Traffic accident severity analysis with rain-related factors using structural equation modeling - A case study of Seoul City

Optimization of colored pavement considering driving behavior and psychological characteristics under dynamic low-visibility conditions related to fog-a driving simulator study

OBJECTIVE

Colored pavement is commonly used to reduce the road traffic risk and promote road traffic safety, but its performance in foggy environments has not been fully assessed. The goal of this research is to explore the effectiveness and optimization of colored pavement in a dynamic low-visibility environment.

METHODS

A driving simulation experiment is conducted. Three road risk sections in which collisions are common, including a long straight section, a sharp bend section, and a long downslope section, are considered, and three forms of colored pavement are used in five different visibility environments. The effectiveness of the colored pavement is explored by collecting and analyzing driving behavior and physiological characteristic data for 30 drivers in the established driving environment, and information is obtained through a subjective colored evaluation questionnaire. Eight evaluation indexes are selected from the perspectives of driving behavior and physiological characteristics, and the gray premium evaluation method is applied to evaluate the effectiveness of different forms of colored pavement considering the influence of visibility. Finally, the optimal colored pavement under various visibility and road alignment conditions is proposed.

RESULTS

The results show that reasonably selecting colored pavement can effectively improve drivers' behaviors and physiological characteristics under foggy conditions. For different road alignments and visibility conditions, different forms of colored pavement should be used to ensure road traffic safety.

CONCLUSIONS

The findings provide a theoretical reference for the optimization of colored pavement in foggy conditions.

Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression

Previous researches have demonstrated that traffic crashes in urban areas are geographical events and strongly linked to local characteristics such as road network and land attributes. However, with a significant emphasis on moving-vehicle crashes, the spatial pattern of fixed-object crashes is unclear so far. The difference between these two types of crashes, and whether existing spatial tools such as geographically weighted regression can interpretate the occurrence mode have not been investigated before. To fill this gap, this paper focuses on understanding the spatial features and occurrence of these two types of crash, i.e., moving-vehicle and fixed-object on the city level. Crash data from Dalian, China were aggregated into subdistricts and calibrated with multi-scale geographically weighted regression (MGWR) models. A noticeable but similar clustering pattern was revealed in both types, with spatial overlap of their accident-prone regions. The spatial influence of explanatory variables (road network, geographic, demographic, socio-economic, and land-use variables) was also found mostly similar in both types of crashes. However, fixed-object crash in downtown is more affected by node count, while POI entrance/exit count, especially those in areas with more industrial zones tend to significantly reduce crash risk. In both types of crashes, terrain slope rather than elevation is found to mitigate the crash risk, especially in the downtown area. Compared to traditional Geographically Weighted Regression (GWR) with a fixed bandwidth, the improvement in modeling performance using MGWR highlights the reasonability and benefits to consider the influence scale of each contributing factor in urban spatial analysis of traffic collisions. This study could help transportation authorities identify high-risk regions, understand their contributing factors and take precautions for improving the local traffic safety.

Exploring Factors Affecting Crash Injury Severity with Consideration of Secondary Collisions in Freeway Tunnels

Although there have been several studies conducted exploring the factors affecting injury severity in tunnel crashes, most studies have focused on identifying factors that directly influence injury severity. In particular, variables related to crash characteristics and tunnel characteristics affect the injury severity, but the inconvenient driving environment in a tunnel space, characterized by narrow space and dark lighting, can affect crash characteristics such as secondary collisions, which in turn can affect the injury severity. Moreover, studies on secondary collisions in freeway tunnels are very limited. The objective of this study was to explore factors affecting injury severity with the consideration of secondary collisions in freeway tunnel crashes. To account for complex relationships between multiple exogenous variables and endogenous variables by considering the direct and indirect relationships between them, this study used a structural equation modeling with tunnel crash data obtained from Korean freeway tunnels from 2013 to 2017. Moreover, based on high-definition closed-circuit televisions installed every 250 m to monitor incidents in Korean freeway tunnels, this study utilized unique crash characteristics such as secondary collisions. As a result, we found that tunnel characteristics indirectly affected injury severity through crash characteristics. In addition, one variable regarding crashes involving drivers younger than 40 years old was associated with decreased injury severity. By contrast, ten variables exhibited a higher likelihood of severe injuries: crashes by male drivers, crashes by trucks, crashes in March, crashes under sunny weather conditions, crashes on dry surface conditions, crashes in interior zones, crashes in wider tunnels, crashes in longer tunnels, rear-end collisions, and secondary collisions with other vehicles.

