Traffic Safety Measures and Assessment

The research presented in this paper examines a new proposed approach for identifying safety improvement sites on rural highways. Unlike conventional approaches, the proposed approach does not require crash history, but rather utilizes classified variables for traffic volume, geometric features, and roadside characteristics that do not require access to exact data or extensive technical expertise. The research validates the performance of the proposed approach using field data from a large sample of rural two-lane highway segments in the state of Oregon including traffic, roadway, and crash data. A mathematical model for the prediction of the EB expected number of crashes using multivariate regression analysis is developed and used as the network screening criterion. The model’s independent variables include roadway geometry, roadside characteristics, and traffic exposure, while the dependent variable is the EB expected number of crashes. Using observed crash history as a reference, the performance of the proposed approach was compared to two of the well-established methods in practice, namely, the Empirical Bayes (EB) and the potential for safety improvement (PSI) methods. The study results suggest that by using crash density for highway segments, the performance of the proposed method was lower than that of the EB and PSI methods. This is despite the high R-square value of the predictive model used in the proposed method. However, when using crash frequencies for highway segments, the performance of the proposed method was found comparable to the well-established EB and PSI methods. Full article

The number of motor vehicles on the road is constantly increasing, leading to a rise in the number of traffic accidents. Accurately identifying the factors contributing to these accidents is a crucial topic in the field of traffic accident research. Most current research focuses on analyzing the causes of traffic accidents rather than investigating the underlying factors. This study creates a complex network for road traffic accident cause analysis using the topology method for complex networks. The network metrics are analyzed using the network parameters to obtain reduced dimensionality feature factors, and four machine learning techniques are applied to accurately classify the accidents’ severity based on the analysis results. The study divides real traffic accident data into three main categories based on the factors that influences them: time, environment, and traffic management. The results show that traffic management factors have the most significant impact on road accidents. The study also finds that Extreme Gradient Boosting (XGBoost) outperforms Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT) in accurately categorizing the severity of traffic accidents. Full article

The current study aimed to develop a relationship between surrogate safety indicators and human judgement of severity. It has been demonstrated that human observers frequently display excellent agreement when asked to assess traffic incidents by their level of danger. Therefore, this research examines, in depth, how surrogate safety indicators might be used to represent human judgement of the severity of traffic incidents. This study analyzed 1141 traffic incidences of various vehicle categories according to their behavior during an interaction. Furthermore, ordinal logistic regression was used to develop a model for evaluating the most significant objective indicators relevant to people’s perceptions of danger. The findings indicated that the most crucial data for determining the severity of a traffic event is found in its earliest conditions, which are defined as the beginning of an evasive action. Moreover, factors affecting both closeness and collision consequences are significant and should be included in severity metrics. Full article

In developing nations, motorcycles are a ubiquitous mode of transportation on local road networks, contributing to a substantial frequency of traffic accidents and fatalities. These occurrences exhibit significant discrepancies between urban and rural road environments. Specifically, urban roads exhibit greater traffic density and more robust law enforcement presence compared to their rural counterparts, factors that invariably influence vehicular speeds. Consequently, these variations in speed are associated with the adoption of riskier behaviors by motorists, with a pronounced impact on Young Adult Motorcyclists (YAMs) who demonstrate a heightened propensity for engaging in perilous activities, such as operating a motorcycle while under the influence or executing excessively close maneuvers. This study seeks to examine the factors that influence injury severity among YAM on local roads by contrasting urban and rural roadways, taking into account the different roadway contexts. Data from motorcycle crashes on local roads in Thailand between 2018 and 2020 were analyzed using the Random Parameter Logit Models with Unobserved Heterogeneity in Means and Variances (RPLMV) approach to investigate the relationship between contributing factors and YAM injury severity. The findings revealed several critical insights into the factors influencing the severity of motorcycle accidents, particularly among YAM on local urban roads. It is evident that a confluence of factors, including the absence of a valid driver’s license, exceeding designated speed limits, and the involvement of male drivers, significantly elevates the probability of fatal outcomes in these accidents. Our analysis has also unveiled intriguing patterns in nighttime accidents involving motorcycles, where those with functioning vehicle lights and those occurring under the cover of darkness without proper illumination share notable similarities in terms of severity. Furthermore, our research has emphasized that accidents transpiring outside city confines during nighttime hours with adequate lighting exhibit a negligible impact on higher crash severity. The key findings advocate for the development and implementation of targeted policy recommendations and countermeasures to alleviate the severity of accidents involving YAM. This includes a proposition to bolster law enforcement efforts, particularly in regions beyond city borders, and to enforce strict adherence to regulations concerning driver’s license verifications. Our study offers a crucial foundation for future research and policy development aimed at improving road safety and reducing the severity of motorcycle accidents, with the ultimate goal of safeguarding the lives of YAM. Full article

