The Utah Department of Transportation (UDOT) Incident Management Team (IMT) program was expanded from 13 units in 2018 to 25 units in 2020. This study analyzes the benefits of the expansion of IMT performance measures, and the user impacts of crashes responded to by IMTs in 2018 and 2022. Data were obtained from the Utah Highway Patrol and UDOT to quantify the IMT performance measures of response time (RT), roadway clearance time (RCT), and incident clearance time (ICT) as well as the user impacts of the affected volume (AV), excess travel time (ETT), and excess user cost (EUC). Results indicate that the program expansion resulted in a significant decrease in IMT RT by 2.7 min, a 10% decrease in AV, and a 50% decrease in ETT and EUC.

This paper presents the findings of a dual-approach research study conducted for the Utah Department of Transportation to evaluate the effectiveness of variable message sign (VMS) treatments on Utah roadways. The study aimed to determine the impact of VMS treatments on mobility and safety for Utah’s unique road conditions and configurations. Two primary analyses were performed: a diversion rate analysis to assess the effectiveness of VMS messages on Utah freeways during incidents and a weather analysis to evaluate the effectiveness of VMS messages on driver speeds in Utah canyons during winter weather. The findings of the diversion rate analysis indicate that the activation of VMS messages during crash incidents increased diversion rates by 18 percent. The weather analysis showed that the activation of VMS messages warning drivers to slow down due to inclement weather conditions increased driver speeds by 0.23 mph (0.37 kph) during weather conditions, which was statistically significant, but holds little practical significance. The methodologies and findings of this study will assist departments of transportation and other interested parties in developing their VMS policies to more effectively influence driver behaviors.

Limited funding availability requires government agencies to focus transportation funding on locations most in need of safety improvements. The Two-Output Model for Safety (TOMS) was created to prioritize segments and intersections for safety analysis. The TOMS compiles input data, prepares the data files so that the format and content are consistent, and then two different processes occur. The first is to segment roadways based on five variables: average annual daily traffic, functional class, lanes, speed limit, and urban code. The second is to assign the physical characteristics of the roadway to each individual intersection. TOMS outputs a segment file and an intersection file used for statistical analysis and are the "two outputs" referenced by the name of the model. The segments and intersections are analyzed using severity and total number of crashes at the sites. An excess weighted risk score was developed using an equivalent property damage only value to analyze the severity and number of crashes concurrently. The segments and intersections with the highest excess weighted risk scores are prioritized as locations for safety funding. A report compiler is then executed to create two-page safety reports that contain roadway and crash information organized in a manner that allows governing agencies to identify how many crashes are occurring at a site and the manner of collision for the crashes. The research presented in this paper shows that the simultaneous use of intersection and segment analysis combined with excess weighted risk scores can provide insight into the prioritization of safety funding.

Unmanned Aerial Systems (UAS) can provide live coverage of traffic conditions during incident management events to supplement camera coverage in areas with poor or limited coverage. Eleven State Departments of Transportation and a State Department of Public Safety were interviewed to create the State of the Practice summary. Interviews were focused on understanding current implementations of UAS livestreams during incidents, although some discussions of developing technology are summarized. Four key findings from the State of the Practice are summarized for UAS livestream integrations. These findings concern the use of DJI UAS, tethered UAS, the role of satellite internet connections, and the various streaming solutions. Technology summary tables are provided for quick reference, with recommendations for future implementations. UAS livestreams are highly feasible and can offer substantial real-time benefits. However, there are practical limitations to existing technology and policy guidelines regarding safety and security.

Statistical modeling of vehicle crashes leads to a better understanding of how and why such crashes occur. Due to the irregular network structure of roadways, analyses are typically confined to a single roadway rather than considering the entire network collectively. Here, we present methodology to model crash risk of vehicle crashes on irregular roadway networks and estimate how that risk varies with road characteristics. We model vehicle crashes observed on a road network as a Poisson point pattern with a piecewise linear intensity surface. Further, we combine Bayesian additive regression trees (BART) and spatial data analysis to accurately explain the intensity surface allowing inference on the effect of road characteristics on crash risk. We illustrate the methodology using a dataset of vehicle crashes on Interstate highways in Utah.

