Modeling secondary accidents identified by traffic shock waves

The high potential for occurrence and the negative consequences of secondary accidents make them an issue of great concern affecting freeway safety. Using accident records from a three-year period together with California interstate freeway loop data, a dynamic method for more accurate classification based on the traffic shock wave detecting method was used to identify secondary accidents. Spatio-temporal gaps between the primary and secondary accident were proven be fit via a mixture of Weibull and normal distribution. A logistic regression model was developed to investigate major factors contributing to secondary accident occurrence. Traffic shock wave speed and volume at the occurrence of a primary accident were explicitly considered in the model, as a secondary accident is defined as an accident that occurs within the spatio-temporal impact scope of the primary accident. Results show that the shock waves originating in the wake of a primary accident have a more significant impact on the likelihood of a secondary accident occurrence than the effects of traffic volume. Primary accidents with long durations can significantly increase the possibility of secondary accidents. Unsafe speed and weather are other factors contributing to secondary crash occurrence. It is strongly suggested that when police or rescue personnel arrive at the scene of an accident, they should not suddenly block, decrease, or unblock the traffic flow, but instead endeavor to control traffic in a smooth and controlled manner. Also it is important to reduce accident processing time to reduce the risk of secondary accident.     

Vehicle occupant injury severity on highways: An empirical investigation

Accident severity analysis is important to both researchers and practitioners because of its implications in accident cost estimation, external cost estimation and road safety. Although much research has been done to explore the factors influencing crash-injury severity, few studies have investigated the association between severity and traffic characteristics collected real-time during the time the accident occurred. We apply a random parameters ordered probit model to explore the influence of speed and traffic volume on the injury level sustained by vehicle occupants involved in accidents on the A4–A86 junction in the Paris region. Results indicate that increased traffic volume has a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions.     

Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections

Poisson and negative binomial (NB) models have been used to analyze traffic accident occurrence at intersections for several years. There are however, limitations in the use of such models. The Poisson model requires the variance-to-mean ratio of the accident data to be about 1. Both the Poisson and the NB models require the accident data to be uncorrelated in time. Due to unobserved heterogeneity and serial correlation in the accident data, both models seem to be inappropriate. A more suitable alternative is the random effect negative binomial (RENB) model, which by treating the data in a time-series cross-section panel, will be able to deal with the spatial and temporal effects in the data. This paper describes the use of RENB model to identify the elements that affect intersection safety. To establish the suitability of the model, several goodness-of-fit statistics are used. The model is then applied to investigate the relationship between accident occurrence and the geometric, traffic and control characteristics of signalized intersections in Singapore. The results showed that 11 variables significantly affected the safety at the intersections. The total approach volumes, the numbers of phases per cycle, the uncontrolled left-turn lane and the presence of a surveillance camera are among the variables that are the highly significant.     

Multi-scale traffic safety and operational performance study of large trucks on mountainous interstate highway

In addition to multi-vehicle accidents, large trucks are also prone to single-vehicle accidents on the mountainous interstate highways due to the complex terrain and fast-changing weather. By integrating both historical data analysis and simulations, a multi-scale approach is developed to evaluate the traffic safety and operational performance of large trucks on mountainous interstate highways in both scales of individual vehicle as well as traffic on the whole highway. A typical mountainous highway in Colorado is studied for demonstration purposes. Firstly, the ten-year historical accident records are analyzed to identify the accident-vulnerable-locations (AVLs) and site-specific critical adverse driving conditions. Secondly, simulation-based single-vehicle assessment is performed for different driving conditions at those AVLs along the whole corridor. Finally, the cellular-automaton (CA)-based simulation is carried out to evaluate the multi-vehicle traffic safety as well as the operational performance of the traffic by considering the actual speed limits, including the differential speed limits (DSL) at some locations. It is found that the multi-scale approach can provide insightful and comprehensive observations of the highway performance, which is especially important for mountainous highways.     

