Fatal Collisions Research Articles

Classification and association rule mining of road collisions for analyzing the fatal severity, a case study

IntroductionNowadays, a significant part of goods and passengers are transported on suburban highways with mainly high-speed vehicles. Hence, these highways are very prone to collisions with different injuries. For this reason, road collisions have become one of the largest international health issues in the world recently. Due to the high fatality or severe physical/mental injury rates caused by car collisions and the complex interactions between the factors affecting them, analyzing these collision-prone areas, identifying the factors affecting their occurrences, and discovering knowledge in the form of key rules is crucial, as the purposes for this study. MethodsThree supervised algorithms including Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forests (RF) were used to build up classification models for the fatality severity of 2355 fatal collision data records during 2007–2009 occurred in the roadways of 8 states in the USA with different driver-related, environmental, and road factors. Predicted risk maps were generated for each classifier and the importance of contributing factors was evaluated based on the mean decrease in accuracy and the mean decrease in Gini Index. Finally, association rule mining was performed by the Apriori algorithm to extract collision rules. ResultsRF outperformed the other methods in terms of the highest overall accuracy and kappa rates, which were 94% and 92% respectively. The risk maps revealed that collisions with the most fatalities were mainly concentrated in the northern states. The number of travel lanes, speed limit, roadway profile, and light conditions were the most influencing factors in the collisions. 68 association rules were mined by the Apriori algorithm. Then, important rules were specified to extract hidden information from the collision data. ConclusionsThe RF model was more accurate in collision analysis, along with comprehensive collision factors. Great importance should be given to road factors in route design, especially in critical locations with severe fatality risk.

Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain

Preventing and mitigating high severity collisions is one of the main opportunities for Automated Driving Systems (ADS) to improve road safety. This study evaluated the Waymo Driver’s performance within real-world fatal collision scenarios that occurred in a specific operational design domain (ODD). To address the rare nature of high-severity collisions, this paper describes the addition of novel techniques to established safety impact assessment methodologies. A census of fatal, human-involved collisions was examined for years 2008 through 2017 for Chandler, AZ, which overlaps the current geographic ODD of the Waymo One fully automated ride-hailing service. Crash reconstructions were performed on all available fatal collisions that involved a passenger vehicle as one of the first collision partners and an available map in this ODD to determine the pre-impact kinematics of the vehicles involved in the original crashes. The final dataset consisted of a total of 72 crashes and 91 vehicle actors (52 initiators and 39 responders) for simulations. Next, a novel counterfactual “what-if'' simulation method was developed to synthetically replace human-driven crash participants one at a time with the Waymo Driver. This study focused on the Waymo Driver’s performance when replacing one of the first two collision partners. The results of these simulations showed that the Waymo Driver was successful in avoiding all collisions when replacing the crash initiator, that is, the road user who made the initial, unexpected maneuver leading to a collision. Replacing the driver reacting (the responder) to the actions of the crash initiator with the Waymo Driver resulted in an estimated 82% of simulations where a collision was prevented and an additional 10% of simulations where the collision severity was mitigated (reduction in crash-level serious injury risk). The remaining 8% of simulations with the Waymo Driver in the responder role had a similar outcome to the original collision. All of these “unchanged” collisions involved both the original vehicle and the Waymo Driver being struck in the rear in a front-to-rear configuration. These results demonstrate the potential of fully automated driving systems to improve traffic safety compared to the performance of the humans originally involved in the collisions. The findings also highlight the major importance of driving behaviors that prevent entering a conflict situation (e.g. maintaining safe time gaps and not surprising other road users). However, methodological challenges in performing single instance counterfactual simulations based solely on police report data and uncertainty in ADS performance may result in variable performance, requiring additional analysis and supplemental methodologies. This study’s methods provide insights on rare, severe events that would otherwise only be experienced after operating in extreme real-world driving distances (many billions of driving miles).