Significant changes in road and traffic conditions in transition areas are key to traffic organization and guaranteeing safety in freeway work zones. Currently, most of the related studies on transition area use theoretical calculations, traffic and driving simulations, and the impact of different transition area conditions on drivers’ psychophysiological indicators and driving behavior are unclear. In this paper, the gap acceptance theory was used to establish a calculation method of the transition area length, and the transition area length was calculated under different closed lane widths, speed limits, and traffic volumes. Based on the results of our theoretical calculations, naturalistic driving experiments were conducted with 48 participants in 12 scenarios involving 3 lane closure forms and 4 transition area lengths, and the relationship of transition area with driving workload and vehicle speed was determined. A transition area that was too short or too long increased traffic safety risks. The overall experimental results were consistent with the theoretical calculation length, and the theoretical calculation model was reliable. Compared to unaffected straight-through vehicles, merging vehicles and vehicles affected by merging have lower speeds, higher driving workloads, and increased traffic safety risks. An increase in the number of lanes in the transition area will result in increased driving workloads and vehicle speeds. Based on the changes in vehicle deceleration points and driving workloads, the affected area of the transition area was measured. When the speed limit was 60 km/h, the upstream affected areas of the transition areas with four, three, and two lanes were 1000 m, 850 m, and 700 m, and the downstream affected areas were 450 m, 400 m, and 350 m. These research results can provide a reference for improving traffic organization and guaranteeing safety in freeway work zones. Full article

Roundabout crashes are common worldwide but have received little attention. In particular, the investigation remains relatively understudied in distinct collision modes of roundabout crashes, including angle and non-angle crashes. This study investigates differences in factors affecting injury severity between angle and non-angle crashes and how these differences change over time. Random parameter logit models with heterogeneity in mean and variance were estimated using 2012–2019 Florida crash data. Variables considered for the modeling include temporal characteristics, environmental characteristics, road characteristics, spatial characteristics, vehicle characteristics, crash characteristics, and human characteristics. In addition, likelihood ratio tests were used to investigate the temporal instabilities of the models and differences in collision modes. The results showed that only a few variables demonstrated temporally stable effects for angle collisions (weekend and dark-lighted) and non-angle collisions (paved shoulders and cyclists), respectively. Unique influencing factors of injury severity were identified for different collision modes, such as dark-lighted, straight alignment, merge, lane departure, the disabled driver for angle collisions, and clear, fog, commercial vehicles, and aggressive driving for non-angle collisions. The results of the out-of-sample prediction simulations further demonstrate the difference in injury severity over time for angle and non-angle collisions. Overall results suggest that countermeasures can be implemented to reduce the injury severity of angle crashes/non-angle crashes based on the significant variables identified. This study may be used to improve roundabout safety by considering different collision modes. Full article

Compared to the construction phase, the assessment of tunnel safety risks during the operational stage has not been thoroughly conducted, and the related research work is relatively lagging. To deepen the research and application of theoretical methods for assessing operational tunnel safety risks, and to improve the level of hazard investigation and prevention in long and large highway tunnels, this paper establishes a tunnel section assessment index system from four perspectives: tunnel condition, traffic characteristics, operational environment, and operational management. A combined qualitative and quantitative approach is employed, and a practical classification standard for the indicators is proposed. Based on the traditional Analytic Hierarchy Process (AHP) and fuzzy analysis theory, a new form of questionnaire survey, known as the expert system analysis method, is introduced to collect expert opinions. A novel approach for determining fuzzy numbers based on expert opinions is proposed. Consequently, a combined model for assessing operational tunnel safety risks in long and large highways is established by integrating the Fuzzy Analytic Hierarchy Process (FAHP) and the Set Pair Analysis (SPA). This model effectively combines subjective weighting with objective evaluation, thereby enhancing the accuracy of operational tunnel safety risk assessment for long and large highways. The safety risks of three long and large tunnels within the G1523 Yongguan Expressway were assessed using the evaluation method proposed in this paper. A comparative analysis was conducted between the results obtained using the proposed Fuzzy Analytic Hierarchy Process (FAHP) and Set Pair Analysis (SPA) method and the traditional Analytic Hierarchy Process (AHP) evaluation. The results indicate that the operational tunnel safety risk assessment method based on FAHP and SPA exhibits greater rationality and accuracy compared to the traditional AHP approach. Full article