Are speed enforcement cameras more effective than other speed management measures? The impact of speed management schemes on 30 mph roads

This paper presents the results of an evaluation of the impact of various types of speed management schemes on both traffic speeds and accidents. The study controls for general trends in accidents, regression-to-mean effects and migration, separately estimating the accident changes attributable to the impact of the schemes on traffic speed and on traffic volume. It was found that, when judged in absolute terms, all types of speed management scheme have remarkably similar effects on accidents, with an average fall in personal injury accidents of about 1 accident/km/year. In terms of the percentage accident reduction, however, engineering schemes incorporating vertical deflections (such as speed humps or cushions) offer the largest benefits: at 44%, the average reduction in personal injury accidents attributable to such schemes, is twice that at sites where safety cameras were used to control speeds (22%) and they were the only type of scheme to have a significant impact on fatal and serious accidents. Other types of engineering scheme (with a fall of 29% in personal injury accidents) were on average less effective in reducing accidents than schemes with vertical features but more effective than cameras. All types of scheme were generally effective in reducing speeds, with the largest reductions tending to be obtained with vertical deflections and the smallest with other types of engineering schemes.     

Quantifying safety benefit of winter road maintenance: Accident frequency modeling

This research presents a modeling approach to investigate the association of the accident frequency during a snow storm event with road surface conditions, visibility and other influencing factors controlling for traffic exposure. The results have the premise to be applied for evaluating different maintenance strategies using safety as a performance measure. As part of this approach, this research introduces a road surface condition index as a surrogate measure of the commonly used friction measure to capture different road surface conditions. Data from various data sources, such as weather, road condition observations, traffic counts and accidents, are integrated and used to test three event-based models including the Negative Binomial model, the generalized NB model and the zero inflated NB model. These models are compared for their capability to explain differences in accident frequencies between individual snow storms. It was found that the generalized NB model best fits the data, and is most capable of capturing heterogeneity other than excess zeros. Among the main results, it was found that the road surface condition index was statistically significant influencing the accident occurrence. This research is the first showing the empirical relationship between safety and road surface conditions at a disaggregate level (event-based), making it feasible to quantify the safety benefits of alternative maintenance goals and methods.     

Evaluation of the impacts of traffic states on crash risks on freeways

The primary objective of this study is to divide freeway traffic flow into different states, and to evaluate the safety performance associated with each state. Using traffic flow data and crash data collected from a northbound segment of the I-880 freeway in the state of California, United States, K-means clustering analysis was conducted to classify traffic flow into five different states. Conditional logistic regression models using case-controlled data were then developed to study the relationship between crash risks and traffic states. Traffic flow characteristics in each traffic state were compared to identify the underlying phenomena that made certain traffic states more hazardous than others. Crash risk models were also developed for different traffic states to identify how traffic flow characteristics such as speed and speed variance affected crash risks in different traffic states. The findings of this study demonstrate that the operations of freeway traffic can be divided into different states using traffic occupancy measured from nearby loop detector stations, and each traffic state can be assigned with a certain safety level. The impacts of traffic flow parameters on crash risks are different across different traffic flow states. A method based on discriminant analysis was further developed to identify traffic states given real-time freeway traffic flow data. Validation results showed that the method was of reasonably high accuracy for identifying freeway traffic states.     

Pedestrian fatality risk as a function of car impact speed

Knowledge of the amount of violence tolerated by the human body is essential when developing and implementing pedestrian safety strategies. When estimating the potential benefits of new countermeasures, the pedestrian fatality risk as a function of impact speed is of particular importance. Although this function has been analysed previously, we state that a proper understanding does not exist. Based on the largest in-depth, pedestrian accident study undertaken to date, we derive an improved risk function for adult pedestrians hit by the front of passenger cars. Our results show far lower fatality risks than generally reported in the traffic safety literature. This discrepancy is primarily explained by sample bias towards severe injury accidents in earlier studies. Nevertheless, a strong dependence on impact speed is found, with the fatality risk at 50 km/h being more than twice as high as the risk at 40 km/h and more than five times higher than the risk at 30 km/h. Our findings should have important implications for the development of pedestrian accident countermeasures worldwide. In particular, the scope of future pedestrian safety policies and research should be broadened to include accidents with impact speeds exceeding 50 km/h.     