Predicting traffic crash severity using hybrid of balanced bagging classification and light gradient boosting machine

Accident severity prediction is a hot topic of research aimed at ensuring road safety as well as taking precautionary measures for anticipated future road crashes. In the past decades, both classical statistical methods and machine learning algorithms have been used to predict traffic crash severity. However, most of these models suffer from several drawbacks including low accuracy, and lack of interpretability for people. To address these issues, this paper proposed a hybrid of Balanced Bagging Classification (BBC) and Light Gradient Boosting Machine (LGBM) to improve the accuracy of crash severity prediction and eliminate the issues of bias and variance. To the best of the author’s knowledge, this is one of the pioneer studies which explores the application of BBC-LGBM to predict traffic crash severity. On the accident dataset of Great Britain (UK) from 2013 to 2019, the proposed model has demonstrated better performance when compared with other models such as Gaussian Naïve Bayes (GNB), Support vector machines (SVM), and Random Forest (RF). More specifically, the proposed model managed to achieve better performance among all metrics for the testing dataset (accuracy = 77.7%, precision = 75%, recall = 73%, F1-Score = 68%). Moreover, permutation importance is used to interpret the results and analyze the importance of each factor influencing crash severity. The accuracy-enhanced model is significant to several stakeholders including drivers for early alarm and government departments, insurance companies, and even hospitals for the services concerned about human lives and property damage in road crashes.

Can haze warning policy reduce traffic accidents: evidence from China

Haze pollution may decrease drivers' driving performance by worsening their physical and psychological states. This paper explores the effects of haze warning policy on traffic accidents for the first time. We use the daily-city traffic accident data from 2016 to 2019 in China and construct Regression Discontinuity (RD) strategies based on the warning signal thresholds of PM2.5 concentration for estimations. The results show that one yellow warning and one orange warning can reduce traffic accidents by 8.8% and 13.1% on that day respectively, while the red warning does not work significantly possibly due to the self-perceived channel rather than the warning-signal channel. We also find that the effects may vary among different groups of drivers, vehicles, and roads. Our results prove that the haze warning policy is a non-negligible tool to reduce traffic accidents, which is useful to policy-making both related to haze pollution regulation and transportation safety.

Exploring Influential Factors Affecting the Severity of Urban Expressway Collisions: A Study Based on Collision Data

When traffic collisions occur on urban expressways, the consequences, including injuries, the loss of lives, and damage to properties, are more serious. However, the existing research on the severity of expressway traffic collisions has not been deeply explored. The purpose of this research was to investigate how various factors affect the severity of urban expressway collisions. The severity of urban expressway collisions was set as the dependent variable, which could be divided into three categories: slight collisions, severe collisions, and fatal collisions. Ten variables, including individual characteristics, collision characteristics, and road environment conditions, were selected as independent factors. Based on 975 valid urban expressway collisions, an ordered logistic regression model was established to evaluate the impacts of influence factors on the severity of these crashes. The results show that gender, collision modality, road pavement conditions, road surface conditions, and visibility are significant factors that affect the severity of urban expressway collisions. Females were more likely to be involved in more severe urban expressway collisions than males. For collisions involving pedestrians and non-motorized vehicles, the risk of more severe injury was 7.508 times higher than that associated with vehicle-vehicle collisions. The probability of more severe collisions on urban expressways with poor pavement conditions and wet surface conditions is greater than that on urban expressways with good pavement conditions and dry surface conditions. In addition, as visibility increases, the probability of more severe collisions on urban expressways gradually decreases. These results provide more effective strategies to reduce casualties as a result of urban expressway collisions.

