Traffic crashes cost society billions of dollars each year as a result of property damage, injuries, and fatalities. Additionally, traffic crashes have a negative impact on mobility, as they are a primary cause of non-recurring delay. With the Interstate 10 corridor between the ports of Los Angeles and Houston being one of the most vital links for goods movement across the United States, safety and mobility along this freeway, particularly for freight traffic, are of significant concern. This study, which utilized six years of crash data from the state of Arizona, explores factors affecting the frequency and severity of crashes along the Arizona portion of the I-10 corridor, with a particular focus on freight-related crashes. The safety performance along the I-10 is analyzed through the development of crash frequency and severity prediction models using integrated crash, roadway, traffic, and environmental data. Negative binomial and ordered logit models, with the incorporation of random parameters, were estimated to provide a detailed understanding of factors associated with freight-involved crashes and how they compare to non-freight crashes in terms of frequency and severity. The results showed that several roadway- crash-, vehicle-, and person-related variables were associated with the frequency and/or severity of crashes along the study corridor. These findings provide important insights which can be used to develop or plan countermeasures aimed at improving the safety and efficiency of freight travel, which may include new ITS technologies, and targeted educational and enforcement campaigns.

Driving safety analysis has recently experienced unprecedented improvements thanks to technological advances in precise positioning sensors, artificial intelligence (AI)-based safety features, autonomous driving systems, connected vehicles, high-throughput computing, and edge computing servers. Particularly, deep learning (DL) methods empowered volume video processing to extract safety-related features from massive videos captured by roadside units (RSU). Safety metrics are commonly used measures to investigate crashes and near-conflict events. However, these metrics provide limited insight into the overall network-level traffic management. On the other hand, some safety assessment efforts are devoted to processing crash reports and identifying spatial and temporal patterns of crashes that correlate with road geometry, traffic volume, and weather conditions. This approach relies merely on crash reports and ignores the rich information of traffic videos that can help identify the role of safety violations in crashes. To bridge these two perspectives, we define a new set of network-level safety metrics (NSM) to assess the overall safety profile of traffic flow by processing imagery taken by RSU cameras. Our analysis suggests that NSMs show significant statistical associations with crash rates. This approach is different than simply generalizing the results of individual crash analyses, since all vehicles contribute to calculating NSMs, not only the ones involved in crash incidents. This perspective considers the traffic flow as a complex dynamic system where actions of some nodes can propagate through the network and influence the crash risk for other nodes. The analysis is carried out using six video cameras in the state of Arizona along with a 5-year crash report obtained from the Arizona Department of Transportation (ADOT). The results confirm that NSMs modulate the baseline crash probability. Therefore, online monitoring of NSMs can be used by traffic management teams and AI-based traffic monitoring systems for risk analysis and traffic control.

Recently, there has been an increasing trend in traffic crash fatalities in the United States, including the State of Arizona. Given this fact, it is imperative to analyze factors that affect crash frequency, particularly those occurring on high-speed freeways. To address this issue, this study utilizes crash data from interstate freeways to explore factors affecting the frequency of crashes with a focus on the impacts of weather (precipitation, wind, etc.). Negative binomial models with random parameters were estimated to examine the effects of several variables on crash frequency. Characteristics of crashes involving different vehicle types were examined through estimation of separate models for passenger vehicle–, freight-, and motorcycle-involved crashes. The study also utilized different weather data sources which have not been considered jointly in previous research (both simulated and observed weather data), providing new insights on the feasibility of using simulated weather data in traffic safety analyses. The analyses of the impacts of weather on passenger vehicle crashes provided inconsistent results between different weather data collection methods; however, results were consistent for the freight-involved analysis. Ultimately, the findings provide important insights that may be useful in planning countermeasures to improve freeway safety and guide further research.

Pedestrian-train collisions at highway-railroad grade crossings (HRGCs), though relatively rare events, often result in severe injuries or fatalities. In the ten year period from 2008-2017, there were 1,470 pedestrian-train collisions reported in the United States resulting in 908 fatalities and 492 injuries according to the Federal Railroad Administration. Furthermore, pedestrians in over 27% of these crashes were coded as disregarding gates at HRGCs, indicating unsafe behavior at these crossings may lead to crashes. In order to examine this behavior (for both pedestrians and bicyclists) and add to the body of knowledge for this topic, this observational study utilized pedestrian and bicyclist behavior data extracted from videos of HRGCs. Several pedestrian and bicyclist behavior characteristics were extracted including the prevalence of pedestrian and bicyclist violation (e.g., disregarding active devices), pedestrian and bicyclist distraction, and pedestrian and bicyclist crossing speeds. Using these data, along with rail-grade crossing features, train crossing parameters, and demographic data, statistical analyses were performed to examine factors significantly associated with pedestrian and bicyclist violations, distraction, and crossing speeds at HRGCs. Ultimately, the results of this study provide important information which may be useful in planning strategies to mitigate the impacts of unsafe pedestrian or bicyclist behavior at HRGCs.