Quantification method analysis of the relationship between occupant injury and environmental factors in traffic accidents

Injury analysis following a vehicle crash is one of the most important research areas. However, most injury analyses have focused on one-dimensional injury variables, such as the AIS (Abbreviated Injury Scale) or the IIS (Injury Impairment Scale), at a time in relation to various traffic accident factors. However, these studies cannot reflect the various injury phenomena that appear simultaneously. In this paper, we apply quantification method II to the NASS (National Automotive Sampling System) CDS (Crashworthiness Data System) to find the relationship between the categorical injury phenomena, such as the injury scale, injury position, and injury type, and the various traffic accident condition factors, such as speed, collision direction, vehicle type, and seat position. Our empirical analysis indicated the importance of safety devices, such as restraint equipment and airbags. In addition, we found that narrow impact, ejection, air bag deployment, and higher speed are associated with more severe than minor injury to the thigh, ankle, and leg in terms of dislocation, abrasion, or laceration.     

Highway accident severities and the mixed logit model: an exploratory empirical analysis

Many transportation agencies use accident frequencies, and statistical models of accidents frequencies, as a basis for prioritizing highway safety improvements. However, the use of accident severities in safety programming has been often been limited to the locational assessment of accident fatalities, with little or no emphasis being placed on the full severity distribution of accidents (property damage only, possible injury, injury)-which is needed to fully assess the benefits of competing safety-improvement projects. In this paper we demonstrate a modeling approach that can be used to better understand the injury-severity distributions of accidents on highway segments, and the effect that traffic, highway and weather characteristics have on these distributions. The approach we use allows for the possibility that estimated model parameters can vary randomly across roadway segments to account for unobserved effects potentially relating to roadway characteristics, environmental factors, and driver behavior. Using highway-injury data from Washington State, a mixed (random parameters) logit model is estimated. Estimation findings indicate that volume-related variables such as average daily traffic per lane, average daily truck traffic, truck percentage, interchanges per mile and weather effects such as snowfall are best modeled as random-parameters-while roadway characteristics such as the number of horizontal curves, number of grade breaks per mile and pavement friction are best modeled as fixed parameters. Our results show that the mixed logit model has considerable promise as a methodological tool in highway safety programming.     

Safety effects of permanent running lights for bicycles: A controlled experiment

Making the use of daytime running lights mandatory for motor vehicles is generally documented to have had a positive impact upon traffic safety. Improving traffic safety for bicyclists is a focal point in the road traffic safety work in Denmark. In 2004 and 2005 a controlled experiment including 3845 cyclists was carried out in Odense, Denmark in order to examine, if permanent running lights mounted to bicycles would improve traffic safety for cyclists. The permanent running lights were mounted to 1845 bicycles and the accident rate was recorded through 12 months for this treatment group and 2000 other bicyclists, the latter serving as a control group without bicycle running lights. The safety effect of the running lights is analysed by comparing incidence rates – number of bicycle accidents recorded per man-month – for the treatment group and the control group. The incidence rate, including all recorded bicycle accidents with personal injury to the participating cyclist, is 19% lower for cyclists with permanent running lights mounted; indicating that the permanent bicycle running light significantly improves traffic safety for cyclists. The study shows that use of permanent bicycle running lights reduces the occurrence of multiparty accidents involving cyclists significantly. In the study the bicycle accidents were recorded trough self-reporting on the Internet. Possible shortcomings and problems related to this accident recording are discussed and analysed.     

Speed, speed limits and road traffic accidents under free flow conditions

The relationship between various measures of traffic speed, under free flow conditions, and accident rate is investigated for two groups of sites, one in the Tyne and Wear county of the UK and the other in Bahrain. The effect of speed limits on traffic speed is estimated for both groups of sites. In Bahrain, there is statistically significant evidence of an association between mean speed and accident rate. In Tyne and Wear the statistical evidence is weaker, and points to a stronger relationship between accidents and the variability of traffic speeds. In both areas, there is an apparent decrease in accident rate if the percentage of heavy vehicles increases, with the speed distribution held constant. In both areas the effect of speed limits is to reduce the mean speed of traffic by at least one quarter. Higher speeds are associated with longer trips.     

