Analyses of rear-end crashes based on classification tree models

Optimization of Forward Collision Warning Algorithm Considering Truck Driver Response Behavior Characteristics

Forward collision warning (FCW) systems have been widely used in trucks to alert drivers of potential road situations so they can reduce the risk of crashes. Research on FCW use shows, however, that there are differences in drivers' responses to FCW alerts under different scenarios. Existing FCW algorithms do not take differences in driver response behavior into account, with the consequence that the algorithms' minimum safe distance assessments that trigger the warnings are not always appropriate for every driver or situation. To reduce false alarms, this study analyzed truck driver behavior in response to FCW warnings, and k-means clustering was adopted to classify driver response behavior into three categories: Response Before Warning (RBW), Response After Warning (RAW), and No Response (NR). Results showed that RBW clusters tend to occur at long following distances (>19 m), and drivers applied braking before the warning. In RAW clusters, deceleration after warning is significantly more forceful than before warning. NR clusters occur at short distances, and deceleration fluctuates only slightly. To optimize the FCW algorithm, the warning distance was divided into reaction distance and braking distance. The linear support vector machine was used to fit the driver reaction distance. The long short-term memory method was used to predict braking distance based on each of the three response scenarios: R2 was 0.896 for RAW scenarios, 0.927 for RBW scenarios, and 0.980 for NR scenarios. Verification results show that the optimized truck FCW algorithm improved safety by 1 % to 5.1 %; accuracy reached 97.92 %, and the false alarm rate was 1.73 %.

Looking back in the rearview: Insights into Queensland's rear-end crashes

OBJECTIVE

Rear-end crashes cost the most out of any crash type to the Compulsory Third Party insurance scheme of Queensland. Rear-end crashes are also one of the most common types of crashes, peaking as the most common type of crash in Queensland in 2019. This study investigated the environmental, driver, and vehicle characteristics associated with rear-end collisions in Queensland, Australia.

METHODS

A totle of 367,230 crashes in the Queensland crash database between 2001 and 2021 were used to identify crash trends in Queensland, particularly related to rear-end crashes. For crashes between 2015 and 2021, additional information was gathered and allowed further analysis of factors contributing to rear-end crashes using the quasi-induced exposure method. Two binary classification tree models were used. Model 1 investigated the environmental characteristics that predicted a crash being a rear-end or not. Model 2 investigated the controller and vehicle characteristics that predict a rear-end crash-involved vehicle to be either the striking (at-fault) or struck (not-at-fault) vehicle.

RESULTS

Model 1 indicated that rear-end crashes were overrepresented in urban areas and major cities and where speed limits exceeded 50 km/h. Model 2 indicated that occupants of struck vehicles (not at-fault) were more likely to be severely injured than the striking (at-fault) vehicles in rear-end crashes. License type and vehicle type also influenced the frequency and severity of rear-end crashes.

CONCLUSION

Rear-end crashes can be unjust in that the not-at-fault party is typically more severely injured. Results from the current study suggest that keeping speed limits no higher than 50 km/h could help reduce the severity of rear-end crashes. Increased enforcement of safe following distances could also assist in preventing road trauma as a result of rear-end crashes.

Assessment of Two-Vehicle and Multi-Vehicle Freeway Rear-End Crashes in China: Accommodating Spatiotemporal Shifts

Accounting for the growing numbers of injuries, fatalities, and property damage, rear-end crashes are an urgent and serious topic nowadays. The vehicle number involved in one crash significantly affected the injury severity outcomes of rear-end crashes. To examine the transferability and heterogeneity across crash types (two-vehicle versus multi-vehicle) and spatiotemporal stability of determinants affecting the injury severity of freeway rear-end crashes, this study modeled the data of crashes on the Beijing-Shanghai Freeway and Changchun-Shenzhen Freeway across 2014-2019. Accommodating the heterogeneity in the means and variances, the random parameters logit model was proposed to estimate three potential crash injury severity outcomes (no injury, minor injury, and severe injury) and identify the determinants in terms of the driver, vehicle, roadway, environment, temporal, spatial, traffic, and crash characteristics. The likelihood ratio tests revealed that the effects of factors differed significantly depending on crash type, time, and freeway. Significant variations were observed in the marginal effects of determinants between two-vehicle and multi-vehicle freeway rear-end crashes. Then, spatiotemporal instability was reported in several determinants, including trucks early morning. In addition, the heterogeneity in means and variances of the random parameters revealing the interactions of random parameters and other insignificant variables suggested the higher risk of determinants including speeding indicators, early morning, evening time, and rainy weather conditions. The current finding accounting for spatiotemporal instability could help freeway designers, decision-makers, management strategies to understand the contributing mechanisms of the factors to develop effective management strategies and measurements.

