With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the Western United States. Communities in wildland–urban interface (WUI) areas are at the greatest risk of fire. Such fires cause mass evacuations and can result in traffic congestion, endangering the lives of both citizens and first responders. While existing wildfire evacuation research focuses on social science surveys and fire spread modeling, they lack data on traffic operations during such incidents. Additionally, traditional traffic data collection methods are unable to gather large sets of data on historical wildfire events. However, the recent availability of connected vehicle (CV) data containing lane-level precision historical vehicle movement data has enabled researchers to assess traffic operational performance at the region and timeframe of interest. To address this gap, this study utilized a CV dataset to analyze traffic operations during a short-notice evacuation event caused by a wildfire, demonstrating that the CV dataset is an effective tool for accurately assessing traffic delays and overall traffic operation conditions during the selected fire incident. The findings also showed that the selected CV dataset provides high temporal coverage and similar travel time estimates as compared to an alternate method of travel time estimation. The study thus emphasized the importance of utilizing advanced technologies, such as CV data, to develop effective evacuation strategies and improve emergency management.

Road traffic accidents are a primary global concern, considering their magnitude and gravity and the subsequent negative impacts on the economy and general well-being of the public. Pakistan is no exception to this worldwide dilemma, with the highest incidence of road traffic accidents in recent years. Big cities like Peshawar are rapidly transforming into cities of congestion as they face severe road safety problems. Past Studies reveal that of the many factors affecting road traffic accidents in Pakistan, the factor of the driver is the deadliest, which is why this study aims to understand and model the effect of different driver characteristics on road traffic accidents. The database for this study was constituted via a survey questionnaire. Citing the complex relationship between driver characteristics and road traffic accidents, the Artificial Neural Networks Approach is used in this study. It provided a very supple and assumption-free methodology. The results from this study reveal that the ANN approach can provide an alternative to estimate the risk of a driver’s involvement in RTAs by eliminating the need for costly, time-consuming studies such as psycho-technical research.

Advanced driving technologies have the potential to transform the transportation sector. Specifically, the progress of autonomous vehicles (AVs) has caught the interest of governmental authorities, industrial groups, and academic institutions, with the goal of improving the driving experience, effectiveness, and comfort while also improving safety and flexibility and lowering vehicle emissions. Considering these facts, the purpose of this study is to assess the possible effects and advantages of AVs under diverse traffic situations in urban and rural environments. Knowledge of traffic behavior inside a certain road network is made easier by traffic microsimulation. PTV VISSIM (Verkehr In Städten—SIMulationsmodell) is among the microsimulation software programs that has attracted great interest because of its remarkable capacity to faithfully simulate traffic conditions. This review helps researchers choose the best methodological strategy for their individual study objectives and restrictions while using VISSIM. This research assesses the effect of AVs in different driving behavior and weather conditions in urban and rural situations using VISSIM and introduces traffic safety using the surrogate safety assessment model (SSAM). The study focuses on 10 parameters from the Wiedemann 99 car-following model and speed distribution to establish the correlation between weather conditions and surrogate safety measures (SSMs). The findings could lead to more accurate and authentic models of driving behavior and encourage the automotive industry to further equip AVs to operate efficiently in various environmental and driving conditions.

Highway–rail grade crossings (HRGCs) remain a critical safety problem despite decades of infrastructure investment. Moreover, the relationship between specific driver behaviors and crash severity across different geographic contexts remains poorly quantified. This study aimed to conduct a systematic review to (1) synthesize existing evidence on behavior-specific crash outcomes at HRGCs and (2) empirically validate findings from crash datasets. The study was conducted following PRISMA 2020 guidelines, identifying peer-reviewed studies (2015–2025) through structured searches of electronic databases. Inclusion criteria required studies to report quantitative associations between driver behavior and crash severity at HRGCs. In addition, crash records from two U.S. states (New Jersey and Texas; 2013 to 2022) were analyzed using multinomial logistic regression to assess empirical validity. Multinomial logistic regression analysis reveals that highway user action (HAU = 1) “going around gates” significantly increase the fatal risk. Empirical analysis confirmed these trends but revealed geographic variation: New Jersey showed a fatality odds ratio of 4.490 versus Texas’s 3.686 for a fatal risk, i.e., severity level 1. In addition, gender emerged as a significant factor in fatal crash risk, with female drivers facing elevated odds of fatal injury compared to male drivers: 1.464 in New Jersey and 1.817 in Texas. Despite these findings, three critical gaps remain in HRGCs: (1) predictive models focus on crash frequency rather than behavior forecasting, (2) data quality issues compromise of risk predictions, and (3) machine learning approaches lack interpretability for practitioners. This review demonstrates that identical driver behaviors produce different crash outcomes across geographic regions. Effective safety interventions need to address local risk factors instead of relying on a uniform national approach.

Highway–rail grade crossings (HRGCs) are locations where roadways and railway tracks intersect at the same level. Due to the shared level of travel and the substantial mass disparity between trains and highway users, collisions at these crossings tend to be catastrophic. As a result, HRGC crashes represent a major public safety concern in the United States. While previous studies have evaluated contributing factors to crash severity, there has been limited focus on the role of highway users’ action and its influence on crash severity. This study aims to examine all relevant factors, with a particular focus on highway user actions. The dataset, sourced from the Federal Railroad Administration’s database, includes data from six states between 2013 and 2022, specifically addressing severity and contributing factors. The proportional analysis highlights that highway user actions such as “went around the gate”, “did not stop”, and “stopped on the crossing” dominantly contribute to crash severity. A multinomial logistic regression was employed to identify significant determinants of crash severity. Odds ratio analysis reveals that “went around the gate” significantly increases the risk of fatal injuries across all six states, with odds ratios ranging from 3.45 in California to 4.55 in Georgia. The findings provide data-driven insights that can support the development of targeted safety countermeasures and intelligent traffic management strategies to enhance safety at HRGCs.