Automated traffic signal performance measures (ATSPM) have become widely adopted and utilized by state and local agencies in the U.S. for collecting real-time traffic data 24 h a day, 7 days a week. These agencies have developed new performance measures and applications to address their local transportation planning needs. However, recent research has identified data quality issues in the collected data from ATSPM systems. Specifically, the traffic volumes collected through ATSPM exhibit data anomalies that do not accurately reflect the actual traffic patterns at intersections. As such, there is a need to address the data quality issues found in ATSPM datasets. The purpose of this paper is to evaluate the use of machine learning algorithms and statistical methods to predict traffic volume at intersections. Existing traffic volume data, along with additional metrics such as timestamps, weather conditions, crash data, and holidays, are evaluated to predict traffic volume and address the data anomalies present in ATSPM datasets. Two statistical methods and four machine learning algorithms are evaluated to determine their ability to predict traffic volumes. By comparing the root mean square error (RMSE) and the mean absolute percentage error (MAPE) between each model, the results demonstrate that the long short-term memory (LSTM) model exhibits the lowest error in predicting traffic volume compared with the other models. The LSTM model achieves an RMSE as low as 9.4 vehicles and an MAPE as low as 35%. By leveraging the LSTM model, traffic agencies can enhance the quality of their ATSPM data, enabling better decision-making for traffic operations by their engineers and planners.

Recent research has shown the power of large-scale regional traffic simulations—such as MATSim—to model the systemic impacts and costs of capacity-reducing incidents. At the same time, observational studies have illustrated the potential for traffic Incident Management Teams (IMTs) to reduce these impacts and costs on a local scale; mathematical optimization models have also attempted to scale or locate these programs. In this research, we connect these two separate lines of scholarly inquiry by simulating the dynamic response of an IMT fleet to incidents arising on a metropolitan highway network. We introduce a MATSim module that handles stochastically-generated incidents of varying severity, dispatches IMT to clear the incidents based on path distance and availability, and measures excess user costs based on the incidents. We apply this module in a scenario with data from the Salt Lake City, Utah metropolitan region. We demonstrate the potential use of the module through an illustrative experiment increasing the IMT fleet size with a collection of simulated incident days.

At the start of the 2019 to 2020 snow season, vehicle-to-everything (V2X) systems using dedicated short-range communication (DSRC) were placed on Utah Department of Transportation snowplows and traffic signal controllers on selected state routes. This study was conducted to understand the overall impacts of snowplows using V2X DSRC to request signal preemption. Roadside units (RSUs) were deployed on five corridors throughout the Salt Lake metropolitan area. Similar routes without RSUs were selected as a control and were used in the analysis to quantify results. Each snowplow on these corridors was equipped with an onboard unit (OBU). Based on data collected, analysis was performed on both traffic signal performance and vehicle performance data. Within the traffic signal performance analysis, it was found that the V2X DSRC system was utilized often, with snowplows requesting preemption in more 50% of the occasions they approached a signalized intersection. Of those requests, signal controllers granted preemption in over 80% of cases. On average, signal controller coordination was affected for less than 5 min. The vehicle performance analysis found that the snowplows on equipped routes had travel speeds that were less affected when there was snow than on corresponding not-equipped routes. Vehicle crash data also showed that there was a greater decrease in crashes on equipped routes than not-equipped routes. Anecdotal evidence gathered from snowplow drivers indicated that snowplows stopped less when using signal preemption. Drivers also noted a benefit to overall snow removal operations on corridors equipped with the V2X DSRC system.