Variance-based global sensitivity analysis for rear-end crash investigation using deep learning

Traffic accidents are rare events with inconsistent spatial and temporal dimensions; thus, accident injury severity (INJ-S) analysis faces a significant challenge in its classification and data stability. While classical statistical models have limitations in accurately modeling INJ-S, advanced machine learning methods have no apparent equations to prioritize/analyze different contributing factors to predict INJ-S levels. Also, the intercorrelations among the input factors could make the results of a typical sensitivity analysis misleading. Rear-end accidents constitute the most frequent type of traffic accidents; and therefore, their associated INJ-S need more insight investigations. To resolve all these issues, this study presents a sophisticated approach based on a deep learning paradigm combined with a Variance-Based Globa1 Sensitivity Analysis (VB/GSA). The methodology proposes a deep residual neural networks structure that utilizes residual shortcuts (i.e., connections), unlike other neural network architectures. The connections allow the DRNNs to bypass a few layers in the deep network architecture, circumventing the regular training with high accuracy problems. The Monte Carlo simulation with the aid of the trained DRNNs model was conducted to investigate the impact of each explanatory factor on the INJ-S levels based on the VB/GSA. The developed methodology was used to analyze all rear-end accidents in North Carolina from 2010 to 2017. The performance of the developed methodology was evaluated utilizing some selected representative indicators and then compared with the well-known ordered logistic regression (OLR) model. The developed methodology was found to achieve an overall accuracy of 83% and attained a superior performance, as compared with the OLR model. Furthermore, the VB/GSA analysis could identify the most significant attributes to rear-end crashes INJ-S level.

Effect of daily car-following behaviors on urban roadway rear-end crashes and near-crashes: A naturalistic driving study

The rear-end crash is one of the most common types of crashes, and key risk factors have been broadly identified in the car-following behaviors preceding a crash. However, the relationships between rear-end crash risk and daily car-following behaviors, or habits, have not been well examined. This study aims to identify the daily car-following behaviors on urban surface roads and urban expressways that have the most influence on rear-end crashes and near-crashes (CNC). Two months of naturalistic driving study data were used to investigate the daily car-following behavior of 54 drivers. A paired t-test and a Wilcoxon matched-pairs signed rank test were conducted to find the differences in behaviors on the two road types, and basic Poisson regression and Poisson hurdle regression models were used to explore significant risk factors. Results revealed that (1) drivers' longitudinal vehicle control, time control, and emergency behaviors are significantly different on urban surface roads and urban expressways; (2) for surface roads, three key influencing factors were ranked, in descending order, as the standard deviation of relative speed, percentage of time gap less than 1 s, and maximum acceleration; (3) for expressways, four key factors were ranked: minimum time gap, maximum deceleration, percentage of TTC less than 5 s, and the percentage of large positive jerk. The knowledge achieved on risky daily driving behaviors can be applied to training drivers to improve safe practices, assist insurance companies in creating usage-based insurance strategies, and support driver assistant systems design.

Factors Affecting Death and Severe Injury in Child Motor Vehicle Passengers

Saving children from motor vehicle collisions is a high priority because the injury rate among motor vehicle passengers has been increasing in Japan. This study aimed to examine the factors that influence death and serious injury in child motor vehicle passengers to establish effective preventive measures. To identify these factors, we performed a retrospective study using a nationwide medical database. The data of child motor vehicle passengers younger than 15 years (n = 1084) were obtained from the Japanese Trauma Data Bank, registered from 2004 to 2019. Physiological variables, outcomes, and injury severity were compared between fatal and non-fatal patients and between those with and without severe injuries. Multivariate logistic regression analysis was performed to determine factors affecting fatality and severe injury. The Glasgow Coma Scale score (odds ratio (OR): 1.964), body temperature (OR: 2.578), and the Abbreviated Injury Scale score of the head (OR: 0.287) were identified as independent predictors of a non-fatal outcome. Systolic blood pressure (OR: 1.012), the Glasgow Coma Scale score (OR: 0.705), and Focused Assessment with Sonography for Trauma positivity (OR: 3.236) were identified as independent predictors of having severe injury. Decreasing the severity of head injury is the highest priority for child motor vehicle passengers to prevent fatality and severe injury.

Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm

Evaluating road safety is an enduring research topic in Infrastructure and Transportation Engineering. The prediction of crash risk is very important for avoiding other crashes and safeguarding road users. According to this task, awareness of the number of vehicles involved in an accident contributes greatly to safety analysis, hence, it is necessary to predict it. In this study, the main aim is to develop a binary model for predicting the number of vehicles involved in an accident using Neural Networks and the Group Method of Data Handling (GMDH). For this purpose, 775 accident cases were accurately recorded and evaluated from the urban and rural areas of Cosenza in southern Italy and some notable parameters were considered as input data including Daylight, Weekday, Type of accident, Location, Speed limit and Average speed; and the number of vehicles involved in an accident was considered as output. In this study, 581 cases were selected randomly from the dataset to train and the rest were used to test the developed binary model. A confusion matrix and a Receiver Operating Characteristic curve were used to investigate the performance of the proposed model. According to the obtained results, the accuracy values of the prediction model were 83.5% and 85.7% for testing and training, respectively. Finally, it can be concluded that the developed binary model can be applied as a reliable tool for predicting the number of vehicles involved in an accident.

Temporal stability of the factors related to the severity of drivers' injuries in rear-end collisions

Most of the previous studies that investigated the factors increasing the severity of rear-end collisions were based on analyzing collision reports from multiple years and combining them into a single dataset for analysis. Analyzing pooled data from multiple years carries the risk of introducing aggregation bias in the analysis. Those aggregated models might be structurally unstable, and the significance of the risk factors identified using those aggregated models might change over time due to the ongoing changes in vehicle technologies, law-enforcement technologies, and drivers' attitudes. This study demonstrates the importance of testing the temporal stability of pooled data by utilizing logistic regression modeling to analyze all rear-end collisions that occurred in North Carolina for the period from January 1, 2004 to December 31, 2015. Separate models were developed for each year to model injury severity of striking and struck drivers. The year-wise models were compared together to identify the most temporally stable factors, and it was found that older and female drivers are usually more severely injured, but they do not increase injury severity of the drivers they collide with. It was also found that compared to other light-duty vehicles, passenger cars are usually associated with increased injury severity to their drivers and reduced injury severity to the drivers of the vehicles they collide with. The increased age of a vehicle was found to increase the injury severity of its driver as well as the driver of the vehicle it collides with. Dark conditions were found to increase drivers' injury severity, but adverse weather conditions have no similar effect. For comparison, aggregated models were also developed by pooling data from all analysis years (from 2004 to 2015) and were found to return significant factors that were found by the year-wise models to be temporally unstable. Chow tests were performed on the data, and it was found that pooling data for four years or more generally returned structurally unstable models.

Modeling Road Accident Severity with Comparisons of Logistic Regression, Decision Tree and Random Forest

To reduce the damage caused by road accidents, researchers have applied different techniques to explore correlated factors and develop efficient prediction models. The main purpose of this study is to use one statistical and two nonparametric data mining techniques, namely, logistic regression (LR), classification and regression tree (CART), and random forest (RF), to compare their prediction capability, identify the significant variables (identified by LR) and important variables (identified by CART or RF) that are strongly correlated with road accident severity, and distinguish the variables that have significant positive influence on prediction performance. In this study, three prediction performance evaluation measures, accuracy, sensitivity and specificity, are used to find the best integrated method which consists of the most effective prediction model and the input variables that have higher positive influence on accuracy, sensitivity and specificity.

