Angle Crash Research Articles

An exploratory investigation into the influence of risk factors on driver injury severity in angle crashes: A random parameter bivariate ordered probit model approach

Objective Angle crashes have been acknowledged as a concerning issue in the traffic safety field, though there is limited understanding of the contributions of risk factors to injury severity. This article aims to examine the impact of risk factors and unobserved heterogeneity on the severity of driver injuries in angle collisions by utilizing angle crash data in the United States from 2016 to 2021. Methods The relationship between risk factors and driver injury severities in angle crashes was investigated using a random parameter bivariate ordered probit model (RPBOP) with 4 categories of injury severity classified as outcome variables, including no injury, possible injury, minor injury, and serious jury. Risk factors were considered as explanatory variables, classified as driver characteristics, vehicle characteristics, road characteristics, environmental characteristics, time characteristics, and crash characteristics. Bayesian inference was used to assess the unobserved heterogeneity in risk factors, and marginal effects were computed to analyze the effect of each factor on injury outcomes. Results The findings demonstrate that risk factors have varying effects on driver involvement in angle crashes. Certain factors exhibited unobserved heterogeneity, including young drivers (ages 25–44), older drivers (over age 59), road grade, and collision point orientation. On the other hand, other factors, such as female gender, motorcycles, intersections, speed limit (>50 mph), poor lighting conditions, adverse weather, urban areas, and workdays, were shown to significantly increase the likelihood of driver injury in angle collisions, as well as increase susceptibility to fatal injury. Conclusions This article offers new insights into reducing driver injuries in angle crashes and has the potential to inform policy development aimed at preventing such incidents. Further research could utilize multisource data fusion and investigate the spatiotemporal stability of risk factors to enhance the generalizability of angle collision prevention strategies.

Investigating the impacts of crash prediction models on quantifying safety effectiveness of Adaptive Signal Control Systems

Introduction: Adaptive Signal Control System (ASCS) can improve both operational and safety benefits at signalized corridors. Methods: This paper develops a series of models accounting for model forms and possible predictors and implements these models in Empirical Bayes (EB) and Fully Bayesian (FB) frameworks for ASCS safety evaluation studies. Different models are validated in terms of the ability to reduce the potential bias and variance of prediction and improve the safety effectiveness estimation accuracy using real-world crash data from non-ASCS sites. This paper then develops the safety effectiveness of ASCS at six different corridors with a total of 65 signalized intersections with the same type of ASCS, in South Carolina. Results: Validation results show that the FB model that accounts for traffic volume, roadway geometric features, year factor, and spatial effects shows the best performance among all models. The study findings reveal that ASCS reduces crash frequencies in the total crash, fatal and injury crash, and angle crash for most of the intersections. The safety effectiveness of ASCS varies with different intersection features (i.e., AADT at major streets, number of legs at an intersection, the number of through lanes on major streets, the number of access points on minor streets, and the speed limit at major streets). Conclusions: ASCS is associated with crash reductions, and its safety effects vary with different intersection features. Practical Applications: The findings of this research encourage more ASCS deployments and provide insights into selecting ASCS deployment sites for reducing crashes considering the variation of the safety effectiveness of ASCS.

Investigating hierarchical effects of adaptive signal control system on crash severity using random-parameter ordered regression models incorporating observed heterogeneity

By handling conflicting traffic movements and establishing dynamic coordination between intersections in real-time, the Adaptive Signal Control System (ASCS) can potentially improve the operation and safety of signalized intersections on a corridor. This study identifies the hierarchical effects of ASCS on the crash severity by exploring the heterogeneous effect of ASCS on the crash severity. Four different random-parameter ordered regression models (two ordered probit models, and two ordered logit models) are developed and compared. The analysis reveals that the random-parameter ordered probit and logit models (ROP and ROL) with observed heterogeneity perform better than the random-parameter ordered probit and logit models (RP and RL) without observed heterogeneity in terms of the Akaike information criteria and the goodness of fit of the model. The ROP model performs better than the ROL model in terms of classification model performance measures. The ROP model enables parameters (i.e., the coefficients of the explanatory variables) to vary as a function of explanatory variables as well as across observations, thus accounting for both observed (captured by available explanatory variables) and unobserved (not captured by available explanatory variables) heterogeneity. The analysis reveals that the presence of ASCS is associated with lower crash severity. In this study, observed heterogeneity of ASCS effects on the crash severity is captured by variables related to the intersection and corridor features. Other contributing factors besides ASCS, such as annual average daily traffic, speed limit, lighting, peak period, crash type (rear-end, angle), and pedestrian involvements, are also associated with the probability of crash severity. Unobserved heterogeneity of the effect of angle crash type on the crash severity is found to exist across the observations. The findings of this research have practical implications for establishing ASCS implementation guidelines in lowering the probability of higher crash severity.