Analysis of features of frontal collision of class D cars

Currently, there is a significant increase in road accidents, which can be associated with an increase in the number of cars, the condition of roads in the country and compliance with road discipline by road users. Modern automotive industry pays great attention to road safety. This is ensured by changes in the design of car interiors, which significantly affects the nature of injuries to victims of road accidents. That is why the solution of this issue has become extremely relevant for forensic expert practice.
 Aim of the work. To establish the characteristic injuries of the driver and passenger in the cabin of class D cars in a frontal collision.
 Material and methods. The analysis of road accidents from 2008 to 2021 in Zhytomyr, Rivne, Volyn regions of Ukraine is carried out. Cases of a fatal head-on collision with a driver and passenger in the front seat of a car with an engine capacity of 2-2.5 liters up to 4.7 meters long and 1.81 meters wide were considered. General and special methods used: system-structural analysis, observation, comparison, description. In addition, a forensic examination of the damage was conducted. Statistical analysis included primary data processing by descriptive statistics methods and null hypothesis testing by multifactor analysis.
 Results. A number of new features have been identified, which are inherent in the damage received in the cabin of a class D car, which can help to identify during the forensic examination. In particular, you should pay attention to the presence of neck injuries. According to the literature, injuries of the cervical spine are among the key and include: rupture of the atlanto-occipital ligament, dislocation and subluxation of the atlanto-axial joint, damage to the spinal cord and its membranes.
 Conclusions: The location and morphological features of spinal injuries can be used as diagnostic criteria in determining the location of the victim in the cabin of a mobile car class D in a frontal collision. The most informative is the analysis of injuries of the cervical spine of the driver and front passenger.

An Autopsy-Based Analysis of Fatal Road Traffic Collisions: How the Pattern of Injury Differs with the Type of Vehicle

In Italy, in only 2018, 3310 people died in road traffic accidents, more than in any other European country. Since the revelation of this occurrence, the authors carried out an analysis aimed at investigating if there was a difference in the injury patterns among different road users. A retrospective post-mortem study on road traffic fatalities was performed, which had been autopsied at the Institute of Forensic Medicine of Milan. First, the authors analyzed the epidemiological data of all the 1022 road traffic accidents subjected to an autopsy from 2007 to 2019. Secondly, further analysis of individual autopsy reports was carried out. For this purpose, 180 autopsies belonging to 5 different categories were analyzed: car, pedestrian, motorbike, bicycle, and truck. Seventy-six percent of road traffic fatalities were male, 54% were between 10 and 49 years of age, and 62% of the patients died before arriving at a hospital. “Multiple injuries” was the main cause of death. Traumatic brain injuries were particularly high in pedestrians and cyclists. In car, motorbike, and truck fatalities, thoracic and abdominal injuries were the most frequent. Therefore, pedestrians and cyclists had a higher prevalence for traumatic head injuries, while car, motorcycle, and truck occupants, on the other hand, had a higher prevalence for thoracic and abdominal injuries.

Prediction of Fatalities in Vehicle Collisions in Canada

Traffic collisions affect millions around the world and are the leading cause of death for children and young adults. Thus, Canada’s road safety plan is to reduce collision injuries and fatalities with a vision of making the safest roads in the world. We aim to predict fatalities of collisions on Canadian roads, and to discover causation of fatalities through exploratory data analysis and machine learning techniques. We analyse the vehicle collisions from Canada’s National Collision Database (1999–2017.) Through data mining methodologies, we investigate association rules and key contributing factors that lead to fatalities. Then, we propose two supervised learning classification models, Lasso Regression and XGBoost, to predict fatalities. Our analysis shows the deadliness of head-on collisions, especially in non-intersection areas with lacking traffic control systems. We also reveal that most collision fatalities occur in non-extreme weather and road conditions. Our prediction models show that the best classifier of fatalities is XGBoost with 83% accuracy. Its most important features are “collision configuration” and “used safety devices” elements, outnumbering attributes such as vehicle year, collision time, age, or sex of the individual. Our exploratory and predictive analysis reveal the importance of road design and traffic safety education.

Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors

Traffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailers, trucks, and panel trucks) contributed to 20.5% of the fatal road traffic collisions in the year 2013. This highlights the need for devoting special attention to heavy vehicle drivers to further explore their driving characteristics. In this research, the effect of heavy vehicle drivers’ behavior on at-fault collisions over three years has been investigated with an innovative approach of structural equation modeling (SEM) and Bayesian Network (BN). The database utilized in this research was collected using a questionnaire. For this purpose, 474 heavy vehicle drivers have been questioned in the Parviz Khan Border Market, located on the border of Iran and Iraq. The response rate of the survey was 80%. The participants answered the questions on Driver Behavior Questionnaire (DBQ) and a sleep assessing questionnaire named Global Dissatisfaction with Sleep (GSD). In this research, human factors affecting at-fault collisions of heavy vehicles were identified and their relationships with other variables were determined using the SEM approach. Then the descriptive model constructed by the SEM method was used as the basis of the BN, and the conditional probabilities of each node in the BN were calculated by the database collected by the field survey. SEM indicates that other attributes including GSD, mobile usage, daily fatigue, exposure, and education level have an indirect relation with heavy vehicle drivers’ at-fault collisions. According to the BN, if there is no information about the characteristics of a heavy vehicle driver, the driver will likely have at least one collision during the next three years with the probability of 0.17. Also, it was indicated that the minimum probability of the at-fault collision occurrence for a heavy vehicle is 0.08.