Impact of traffic oscillations on freeway crash occurrences

Traffic oscillations are typical features of congested traffic flow that are characterized by recurring decelerations followed by accelerations (stop-and-go driving). The negative environmental impacts of these oscillations are widely accepted, but their impact on traffic safety has been debated. This paper describes the impact of freeway traffic oscillations on traffic safety. This study employs a matched case-control design using high-resolution traffic and crash data from a freeway segment. Traffic conditions prior to each crash were taken as cases, while traffic conditions during the same periods on days without crashes were taken as controls. These were also matched by presence of congestion, geometry and weather. A total of 82 cases and about 80,000 candidate controls were extracted from more than three years of data from 2004 to 2007. Conditional logistic regression models were developed based on the case-control samples. To verify consistency in the results, 20 different sets of controls were randomly extracted from the candidate pool for varying control-case ratios. The results reveal that the standard deviation of speed (thus, oscillations) is a significant variable, with an average odds ratio of about 1.08. This implies that the likelihood of a (rear-end) crash increases by about 8% with an additional unit increase in the standard deviation of speed. The average traffic states prior to crashes were less significant than the speed variations in congestion.     

基于支持向量机的高速公路事故实时风险预测

以高速公路事故数据、交通流数据和天气数据为基础,以交通流为事故主要影响因素,建模预测高速公路事故实时风险。将事故记录作为病例组,采用病例对照方法来配对匹配实验样本,通过随机森林算法从众多变量中筛选出对事故风险影响最重要的10 个特征变量,以支持向量机建立模型预测事故实时风险。实验表明,通过随机森林筛选重要的特征变量,再使用支持向量机建模预测事故风险具有可行性,且以高斯核、Sigmoid核作为支持向量机的核函数比线性核函数和多项式核函数时分类准确性更高;其中,高斯核下支持向量机模型对事故风险预判的准确率达73.20%,对正常交通流的分类达91.44%。     

Road safety effects of porous asphalt: a systematic review of evaluation studies

This paper presents a systematic review of studies that have evaluated the effects on road safety of porous asphalt. Porous asphalt is widely used on motorways in Europe, mainly in order to reduce traffic noise and increase road capacity. A meta-analysis was made of six studies, containing a total of eighteen estimates of the effect of porous asphalt on accident rates. No clear effect on road safety of porous asphalt was found. All summary estimates of effect indicated very small changes in accident rates and very few were statistically significant at conventional levels. Studies that have evaluated the effects of porous asphalt on nine different risk factors associated with accident occurrence were also reviewed. It was found that four of the risk factors were favourably influenced by porous asphalt, three were adversely influenced, and two were not influenced by porous asphalt. The net impact of these changes in risk factors on accident occurrence cannot be predicted. On the whole, the research that has been reported so far regarding road safety effects of porous asphalt is inconclusive. The studies are not of high quality and the findings are inconsistent.     

Application of classification algorithms for analysis of road safety risk factor dependencies

Transportation continues to be an integral part of modern life, and the importance of road traffic safety cannot be overstated. Consequently, recent road traffic safety studies have focused on analysis of risk factors that impact fatality and injury level (severity) of traffic accidents. While some of the risk factors, such as drug use and drinking, are widely known to affect severity, an accurate modeling of their influences is still an open research topic. Furthermore, there are innumerable risk factors that are waiting to be discovered or analyzed. A promising approach is to investigate historical traffic accident data that have been collected in the past decades. This study inspects traffic accident reports that have been accumulated by the California Highway Patrol (CHP) since 1973 for which each accident report contains around 100 data fields. Among them, we investigate 25 fields between 2004 and 2010 that are most relevant to car accidents. Using two classification methods, the Naive Bayes classifier and the decision tree classifier, the relative importance of the data fields, i.e., risk factors, is revealed with respect to the resulting severity level. Performances of the classifiers are compared to each other and a binary logistic regression model is used as the basis for the comparisons. Some of the high-ranking risk factors are found to be strongly dependent on each other, and their incremental gains on estimating or modeling severity level are evaluated quantitatively. The analysis shows that only a handful of the risk factors in the data dominate the severity level and that dependency among the top risk factors is an imperative trait to consider for an accurate analysis.     

Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar

Geographical Information System (GIS) technology has been a popular tool for visualization of accident data and analysis of hot spots in highways. Many traffic agencies have been using GIS for accident analysis. Accident analysis studies aim at the identification of high rate accident locations and safety deficient areas on the highways. So, traffic officials can implement precautionary measures and provisions for traffic safety. Since accident reports are prepared in textual format in Turkey, this situation makes it difficult to analyze accident results. In our study, we developed a system transforming these textual data to tabular form and then this tabular data were georeferenced onto the highways. Then, the hot spots in the highways in Afyonkarahisar administrative border were explored and determined with two different methods of Kernel Density analysis and repeatability analysis. Subsequently, accident conditions at these hot spots were examined. We realized that the hot spots determined with two methods reflect really problematic places such as cross roads, junction points etc. Many of previous studies introduced GIS only as a visualization tool for accident locations. The importance of this study was to use GIS as a management system for accident analysis and determination of hot spots in Turkey with statistical analysis methods.     