Factors influencing vehicle passenger fatality have changed over 10 years: a nationwide hospital-based study

Traffic injury trends have changed with safety developments. To establish effective preventive measures against traffic fatalities, the factors influencing fatalities must be understood. The present study evaluated data from a national medical database to determine the changes in these factors over time, as this has not been previously investigated. This observational study retrospectively analysed data from the Japanese Trauma Data Bank. Vehicle passengers involved in collisions from 2004–2008 and 2016–2017 were included. Data were compared between the two study periods, and between fatal and non-fatal patients within each period. Multivariate logistic regression analyses were performed to determine the factors influencing fatalities. In 2016–2017, patients were older and had lower fatality rates. In 2004–2008, fatalities were more likely to involve older male front-seat passengers with low d-BP, BT, and GCS values, and high AIS of the neck and abdomen. However, in 2016–2017, fatalities were more likely to involve older males with low GCS, high AIS of the abdomen, and positive focused assessment with sonography for trauma results. Our study identified independent factors influencing vehicle passenger fatalities, which will likely continue to evolve with the aging of the population and changing manners of injury.

Assessing crash risk considering vehicle interactions with trucks using point detector data

Trucks have distinct driving characteristics in general traffic streams such as lower speeds and limitations in acceleration and deceleration. As a consequence, vehicles keep longer headways or frequently change lane when they follow a truck, which is expected to increase crash risk. This study introduces several traffic measures at the individual vehicle level to capture vehicle interactions between trucks and non-trucks and analyzed how the measures affect crash risk under different traffic conditions. The traffic measures were developed using headways obtained from Inductive Loop Detectors (ILDs). In addition, a truck detection algorithm using a Gaussian Mixture (GM) model was developed to identify trucks and to estimate truck exposure from ILD data. Using the identified vehicle types from the GM model, vehicle interaction metrics were categorized into three groups based on the combination of leading and following vehicle types. The effects of the proposed traffic measures on crash risk were modeled in two different cases of prior- and non-crash using a case-control approach utilizing a conditional logistic regression. Results showed that the vehicle interactions between the leading and following vehicle types were highly associated with crash risk, and further showed different impacts on crash risk by traffic conditions. Specifically, crashes were more likely to occur when a truck following a non-truck had shorter average headway but greater headway variance in heavy traffic while a non-truck following a truck had greater headway variance in light traffic. This study obtained meaningful conclusions that vehicle interactions involved with trucks were significantly related to the crash likelihood rather than the measures that estimate average traffic condition such as total volume or average headway of the traffic stream.

Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

The existing studies on drivers' injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers' injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.

Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network

Identification of the significant factors of traffic crashes has been a primary concern of the transportation safety research community for many years. A fatal-injury crash is a comprehensive result influenced by multiple variables involved at the moment of the crash scenario, the main idea of this paper is to explore the process of significant factors identification from a multi-objective optimization (MOP) standpoint. It proposes a data-driven model which combines the Non-dominated Sorting Genetic Algorithm (NSGA-II) with the Neural Network (NN) architecture to efficiently search for optimal solutions. This paper also defines the index of Factor Significance (F_s) for quantitative evaluation of the significance of each factor. Based on a set of three year data of crash records collected from three main interstate highways in the Washington State, the proposed method reveals that the top five significant factors for a better Fatal-injury crash identification are 1) Driver Conduct, 2) Vehicle Action, 3) Roadway Surface Condition, 4) Driver Restraint and 5) Driver Age. The most sensitive factors from a spatiotemporal perspective are the Hour of Day, Most Severe Sobriety, and Roadway Characteristics. The method and results in this paper provide new insights into the injury pattern of highway crashes and may be used to improve the understanding of, prevention of, and other enforcement efforts related to injury crashes in the future.

Personal factors influencing the visual reaction time of pedestrians to detect turn indicators in the presence of Daytime Running Lamps

Daytime running lamps (DRL) on vehicles have proven to be an effective measure to prevent accidents during the daytime, particularly when pedestrians and cyclists are involved. However, there are negative interactions of DRL with other functions in automotive lighting, such as delays in pedestrians' visual reaction time (VRT) when turn indicators are activated in the presence of DRL. These negative interactions need to be reduced. This work analyses the influence of variables inherent to pedestrians, such as height, gender and visual defects, on the VRT using a classification and regression tree as an exploratory analysis and a generalized linear model to validate the results. Some pedestrian characteristics, such as gender, alone or combined with the DRL colour, and visual defects, were found to have a statistically significant influence on VRT and, hence, on traffic safety. These results and conclusions concerning the interaction between pedestrians and vehicles are presented and discussed. Practitioner Summary: Visual interactions of vehicle daytime running lamps (DRL) with other functions in automotive lighting, such as turn indicators, have an important impact on a vehicle's conspicuity for pedestrians. Depending on several factors inherent to pedestrians, the visual reaction time (VRT) can be remarkably delayed, which has implications in traffic safety.