Different toll collection types of vehicles and different distribution of tollbooths lead to the toll plaza diverging area becoming a typical vehicle weaving area with frequent crossing behaviors and conflicts on highways. This study aims to identify contributing factors to conflict risks of four RP by developing random parameters ordered logit models with heterogeneity in means and variances. The model can flexibly capture the unobserved heterogeneity of the contributing factors in different vehicle-following patterns. Real-world vehicle trajectory data obtained from the toll plaza diverging area in Nanjing, China, are used for model estimation. The results show that vehicle-following patterns with the same toll collection types have a higher percentage of severe conflict risks. The average acceleration of the following vehicles, lane-marking indicator, the initial lanes and lane changes of vehicles are significantly associated with the collision risk levels. The standard deviation of surrogate safety measures of all vehicles in sub-segments are found to differ significantly between vehicle-following patterns. Furthermore, a series of likelihood ratio tests are adopted to test the spatial dependence in sub-segments of the diverging area. The findings of this study could provide valuable information for safety improvement in toll plazas. Full article

This study explores risk factors influencing the at-fault party in traffic accidents and analyzes their impact on traffic accident severity. Based on the traffic accident data of Shenyang City, Liaoning Province, China, from 2018 to 2020, 19 attribute variables including road attributes, time attributes, environmental attributes, and characteristics of the at-fault parties with either full responsibility, primary responsibility, or equal responsibility of the traffic accidents were extracted and analyzed in conjunction with the built environment attributes, such as road network density and POI (points of interest) density at the sites of traffic accidents. Using the RF-SHAP method to determine the relative importance of risk factors influencing the severity of traffic accidents with either motor vehicles or vulnerable groups at-fault, the top ten risk factors influencing the severity of traffic accidents with vulnerable road users as the at-fault parties are: functional zone, density of shopping POI, density of services POI, cause of accident, travel mode, collision type, season, road type, age of driver, and physical isolation. Travel mode, season, and road speed limit are more important risk factors for traffic accidents, with motor vehicle drivers as the at-fault parties. The density of service POI and cause of the accident are less critical for traffic accidents with motor vehicle drivers than traffic accidents with vulnerable road users who are at-fault. Subsequently, the Apriori algorithm based on association rules is used to analyze the important causal factors of traffic accidents, so as to explore the influence mechanism of multiple causal factors and their implied strong association rules. Our results show that most combined factors are associated with the matched Service and Shopping POI features. This study provides valuable information on the perceived risk of fatal accidents and highlights the built environment’s significant influence on fatal traffic accidents. Management strategies targeting the most typical combinations of accident risk factors are proposed for preventing fatalities and injuries in serious traffic accidents. Full article

Traffic accidents are widely recognized as being a significant societal disaster that causes severe human, economic, and social damage. While various factors, such as environmental, road, and human factors are commonly associated with traffic accidents, this study proposes a novel approach to consider the directionality of traffic accidents. It examines its impact on traffic safety in Chungcheongnam-do province, South Korea. Using police reports, we estimated the directionality of each accident by calculating the angle between its occurrence site and destination. Our experimental results revealed a statistically significant association between the directionality of traffic accidents and their incidence rates in Chungcheongnam-do province, with a higher frequency of accidents occurring in a Southbound direction. These findings suggest that considering the directionality of traffic accidents could enhance the effectiveness of traffic safety policies and interventions. Further research is needed to explore this approach’s underlying mechanisms and potential applications. Nevertheless, this study provides important insights for policy makers and practitioners to develop targeted and practical measures for improving traffic safety. Full article

The emergence of autonomous vehicles and the advancement of technology over the past several decades has increased the demand for intelligent intersection management systems. Since there has been increased interest in researching how autonomous vehicles manage traffic at junctions, a thorough literature analysis is urgently needed. This study discovered peer-reviewed publications published between 2012 and 2022 in the most prestigious libraries to address this problem. After that, 100 primary studies were identified, and the chosen literature was subjected to systematic analysis. According to the findings, there are four primary categories of approaches, i.e., rule-based, optimization, hybrid, and machine learning procedures, which are used to achieve diverse driving objectives, including efficacy, safety, ecological, and passenger ease. The analyses illustrate the many attributes, limits, and views of the current solutions. This analysis enables the provision of potential future difficulties and directions in this study area. Full article