Multidisciplinary in-depth investigations of head-on and left-turn road collisions

To enhance traffic safety, a multidisciplinary Road Accident Investigation Team was established in Denmark for a 2-year trial period. The objective was to conduct in-depth investigations of specific types of accidents, and to identify effective preventive measures. The team consisted of a road engineer, a vehicle inspector, a police superintendent, a psychologist and a physician. Seventeen serious head-on collisions as well as 17 left-turn collisions were analysed. In collecting data, police reports were supplemented by the team's investigation of accident sites and vehicles involved, and interviews were carried out with the involved road users and witnesses. The drivers, to whom the accident factors were primarily related in the head-on collisions, were characterised by their conscious risk-taking behaviour. They were all males; several of them were under age 40 and had earlier traffic and/or drug convictions. The main accident factors were excessive speed, drunk driving and driving under the influence of illegal drugs. In the left-turn accidents, the most common accident factors were attention errors, and it was also noted that elderly drivers ( > 74) were over-represented. The synergy effect of working as a multidisciplinary team proved fruitful. It resulted in a more precise knowledge of the road accident circumstances and of contributing factors leading up to the accidents. Due to the great demand on resources, only a limited number of accidents could be analysed, but the results provide a basis for further and more targeted research.     

Logistic regression analysis of pedestrian casualty risk in passenger vehicle collisions in China

A large number of pedestrian fatalities were reported in China since the 1990s, however the exposure of pedestrians in public traffic has never been measured quantitatively using in-depth accident data. This study aimed to investigate the association between the impact speed and risk of pedestrian casualties in passenger vehicle collisions based on real-world accident cases in China. The cases were selected from a database of in-depth investigation of vehicle accidents in Changsha-IVAC. The sampling criteria were defined as (1) the accident was a frontal impact that occurred between 2003 and 2009; (2) the pedestrian age was above 14; (3) the injury according to the Abbreviated Injury Scale (AIS) was 1+; (4) the accident involved passenger cars, SUVs, or MPVs; and (5) the vehicle impact speed can be determined. The selected IVAC data set, which included 104 pedestrian accident cases, was weighted based on the national traffic accident data. The logistical regression models of the risks for pedestrian fatalities and AIS 3+ injuries were developed in terms of vehicle impact speed using the unweighted and weighted data sets. A multiple logistic regression model on the risk of pedestrian AIS 3+ injury was developed considering the age and impact speed as two variables. It was found that the risk of pedestrian fatality is 26% at 50 km/h, 50% at 58 km/h, and 82% at 70 km/h. At an impact speed of 80 km/h, the pedestrian rarely survives. The weighted risk curves indicated that the risks of pedestrian fatality and injury in China were higher than that in other high-income countries, whereas the risks of pedestrian casualty was lower than in these countries 30 years ago. The findings could have a contribution to better understanding of the exposures of pedestrians in urban traffic in China, and provide background knowledge for the development of strategies for pedestrian protection.     

Optimal use of warning signs in traffic

The aim of the paper is to develop a model of drivers’ behaviour particularly designed to analyse the safety and total driving cost implications of warning sign installations. One special feature of the model is that it makes a clear distinction between drivers’ perceived risk values at certain speeds and their respective objective values. When focusing on a certain stretch of road only, the paper concludes that warning signs will increase safety and probably reduce total objective driving costs; that is the sum of time costs and objective expected accident costs. Since drivers’ speed will reduce implying higher time costs per distance, the reduction in total objective driving costs will be lower than the reductions in accident costs. The analysis is then extended to comprise the whole road system and using warning signs prior to curves as an example. Besides the driving conditions in different curves, the analysis shows that the optimal number of signs is dependent on the road authorities’ objectives for road traffic and on how drivers form their risk perceptions. Generally speaking, simulations indicate that the safety and economic benefits of warning sign installation are not very high. When considering the whole road system, warning signs seem, however, to have a greater positive impact on total driving costs than on accident costs.