Prioritizing Highway Safety Manual's crash prediction variables using boosted regression trees

The Highway Safety Manual (HSM) recommends using the empirical Bayes (EB) method with locally derived calibration factors to predict an agency's safety performance. However, the data needs for deriving these local calibration factors are significant, requiring very detailed roadway characteristics information. Many of the data variables identified in the HSM are currently unavailable in the states' databases. Moreover, the process of collecting and maintaining all the HSM data variables is cost-prohibitive. Prioritization of the variables based on their impact on crash predictions would, therefore, help to identify influential variables for which data could be collected and maintained for continued updates. This study aims to determine the impact of each independent variable identified in the HSM on crash predictions. A relatively recent data mining approach called boosted regression trees (BRT) is used to investigate the association between the variables and crash predictions. The BRT method can effectively handle different types of predictor variables, identify very complex and non-linear association among variables, and compute variable importance. Five years of crash data from 2008 to 2012 on two urban and suburban facility types, two-lane undivided arterials and four-lane divided arterials, were analyzed for estimating the influence of variables on crash predictions. Variables were found to exhibit non-linear and sometimes complex relationship to predicted crash counts. In addition, only a few variables were found to explain most of the variation in the crash data.

Application of a Rule-Based Approach in Real-Time Crash Risk Prediction Model Development Using Loop Detector Data

OBJECTIVES

There is a growing trend in development and application of real-time crash risk prediction models within dynamic safety management systems. These real-time crash risk prediction models are constructed by associating crash data with the real-time traffic surveillance data (e.g., collected by loop detectors). The main objective of this article is to develop a real-time risk model that will potentially be utilized within traffic management systems. This model aims to predict the likelihood of crash occurrence on motorways.

METHODS

In this study, the potential prediction variables are confined to traffic-related characteristics. Given that the dependent variable (i.e., traffic safety condition) is dichotomous (i.e., "no-crash" or "crash"), a rule-based approach is considered for model development. The performance of rule-based classifiers is further compared with the more conventional techniques like binary logistic regression and decision trees. The crash and traffic data used in this study were collected between June 2009 and December 2011 on a part of the E313 motorway in Belgium between Geel-East and Antwerp-East exits, on the direction toward Antwerp.

RESULTS

The results of analysis show that several traffic flow characteristics such as traffic volume, average speed, and standard deviation of speed at the upstream loop detector station and the difference in average speed on upstream and downstream loop detector stations significantly contribute to the crash occurrence prediction. The final chosen classifier is able to predict 70% of crash occasions accurately, and it correctly predicts 90% of no-crash instances, indicating a 10% false alarm rate.

CONCLUSIONS

The findings of this study can be used to predict the likelihood of crash occurrence on motorways within dynamic safety management systems.

A comprehensive review on the quasi-induced exposure technique

OBJECTIVE

The goal is to comprehensively examine the state-of-the-art applications and methodological development of quasi-induced exposure and consequently pinpoint the future research directions in terms of implementation guidelines, limitations, and validity tests.

METHODS

The paper conducts a comprehensive review on approximately 45 published papers relevant to quasi-induced exposure regarding four key topics of interest: applications, responsibility assignment, validation of assumptions, and methodological development.

RESULTS

Specific findings include that: (1) there is no systematic data screening procedure in place and how the eliminated crash data will impact the responsibility assignment is generally unknown; (2) there is a lack of necessary efforts to assess the validity of assumptions prior to its application and the validation efforts are mostly restricted to the aggregated levels due to the limited availability of exposure truth; and (3) there is a deficiency of quantitative analyses to evaluate the magnitude and directions of bias as a result of injury risks and crash avoidance ability.

CONCLUSIONS

The paper points out the future research directions and insights in terms of the validity tests and implementation guidelines.