Predicting casualty-accident count by highway design standards compliance

Abstract Compliance to standard has been the main doctrine in highway design, but its relationship with accident count has not been widely scrutinized. One of the key programs in road safety in Indonesia is road-worthiness test which assesses the compliance of a road to national design specifications and criteria. In light of current improvements in the crash data system in Indonesia, this study is carried out to develop a model to predict the accident count per type of crashes and to identify significant road features based on their compliance to a national standard. 272,200 km of arterial road in East Java North Corridor (EJNC) is selected as case study and 2012–2014 crash data is analyzed. Zero-Inflated Negative Binomial (ZINB) Model is preferred to develop crash prediction model with significant variables. This study has several findings. First, the constant values of the prediction model are relatively close to the average number of accident which implies that the mere compliance to current standard cannot warrant the safeness of Indoensian highways. Second, the number of median opening per unit length and disturbance level to pedestrian and road reserve area are the features that having positive relationships with total accident count. Meanwhile, the ROW disturbance, conformance of intersection and of road marking also show significant value but negative relationship with total accident count. Third, significant variables for each type of crash may have different sign. For example, in right angle crash, median width has positive relation with the number of accident, while in run off and rear end crash, median width compliance is shown to have negative relation.

Statistical Analysis and Development of Crash Prediction Model for Roundabouts on High-Speed Rural Roadways

Roundabouts have proved to be effective in urban and suburban environments in the United States, but little has been reported on the effectiveness of roundabouts in rural environments on high-speed roadways. There is no question that roundabouts reduce the speeds of all vehicles at intersections and reduce the frequency of fatal and injury-related crashes. This research is the first comprehensive look at roundabouts in a rural environment with high-speed approaches. Nineteen intersections had ample comprehensive crash data to be evaluated and analyzed for safety performance. The findings validated the hypothesis that roundabouts in a rural environment as well as roundabouts in urban and suburban environments outperformed other intersection safety improvements. A before-and-after crash analysis was conducted for the 19 intersections by using a negative binomial regression model. Results showed statistically significant reductions for the total number of crashes (63%) and injury crashes (88%) when roundabouts were implemented. A before-and-after empirical Bayes estimation was also conducted, and the results were consistent, indicating a 62% to 67% reduction in total crashes and an 85% to 87% reduction in injury crashes at these rural intersections. Furthermore, results showed that injury-producing crash types, such as the angle crash, were reduced by 91%, and were statistically significant. Finally, this research produced crash prediction models at planning level for total and injury crashes at rural roundabouts on high-speed roadways. This supplements the models produced in NCHRP Report 572: Roundabouts in the United States and reported in the AASHTO Highway Safety Manual.

Quality of traffic flow on urban arterial streets and its relationship with safety

The two-fluid model for vehicular traffic flow explains the traffic on arterials as a mix of stopped and running vehicles. It describes the relationship between the vehicles’ running speed and the fraction of running vehicles. The two parameters of the model essentially represent ‘free flow’ travel time and level of interaction among vehicles, and may be used to evaluate urban roadway networks and urban corridors with partially limited access. These parameters are influenced by not only the roadway characteristics but also by behavioral aspects of driver population, e.g., aggressiveness. Two-fluid models are estimated for eight arterial corridors in Orlando, FL for this study. The parameters of the two-fluid model were used to evaluate corridor level operations and the correlations of these parameters’ with rates of crashes having different types/severity. Significant correlations were found between two-fluid parameters and rear-end and angle crash rates. Rate of severe crashes was also found to be significantly correlated with the model parameter signifying inter-vehicle interactions. While there is need for further analysis, the findings suggest that the two-fluid model parameters may have potential as surrogate measures for traffic safety on urban arterial streets.

Understanding Causality of Intersection Crashes

Intersection crashes in the United States account for more than one-fifth of all highway fatalities. Crash, geometric, and roadway information can help identify crash causes. How such elements can serve this function, however, may not be clear to database users because of the random variation inherent in crashes. For this reason, classification trees and crash estimation models (CEMs) were developed from a 6-year data set, which contained more than 70,000 crashes that occurred at more than 6,000 intersections in Northern Virginia. The trees showed that specific causal factors, such as surface condition, indicated whether a given crash was rear-end or angle. Because such trees suggested that intersection crashes were not purely random, CEMs for 17 intersection classes were developed on the basis of traffic control, number of approaches and lanes, and rural versus urban area to predict four crash frequencies: rear-end, angle, injury, and total. The 68 CEMs showed deviance-based, pseudo–R-squared values between .07 and .74 and varied by intersection class. Of the nine angle crash models in which risk increased by making the approaches undivided, the increase varied between 43% and 154%. Two lessons emerged. First, the small proportion of variables that successfully classified most rear-end and angle crashes should be given increased attention to ensure that these data elements are recorded accurately at the crash scene. The methodology used in this study showed that much of the tree factor space (81%) was composed of only 10 variables. Second, facility-specific intersection CEMs should be developed because a geometric variable may be a surrogate for other phenomena.