Diagnostic analysis of environmental factors affecting the severity of traffic crashes: From the perspective of pedestrian-vehicle and vehicle-vehicle collisions

OBJECTIVES

Traffic crashes under low-visibility conditions are frequent and serious. The aim of this study was to investigate how the road environment affects the severity of pedestrian-vehicle and vehicle-vehicle collisions under low-visibility conditions.

METHODS

The injury severity of pedestrian-vehicle collisions and vehicle-vehicle collisions under low-visibility conditions was set as the dependent variable and divided into 2 categories: "killed or severe injury collision" and "slight injury collision." Ten variables, including environment conditions, road traffic facility status, collision characteristics, and road attributes, were selected as independent factors according to the existing research and the traffic collision data set. Based on 656 valid pedestrian-vehicle collisions and 1,430 valid vehicle-vehicle collisions under low-visibility conditions, 2 random parameter logit models were established to evaluate the impacts of influencing factors on the severity of pedestrian-vehicle collisions and vehicle-vehicle collisions, in which the effect of unobserved heterogeneity was accounted for.

RESULTS

The results show that visibility, presence of a roadside protection, road type, road pavement condition, and road alignment were significant factors affecting the severity of pedestrian-vehicle collisions. In addition, the presence of a median divider, location of the collision, road type, road surface condition, road pavement condition, and road alignment were significant factors affecting the severity of vehicle-vehicle collisions. Furthermore, the injury severity of both pedestrian-vehicle collisions and vehicle-vehicle collisions under low-visibility conditions on highways, poor road pavement, and non-straight-line sections was more likely to be fatal or serious.

CONCLUSION

These results have implications for the design of more effective strategies to reduce casualties from traffic crashes under low-visibility conditions.

Analysis of the regional distribution of road traffic mortality and associated factors in Japan

BACKGROUND

Regional differences in road traffic (RT) mortality among municipalities have not been revealed in Japan. Further, the association between RT mortality and regional socioeconomic characteristics has not been investigated. We analyzed geographic differences in RT mortality and its associated factors using the Vital Statistics in Japan.

METHODS

We used data on RT mortality by sex and municipality in Japan from 2013 to 2017. We calculated the standardized mortality ratio (SMR) of RT for each municipality by sex using an Empirical Bayes method. The SMRs were mapped onto a map of Japan to show the geographic differences. In addition, an ecological study investigated the municipal characteristics associated with the SMR using demographic socioeconomic, medical, weather, and vehicular characteristics as explanatory variables. The ecological study used a spatial statistical model.

RESULTS

The mapping revealed that the number of municipalities with a high SMR of RT (SMR > 2) was larger in men than in women. In addition, SMRs of capital areas (Kanagawa and Tokyo prefectures) tended to be low in men and women. The regression analysis revealed that population density was negatively associated with the SMR in men and women, and the degree of the association was the largest among explanatory variables. In contrast, there was a positive association between the proportion of non-Japanese persons and SMR. The proportions of lower educational level (elementary school or junior high school graduates), agriculture, forestry, and fisheries workers, service workers, and blue-collar workers were positively associated with the SMR in men. The proportion of unemployed persons was negatively associated with the SMR in men.

CONCLUSIONS

Socioeconomic characteristics are associated with geographic differences in RT mortality particularly in men. The results suggested preventive measures targeted at men of low socioeconomic status and non-Japanese persons are needed to decrease RT mortality further.

Assessment of the Influence of Technology-Based Distracted Driving on Drivers' Infractions and Their Subsequent Impact on Traffic Accidents Severity

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.