Older drivers and rapid deceleration events: Salisbury Eye Evaluation Driving Study

Drivers who rapidly change speed while driving may be more at risk for a crash. We sought to determine the relationship of demographic, vision, and cognitive variables with episodes of rapid decelerations during five days of normal driving in a cohort of older drivers. In the Salisbury Eye Evaluation Driving Study, 1425 older drivers aged 67-87 were recruited from the Maryland Motor Vehicle Administration's rolls for licensees in Salisbury, Maryland. Participants had several measures of vision tested: visual acuity, contrast sensitivity, visual fields, and the attentional visual field. Participants were also tested for various domains of cognitive function including executive function, attention, psychomotor speed, and visual search. A custom created driving monitoring system (DMS) was used to capture rapid deceleration events (RDEs), defined as at least 350 milli-g deceleration, during a five day period of monitoring. The rate of RDE per mile driven was modeled using a negative binomial regression model with an offset of the logarithm of the number of miles driven. We found that 30% of older drivers had one or more RDE during a five day period, and of those, about 1/3 had four or more. The rate of RDE per mile driven was highest for those drivers driving<59 miles during the 5-day period of monitoring. However, older drivers with RDE's were more likely to have better scores in cognitive tests of psychomotor speed and visual search, and have faster brake reaction time. Further, greater average speed and maximum speed per driving segment was protective against RDE events. In conclusion, contrary to our hypothesis, older drivers who perform rapid decelerations tend to be more "fit", with better measures of vision and cognition compared to those who do not have events of rapid deceleration.

Aggregate nonparametric safety analysis of traffic zones

Exploring the significant variables related to specific types of crashes is vitally important in the planning stage of a transportation network. This paper aims to identify and examine important variables associated with total crashes and severe crashes per traffic analysis zone (TAZ) in four counties of the state of Florida by applying nonparametric statistical techniques such as data mining and random forest. The intention of investigating these factors in such aggregate level analysis is to incorporate proactive safety measures in transportation planning. Total and severe crashes per TAZ were modeled to provide predictive decision trees. The variables which carried higher weight of importance for total crashes per TAZ were - total number of intersections per TAZ, airport trip productions, light truck productions, and total roadway segment length with 35 mph posted speed limit. The other significant variables identified for total crashes were total roadway length with 15 mph posted speed limit, total roadway length with 65 mph posted speed limit, and non-home based work productions. For severe crashes, total number of intersections per TAZ, light truck productions, total roadway length with 35 mph posted speed limit, and total roadway length with 65 mph posted speed limit were among the significant variables. These variables were further verified and supported by the random forest results.

Analysis of factors associated with traffic injury severity on rural roads in Iran

Background:

Iran is a country with one of the highest rates of traffic crash fatality and injury, and seventy percent of these fatalities happen on rural roads. The objective of this study is to identify the significant factors influencing injury severity among drivers involved in crashes on two kinds of major rural roads in Iran: two-lane, two-way roads and freeways.

Methods:

According to the dataset, 213569 drivers were involved in rural road crashes in Iran, over the 3 years from 2006 to 2008. The Classification And Regression Tree method (CART) was applied for 13 independent variables, and one target variable of injury severity with 3 classes of no-injury, injury and fatality. Some of the independent variables were cause of crash, collision type, weather conditions, road surface conditions, driver's age and gender and seat belt usage. The CART model was trained by 70% of these data, and tested with the rest.

Results:

It was indicated that seat belt use is the most important safety factor for two-lane, two-way rural roads, but on freeways, the importance of this variable is less. Cause of crash, also turned out to be the next most important variable. The results showed that for two-lane, two-way rural roads, "improper overtaking" and "speeding", and for rural freeways, "inattention to traffic ahead", "vehicle defect", and "movement of pedestrians, livestock and unauthorized vehicles on freeways" are the most serious causes of increasing injury severity.

Conclusions:

The analysis results revealed seat belt use, cause of crash and collision type as the most important variables influencing the injury severity of traffic crashes. To deal with these problems, intensifying police enforcement by means of mobile patrol vehicles, constructing overtaking lanes where necessary, and prohibiting the crossing of pedestrians and livestock and the driving of unauthorized vehicles on freeways are necessary. Moreover, creating a rumble strip on the two edges of roads, and paying attention to the design consistency of roads can be a helpful factor in order to prevent events such as "overturning" and improve the overall safety of freeways.

Head restraint backset during routine automobile driving: drivers usually exceed the recommended guidelines

OBJECTIVE

Although several previous studies have evaluated horizontal head restraint backset distances, few studies have evaluated them during driving. The purpose of this study was to measure this backset during routine automobile driving and to specifically evaluate the backset during individual driving tasks such as turning, stopping, starting, and lane changes.