In 2021, the Speed Limit Setting Procedure (SLS-Procedure) and Speed Limit Setting Tool (SLS-Tool) were developed as part of the National Cooperative Highway Research Program (NCHRP) Project 17-76. The SLS-Procedure and SLS-Tool emphasize the importance of context sensitivity during the speed limit suggestion process. To evaluate the potential for implementation on Utah roads, the research team analyzed 29 locations that were divided into 66 uniform segments that could be individually analyzed with the SLS-Tool. Then, the suggested speed limits that resulted from the SLS-Tool were compared to the recommendations from the Utah Department of Transportation (UDOT) speed studies. The tool suggested using the 50th percentile speed in 54% of segments due to various roadway conditions resulting in a lower speed limit for 23% of segments. This resource can aid an analyst in complying with state guidelines on when to depart from the standard 85th percentile speed requirement.

Automated traffic signal performance measures (ATSPMs) have garnered significant attention for their ability to collect and evaluate real-time and historical data at signalized intersections. ATSPM data are widely utilized by traffic engineers, planners, and researchers in various application scenarios. In working with ATSPM data in Utah, it was discovered that five types of ATSPM data anomalies (data switching, data shifting, data missing under 6 months, data missing over 6 months, and irregular curves) were present in the data. To address the data issues, this paper presents a method that enables transportation agencies to automatically detect data anomalies in their ATSPM datasets. The proposed method utilizes the moving average and standard deviation of a moving window to calculate the z-score for traffic volume data points at each timestamp. Anomalies are flagged when the z-score exceeds 2, which is based on the data falling within two standard deviations of the mean. The results demonstrate that this method effectively identifies anomalies within ATSPM systems, thereby enhancing the usability of data for engineers, planners, and all ATSPM users. By employing this method, transportation agencies can improve the efficiency of their ATSPM systems, leading to more accurate and reliable data for analysis.

Transportation agencies commonly identify and mitigate crash hot spots. Using a full roadway (i.e., including both intersections and segments) crash analysis is beneficial, but the results may be skewed towards locations with high intersection crashes. The purpose of this paper is to demonstrate the use of a combination of two unique, yet complementary crash hot spot analyses, one for intersections and one for roadway segments. The combination of the two allow for an in-depth analysis of causes associated with specific roadway conditions to identify locations that are experiencing more injury-causing crashes than predicted. The crashes on the state route network are separated into one of the two analyses; crashes within the influence area of major intersections become part of the intersection analysis and all other crashes become part of the segment-only analysis. Due to the separation between intersection- and segment-related crashes, safety concerns can be brought to light that might otherwise go unnoticed in a full network analysis. For example, crashes related to driveways or excessive queuing found in the segment analysis indicate a potential need for access management countermeasures. In addition, locations with both adjacent segment and intersection hot spots can be pinpointed as places for more in-depth analysis of the contributing circumstances surrounding the crashes at these locations. Agencies that use an intersection analysis and a segment analysis together to identify crash hot spots can benefit from an increased accuracy of hot spot locations. Furthermore, the approach to mitigating safety concerns can become clearer if hot spots are separated into intersections and segments.

Automated traffic signal performance measures (ATSPMs) are used to collect data concerning the current and historical performance of signalized intersections. However, transportation agencies are not using ATSPM data to the full extent of this “big data” resource, because the volume of information can overwhelm traditional identification and prioritization techniques. This paper presents a method that summarizes multiple dimensions of intersection- and corridor-level performance using ATSPM data and returns information that can be used for prioritization of intersections and corridors for further analysis. The method was developed and applied to analyze three signalized corridors in Utah, consisting of 20 total intersections. Four performance measures were used to develop threshold values for evaluation: platoon ratio, split failures, arrivals on green, and red-light violations. The performance measures were scaled and classified using k-means cluster analysis and expert input. The results of this analysis produced a score for each intersection and corridor determined from the average of the four measures, weighted by expert input. The methodology is presented as a prototype that can be developed with more performance measures and more extensive corridors for future studies.