Predicting the water film depth: A model based on the geometric features of road and capacity of drainage facilities

The water film depth is a key variable that affects traffic safety under rainfall conditions. According to the Federal Highway Administration, approximately 5700 people are killed and more than 544 700 people are injured in crashes on wet pavements annually. While several studies have attempted to address water film depth issues by establishing prediction models, a few focused on the relationship among road geometric features, capacity of drainage facilities and water film depth. To ascertain the influence of the geometric features of road and facility drainage capacities on the water film depth, the road geometry features were first classified into four types, and the facility drainage capacities were considered from three aspects in this study. Furthermore, the concept of short-time rainfall grade was proposed according to the results of the field test. Finally, the theoretical prediction model for the water film depth was conceived, based on the geometric features of road and facility drainage capacities with different rainfall intensities. Compared with the traditional regression prediction models, the theoretical prediction model clearly shows the effects of the geometric features of road and facility drainage capacities. When the road drainage facilities have no drainage capacity, the water film depth increases rapidly with the rainfall intensity. This model can be used to predict the water film depth of road surfaces on rainy days, evaluate the effect of rainfall on the driving environment, and provide guidance for determining safety control measures on rainy days.

Rule discovery to identify patterns contributing to overrepresentation and severity of run-off-the-road crashes

The main objective of this paper was to analyse the roadway, environmental, and driver-related factors associated with an overrepresentation of frequency and severity of run-off-the-road (ROR) crashes. The data used in this study refer to the 6167 crashes occurred in the section Naples-Candela of A16 motorway, Italy in the period from 2001 to 2011. The analysis was carried out using the rule discovery technique due to its ability of extracting knowledge from large amounts of data previously unknown and indistinguishable by investigating patterns that occur together in a given event. The rules were filtered by support, confidence, lift, and validated by the lift increase criterion. A two-step analysis was carried out. In the first step, rules discovering factors contributing to ROR crashes were identified. In the second step, studying only ROR crashes, rules discovering factors contributing to severe and fatal injury (KSI) crashes were identified. As a result, 94 significant rules for ROR crashes and 129 significant rules for KSI crashes were identified. These rules represent several combinations of geometric design, roadside, barrier performance, crash dynamic, vehicle, environmental and drivers' characteristics associated with an overrepresentation of frequency and severity of ROR crashes. From the methodological point of view, study results show that the a priori algorithm was effective in providing new information which was previously hidden in the data. Finally, several countermeasures to solve or mitigate the safety issues identified in this study were discussed. It is worthwhile to observe that the study showed a combination of factors contributing to the overrepresentation of frequency and severity of ROR crashes. Consequently, the implementation of a combination of countermeasures is recommended.

Incorporating accident liability into crash risk analysis: A multidimensional risk source approach

In the field of traffic safety, the occurrence of accidents remains a cause of concern for road regulators as well as users. Exploring risk factors inducing the accidents and quantifying the accident risk will not only benefit the prevention and control of traffic accidents but also assist in developing effective risk propagation model for road accidents. This study uses detailed accident record data to mine the risk factors affecting the occurrence of accidents, and quantify the accident risk under the combination of risk factors. First, by reviewing relevant literature and analyzing historical accident, we construct a multi-dimension characterization framework of risk factors with bi-level structure. The Human Factors Analysis and Classification System (HFACS) is applied to supplement and improve the framework. Next, under this framework, we identify the risk factors in traffic accident record, and analyze the statistical characteristics from the level of risk sources and risk characteristics. Then, the concept of accident liability weight is proposed to measure the impact of risk factors on accident occurrence. Through the liability affirmation of risk factors, the accident probability are updated. Last, we establish an accident risk quantify model (ARQM) based on the mean mutual information to compare the likelihood of accidents in different scenarios. In addition, we compare the accident probability and risk under equivalent liability and liability affirmation, as well as give some fundamental ideas regarding how to effectively prevent accidents.

Exploration of the relationship among roadway characteristics, operating speed, and crashes for city streets using path analysis

Estimating the speed-crash relationship has long been a focus area of interest in roadway safety analysis. Because of many confounding factors that may influence both speeds and crashes, the relationship cannot be appropriately established without considering the corresponding roadway contexts and accounting for their effects on speeds and crashes. This paper investigates the speed-crash relationship for city streets by jointly modeling speed, roadway characteristics, and crashes using a path analysis approach that has been recently introduced into safety analysis while incorporating a wide range of roadway and traffic related variables and additional speed measures. The results from the coherent path analysis identified multiple speed measures of interest that have a statistically significant association with crashes as well as having intuitive and useful interpretation. The results also supported a positive relationship between speed variability and crash occurrence (i.e., larger spread/variability in operational speed is associated with more crashes).