METHODS

Fourteen subjects drove around a specified route through the city of Guelph, Ontario, Canada, that included residential, thruway, and highway driving; additional minor driving tasks, such as lane changes, were evaluated. The distance of head restraint to posterior aspect of the head was measured continuously throughout the drive using an ultrasonic measurement system. The timing of specific tasks was documented using a video camera.

RESULTS

The average head-to-restraint distance throughout the driving route was 78.1 mm (standard deviation [SD] 24.8 mm); this distance did not vary significantly between the global measures during various driving areas (residential, thruway, and highway). We observed that the head restraint backsets during right turns (93.6 mm; SD 34.8 mm) were significantly larger compared to the other driving tasks (p < .001). The 7 males and 7 females showed similar backset distances: 84.52 mm (SD 12.08) and 71.68 mm (SD 5.53), respectively (p = .0785).

CONCLUSIONS

We observed that most subjects maintain a relatively consistent head-to-restraint distance throughout their driving route; 2 subjects adopted very large head restraint backset distances throughout their drive and 2 others adopted very small head restraint backset distances-this appears to reflect driver posture. Twelve of 14 subjects had average backset distances that exceed the National Highway Traffic Safety Administration and the Federal Motor Vehicle Safety Standard guidelines, indicating that most drivers may be at risk for whiplash-like disorders if exposed to a rear impact while driving. Of the monitored driving tasks, turning, especially right turns, caused drivers to increase their head-to-restraint backset distance.

Identifying driver characteristics influencing overtaking crashes

OBJECTIVE

To identify the most important driver characteristics influencing crash-causing overtaking maneuvers on 2-lane, 2-way rural roads of Iran.

METHODS

Based on the crash data for rural roads of Iran over 3 years from 2006 to 2008, the classification and regression tree (CART) method combined with the quasi-induced exposure concept was applied for 4 independent variables and one target variable of "driver status" with 2 classes of at fault and not at fault. The independent variables were vehicle type, driver's age, driving license, and driving experience of the driver-the latter 2 driver characteristics are relatively new in traffic safety studies.

RESULTS

According to the data set, 16,809 drivers were involved in 2-lane, 2-way rural roads overtaking crashes. The analysis revealed that drivers who are younger than 28 years old, whose driving license is type 2--a common driving license that is for driving with passenger car and light vehicles--and whose driving experience is less than 2 years are most probably responsible for overtaking crashes.

CONCLUSION

It was indicated that vehicle type is the most important factor associated with drivers being responsible for the crashes. The results also revealed that younger drivers (18-28 years) are most likely to be at fault in overtaking crashes. Therefore, enforcement and education should be more concentrated on this age group. Due to the incompliant nature of this group, changing the type and amount of traffic fines is essential for more preventing objectives. The research also found 2 relatively new factors of driving license and driving experience to have considerable effects on drivers being at fault, such that type 2 licensed drivers are more responsible compared to type 1 (a driving license for driving with all motor vehicles, which has some age and experience requirements) licensed drivers or drivers with a special license (a driving license with special vehicle types). Moreover, drivers with less than 2 years' driving experience are more responsible for these kind of crashes; thus prohibiting new drivers from driving on rural roads for new drivers seems substantial.

Using hierarchical tree-based regression model to predict train-vehicle crashes at passive highway-rail grade crossings

This paper applies a nonparametric statistical method, hierarchical tree-based regression (HTBR), to explore train-vehicle crash prediction and analysis at passive highway-rail grade crossings. Using the Federal Railroad Administration (FRA) database, the research focuses on 27 years of train-vehicle accident history in the United States from 1980 through 2006. A cross-sectional statistical analysis based on HTBR is conducted for public highway-rail grade crossings that were upgraded from crossbuck-only to stop signs without involvement of other traffic-control devices or automatic countermeasures. In this study, HTBR models are developed to predict train-vehicle crash frequencies for passive grade crossings controlled by crossbucks only and crossbucks combined with stop signs respectively, and assess how the crash frequencies change after the stop-sign treatment is applied at the crossbuck-only-controlled crossings. The study results indicate that stop-sign treatment is an effective engineering countermeasure to improve safety at the passive grade crossings. Decision makers and traffic engineers can use the HTBR models to examine train-vehicle crash frequency at passive crossings and assess the potential effectiveness of stop-sign treatment based on specific attributes of the given crossings.