Analyzing angle crashes at unsignalized intersections using machine learning techniques

A recently developed machine learning technique, multivariate adaptive regression splines (MARS), is introduced in this study to predict vehicles’ angle crashes. MARS has a promising prediction power, and does not suffer from interpretation complexity. Negative Binomial (NB) and MARS models were fitted and compared using extensive data collected on unsignalized intersections in Florida. Two models were estimated for angle crash frequency at 3- and 4-legged unsignalized intersections. Treating crash frequency as a continuous response variable for fitting a MARS model was also examined by considering the natural logarithm of the crash frequency. Finally, combining MARS with another machine learning technique (random forest) was explored and discussed. The fitted NB angle crash models showed several significant factors that contribute to angle crash occurrence at unsignalized intersections such as, traffic volume on the major road, the upstream distance to the nearest signalized intersection, the distance between successive unsignalized intersections, median type on the major approach, percentage of trucks on the major approach, size of the intersection and the geographic location within the state. Based on the mean square prediction error (MSPE) assessment criterion, MARS outperformed the corresponding NB models. Also, using MARS for predicting continuous response variables yielded more favorable results than predicting discrete response variables. The generated MARS models showed the most promising results after screening the covariates using random forest. Based on the results of this study, MARS is recommended as an efficient technique for predicting crashes at unsignalized intersections (angle crashes in this study).

Investigation of Truck-Involved Angle Crashes Based on GES and FARS Crash Databases

Truck-car angle collisions have a higher crash frequency than other truck-involved collision types and tend to increase injury severity. This paper investigates both general and fatal truck-involved angle collisions using two national crash databases (2000-2004), General estimates system (GES) and Fatality Analysis Reporting system (FARS). In this study, two-vehicle angle collisions were classified into three groups based on fault roles of truck or car drivers in the acci- dents, including Truck-Car, Car-Truck, and Car-Car crashes. The occurrence conditions of the three angle crash types were compared to each other to identify the potential risk factors such as driver characteristics, road environments, and highway designs related to the truck-involved crashes. The multinomial logistic regression is used for the statistical analy- sis. Based on the result analysis of this study, it is suggested that truck-involved angle collisions should be considered as an important scenario design for retraining or education programs for the purposes of reducing older drivers' fatality rate; improving either the conspicuity of truck trailers or lighting design of the highway would reduce the frequency and sever- ity of truck-involved angle crashes; to improve incompatibilities between truck, car, and highway design, further studies should conduct in-depth analyses of geometric factors related to driver performances and behaviors in the car-truck con- flicts at intersections.

Modelling motorcyclist injury severity by various crash types at T-junctions in the UK

Motorcyclists tend to be more vulnerable to injuries than those using other motorised vehicles and this may act synergistically with the complexity of conflicting movements between vehicles and motorcycles to increase injury severity in a junction-type accident. A junction-type crash can be more severe to motorcyclists than a non-junction case due to the fact that some of the injurious crashes such as angle crash commonly occur. Previous studies have applied crash prediction models to investigate influential factors on the occurrences of different crashes among motorised vehicles but statistical models of motorcyclist injury severity resulting from different collision types have rarely been developed. This paper develops injury severity models for different collision-types conditioned on crash occurrence at T-junctions in the UK. The ordered logit models are estimated using human, weather, road and vehicle factors as predictors and the data for the model estimation were extracted from the STATS19 accident injury database (1991–2004). The modelling results show that motorcyclist injury severity in specific crash types is associated with predictor variables in different ways. This study offers a guideline for future research, as well as insight into potential prevention strategies that might help prevent the most hazardous situation(s) from occurring in different collision types.

The significance of endogeneity problems in crash models: An examination of left-turn lanes in intersection crash models

Crash prediction models are used for a variety of purposes including forecasting the expected future performance of various transportation system segments with similar traits. The influence of intersection features on safety have been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes compared to other segments in the transportation system. The effects of left-turn lanes at intersections in particular have seen mixed results in the literature. Some researchers have found that left-turn lanes are beneficial to safety while others have reported detrimental effects on safety. This inconsistency is not surprising given that the installation of left-turn lanes is often endogenous, that is, influenced by crash counts and/or traffic volumes. Endogeneity creates problems in econometric and statistical models and is likely to account for the inconsistencies reported in the literature. This paper reports on a limited-information maximum likelihood (LIML) estimation approach to compensate for endogeneity between left-turn lane presence and angle crashes. The effects of endogeneity are mitigated using the approach, revealing the unbiased effect of left-turn lanes on crash frequency for a dataset of Georgia intersections. The research shows that without accounting for endogeneity, left-turn lanes 'appear' to contribute to crashes; however, when endogeneity is accounted for in the model, left-turn lanes reduce angle crash frequencies as expected by engineering judgment. Other endogenous variables may lurk in crash models as well, suggesting that the method may be used to correct simultaneity problems with other variables and in other transportation modeling contexts.