Exploring the Impact of Climate and Extreme Weather on Fatal Traffic Accidents

Climate change and the extreme weather have a negative impact on road traffic safety, resulting in severe road traffic accidents. In this study, a negative binomial model and a log-change model are proposed to analyse the impact of various factors on fatal traffic accidents. The dataset used in this study includes the fatal traffic accident frequency, social development indicators and climate indicators in California and Arizona. The results show that both models can provide accurate fitting results. Climate variables (i.e., average temperature and standard precipitation 24) can significantly affect the frequency of fatal traffic accidents. Non-climate variables (i.e., beer consumption, rural Vehicle miles travelled ratio, and vehicle performance) also have a significant impact. The modelling results can provide decision-making guidelines for the transportation management agencies to improve road traffic safety.

Examining Injury Severity of Pedestrians in Vehicle-Pedestrian Crashes at Mid-Blocks Using Path Analysis

Walking is a sustainable mode of transport which has well established health and environmental benefits. Unfortunately, hundreds of thousands of pedestrians lose their lives each year over the world due to involvement in road traffic crashes, and mid-blocks witness a significant portion of pedestrian fatalities. This study examined the direct and indirect effects of various contributing factors on the pedestrian injury severity in vehicle-pedestrian crashes at mid-blocks. Data of vehicle-pedestrian crashes during 2002-2009 were extracted from the NASS-GES, with pre-crash behaviors and injury severity included. The SEM path analysis method was applied to uncover the inter-relationships between the pedestrian injury severity and various explanatory variables. Both the direct and indirect effects of these explanatory variables on the pedestrian injury severity were calculated based on the marginal effects in the multinomial and ordered logit models. The results indicate some variables including number of road lanes and the age of pedestrian have indirect impacts on the injury severity through influencing the pre-crash behaviors. Although most indirect effects are relatively small compared with the direct effects, the results in this study still provide some valuable information to improve the overall understanding of pedestrian injury severity at mid-blocks.

Determinants of fatal car accident risk in Finote Selam town, Northwest Ethiopia

Background

In the globe, 1.3 million deaths and around 50 million non-fatal injuries were reported. From all deaths, 90% occur in developing countries. Ethiopia is considered as one of the worst countries in the globe where road traffic accident causes a lot of fatalities and injuries of road users every year. The main objective of the study was to identify the main predictors of fatal car accident.

Methods

The retrospective research design was applied. 255 records were taken from Finote Selam traffic police office, northwest part of Ethiopia from September 2009 to January 2018. The statistical analysis was performed by using SPSS version 23 software. Chi-square for association test and ordinal logistic regression for predictor identification were used.

Results

Age of drivers were the responsible causes of fatal road traffic accident (p-value = 0.033). The more experienced drivers decreased the occurrence of fatal traffic accidents. In addition, increasing vehicle service year reduced the occurrence of accidental death. Besides, the occurrence of fatal car accident in autumn season was 0.44 times less than that of in summer season. Additionally, drivers’ educational level was played a crucial role in a road traffic accident. For instance, drivers whose educational level was below 12th grade were the most responsible factor for car accident deaths. What is more, it was seen that drivers who drove their vehicles could minimize the occurrence of fatal traffic accident (p-value = 0.010).

Conclusion

In conclusion, fatal road traffic accidents happened due to drivers’ lack of sufficient driving experience and low educational level. In addition, driving on weekends and driving on summer season were disclosed as responsible for fatal car accident. Moreover, drivers with younger age and those who drove a new vehicle likely caused fatal car accident. However, drivers who drive their vehicles seemed to be less responsible for fatal car accident than that of employed.

