners by providing state DOT decision makers a framework to analyze different factors on adopting snowplow driving simulators in training.</p>