Models to predict ramp meter queue length from traffic detector data are potentially useful tools in improving traffic operations and safety. Existing research, however, has been based on microscopic simulation or relied on extensive calibration of Kalman filter and related models to produce reliable queue length estimates. This research seeks to develop methodologies for improving and simplifying the calibration process of existing queue length models by applying loop detector data including volume, occupancy, and the metering rate data for ramp meters along I-15 in Utah. A conservation model and several variations of a Kalman filter model generated estimated queues that were compared to observed queue lengths in 60 s bins. A modified Kalman filter model and a new heuristic model derived from cluster analysis—the models that yielded the best results—provided queue length estimates that were generally within approximately eight vehicles of the observed queue length. Using the ramp metering rate, the queue length estimates were converted into wait times that were generally within approximately 30 s of the actual wait time, producing a viable method to predict wait time from up-to-the-minute traffic detection information with relatively little required calibration. The implementation of the ramp meter queue length and wait time estimation algorithms presented in this research will allow departments of transportation to better assess freeway and ramp conditions, which can then aid in reducing congestion throughout the freeway network.

A 2018 study of performance measures for the Utah Department of Transportation's (UDOT) Incident Management Team (IMT) program concluded that the program was cost effective and benefited Utah motorists. During the 2018 legislative session, UDOT received funding to expand its IMT program. To determine the benefits of expanding the IMT program, a comparison of performance measures for 2018 and 2020 incident data was conducted. In addition, data regarding the affected volume, the excess travel time, and the excess user cost associated with incident congestion were gathered. The effects of the COVID-19 pandemic affected traffic volumes during this study, and statistical analyses were utilized to account for volume differences between the two years. Results indicated that the expansion of the IMT program has allowed UDOT to respond more consistently to incidents and respond to a larger quantity of incidents over a larger coverage area and in extended operating hours.

An analysis was performed to evaluate the impact of changing the transit signal priority (TSP) requesting threshold on bus performance and general traffic, using field-generated data exclusively. Route 217, a conventional bus route that uses a dedicated short-range communication (DSRC)-based TSP system as part of its normal day-to-day operations, was analyzed over a three-month period from May 2019 through August 2019. The requesting thresholds evaluated for Route 217 were 3, 2, and 0 min, which stipulate how far behind schedule the bus must be to request TSP. For each requesting threshold, bus performance was evaluated through on-time performance (OTP), schedule deviation, travel time, and dwell time, while the traffic analysis was performed by evaluating split failure, change in green time, and the frequency at which TSP was served. A combination of observational and statistical analyses concluded with convincing evidence that OTP, schedule deviation, and travel time improve as the requesting threshold approaches zero with negligible impacts on general traffic. As the requesting threshold changed from 3, to 2, to 0 min, OTP increased 2.0% and 2.5%, respectively; mean schedule deviation improved by 15.9 s and 20.9 s, respectively; and travel time decreased at 75% of timepoints. Meanwhile, negative impacts to traffic occurred if an increase in split failure was measured after TSP was served, a phenomenon observed a maximum of once every 43 min. Thus, it is concluded that bus performance improves as the requesting threshold approaches zero with inconsequential impacts on general traffic.

The Utah Department of Transportation (UDOT) recently evaluated performance measures of its Incident Management Team (IMT) program. This study analyzed excess user costs (EUC) generated by incidents, including travel time and traffic volume affected. After data reduction and quality checks, 63 crash cases where IMT units responded were identified to have all data necessary for the analysis. The Mixed Procedure of SAS was employed for the EUC analysis. Results of the statistical analyses showed that, on average, for each minute delay of IMT response time (RT), $925 is added to the EUC with a range from $274 to $1,576 in 2018 dollars, and on average 93 more vehicles are affected with a range of 10-177 vehicles. These analysis results help UDOT to make decisions on how the program needs to be operated and managed to minimize EUC as they plan for future expansion of the program.

We report the results of an experiment to evaluate the impact of transit signal priority (TSP) on headway adherence for a bus rapid transit (BRT) system in Provo / Orem, Utah. The bus requests TSP based on its unpublished schedule, but users perceive only a headway. Quantile regression models estimated on raw timepoint data from the BRT system reveal that TSP significantly improves headway adherence, after controlling for peak times, direction, and cumulative trip dwell time. We also find that requiring the bus to be 2 minutes late before requesting TSP improves headway adherence more than allowing all buses to request TSP.