Risk factors of road accident severity and the development of a new system for prevention: New insights from China

Road accident fatalities and accident severity costs have become top priorities and concerns for Chinese policymakers. Understanding the principal factors that explain accident severity is considered to be the first step towards the adequate design of an accident prevention strategy. In this paper, we examine the contribution of various types of factors (vehicle, driver and others) in explaining accident severity in China. Unlike previous studies, the analysis gives a particular focus on fatal accidents. Using a large sample of 405,177 observations for 4-wheeled vehicles in the year 2017 and various statistical and econometrics approaches (e.g., OLS, quantile regression and extreme value theory), the results show that the factors explaining the severity of accidents differs significantly between normal and extreme severity accidents, e.g. across quantiles. Interestingly, we find that the gender factor is only significant for fatal accidents. In particular, the analysis shows that male drivers have an increased likelihood of extreme risk taking. On the basis of these empirical findings, a new ratemaking approach that aims to improve road safety and prevention is discussed and proposed.

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

There have been numerous studies on traffic accidents and their severity, particularly in relation to weather conditions and road geometry. In these studies, traditional statistical methods have been employed, such as linear regression, logistic regression, and negative binomial regression modeling, which are the most common linear and non-linear regression analysis methods. In this research, machine learning architecture was applied to this problem using the random forest, artificial neural network, and decision tree techniques to ascertain the strengths and weaknesses of these methods. Three data sets were used: road geometry data, precipitation data, and traffic accident data over nine years corresponding to the Naebu Expressway, which is located in Seoul, Korea. For the model evaluation, three measures were employed: the out-of-bag estimate of error rate (OOB), mean square error (MSE), and root mean square error (RMSE). The low mean OOB, MSE, and RMSE observed in the results obtained using the proposed random forest model demonstrate its accuracy.

Modelling driver propensity for traffic accidents: a comparison of multiple regression analysis and fuzzy approach

This research proposes an assessment and decision support model to use when a driver should be examined about their propensity for traffic accidents, based on an estimation of the driver's psychological traits. The proposed model was tested on a sample of 305 drivers. Each participant completed four psychological tests: the Barratt Impulsiveness Scale (BIS-11), the Aggressive Driving Behaviour Questionnaire (ADBQ), the Manchester Driver Attitude Questionnaire (DAQ) and the Questionnaire for Self-assessment of Driving Ability. In addition, participants completed an extensive demographic and driving survey. Various fuzzy inference systems were tested and each was defined using the well-known Wang-Mendel method for rule-base definition based on empirical data. For this purpose, a programming code was designed and utilized. Based on the obtained results, it was determined which combination of the considered psychological tests provides the best prediction of a driver's propensity for traffic accidents. The best of the considered fuzzy inference systems might be used as a decision support tool in various situations, such as in recruitment procedures for professional drivers. The validity of the proposed fuzzy approach was confirmed as its implementation provided better results than from statistics, in this case multiple regression analysis.

Diagnostic analysis of the effects of weather condition on pedestrian crash severity

Pedestrians are vulnerable to severe injury and mortality in road crashes. Numerous studies have attempted to identify factors contributing to crashes and pedestrian injury risks. As an active transport mode, the act of walking is sensitive to changes in weather conditions. However, comprehensive real-time weather data are often unavailable for road safety analysis. In this study, we used a geographical information system approach to integrate high-resolution weather data, as well as their corresponding temporal and spatial distributions, with crash data. Then, we established a mixed logit model to determine the association between pedestrian crash severity and possible risk factors. The results indicate that high temperature and the presence of rain were associated with a higher likelihood of Killed and Severe Injury (KSI) crashes. Also, we found the interaction effects of weather condition (hot weather and presence of rain) on the association between pedestrian crash severity and pedestrian and driver behaviors to be significant. For instance, the effects of jaywalking and risky driving behavior on crash severity were more prevalent under rainy conditions. In addition, the effects of driver inattention and reckless crossing were more significant in hot weather conditions. This has critical policy implications for the development and implementation of proactive traffic management systems. For instance, real-time weather and traffic data should be incorporated into dynamic message signs and in-vehicle warning systems. Doing so will enhance the levels of safety awareness of drivers and pedestrians, especially in adverse weather conditions. As a result, pedestrian safety can be improved over the long term.