An integrated clustering and Bayesian approach to investigate the severity of pedestrian collisions at highway-railway grade crossings collisions

This study aims at developing a solid understanding of the contributing factors to pedestrian fatal and injury collisions at highway-railway grade crossings (HRGC), along with the impact of different warning devices that are commonly used at HRGCs. The study utilized integrated Machine Learning and Bayesian models to analyze the United States HRGC collision using the Federal Railroad Administration database between 2009 and 2018. The results demonstrate the association between different factors and the collision severity in each cluster and attempt to explain the inconsistency associated with the impact of some factors, such as weather conditions and pedestrian traits, on collision severity. The results also highlighted the conditions at which the different types of countermeasures and warning devices are most effective and the circumstances that limit their benefits. The results confirmed the benefits of the proposed analysis approach, in which collision data are classified into a group of clusters first before investigating the impact of the different factors on collision severity. The results wills support engineers and planners to develop specific policies and designs that aim at mitigating severe collisions at HRGCs and enhance pedestrian safety.

Bird strikes at commercial airports explained by citizen science and weather radar data

Abstract Aircraft collisions with birds span the entire history of human aviation, including fatal collisions during some of the first powered human flights. Much effort has been expended to reduce such collisions, but increased knowledge about bird movements and species occurrence could dramatically improve decision support and proactive measures to reduce them. Migratory movements of birds pose a unique, often overlooked, threat to aviation that is particularly difficult for individual airports to monitor and predict the occurrence of birds vary extensively in space and time at the local scales of airport responses. We use two publicly available datasets, radar data from the US NEXRAD network characterizing migration movements and eBird data collected by citizen scientists to map bird movements and species composition with low human effort expenditures but high temporal and spatial resolution relative to other large‐scale bird survey methods. As a test case, we compare results from weather radar distributions and eBird species composition with detailed bird strike records from three major New York airports. We show that weather radar‐based estimates of migration intensity can accurately predict the probability of bird strikes, with 80% of the variation in bird strikes across the year explained by the average amount of migratory movements captured on weather radar. We also show that eBird‐based estimates of species occurrence can, using species’ body mass and flocking propensity, accurately predict when most damaging strikes occur. Synthesis and applications. By better understanding when and where different bird species occur, airports across the world can predict seasonal periods of collision risks with greater temporal and spatial resolution; such predictions include potential to predict when the most severe and damaging strikes may occur. Our results highlight the power of federating datasets with bird movement and distribution data for developing better and more taxonomically and ecologically tuned models of likelihood of strikes occurring and severity of strikes.

A random parameters with heterogeneity in means and Lindley approach to analyze crash data with excessive zeros: A case study of head-on heavy vehicle crashes in Queensland

This study performed statistical analyses to identify likely crash contributing factors for Head-on Fatal and Serious Injury (FSI) collisions involving heavy vehicles (HVs) on the Queensland state road network. Head-on HV collisions are associated with the largest number of fatalities compared to other crash types in Queensland. However, there is limited relevant literature regarding this type of crashes. Recent studies on road safety research have focused on variants of random parameters models to capture unobserved heterogeneity that may influence the occurrence of crashes. Among such models, random parameters with heterogeneity in means has recently provided better results and has become popular. However, this study illustrates a potential limitation regarding the use of these models without explicitly factoring for excessive zero crash observations. To address this potential limitation, a random parameters with heterogeneity in means and a Lindley distribution is introduced in this study to factor for the unobserved heterogeneity using additional variables as well as site-specific variation from excessive zero crash observations. Results showed that a Poisson model with random parameters and heterogeneity in means using a Lindley distribution outperformed multiple alternative state-of-the-art specifications in terms of fit as well as overall prediction ability. The analyses using the proposed modelling approach revealed factors likely to affect the likelihood of Head-on FSI crashes involving HVs in Queensland including volume, segment length, period of analysis, terrain type being rolling, curve (moderate/sharp/very sharp) longer than 50% of the corresponding segment length, rural single carriageway with high (>=100 kph) and medium (>=50 and <100 kph) speed limits, and urban single carriageway. Unobserved heterogeneity regarding the parameter for road curvature was explained using rolling terrain type as an explanatory variable. This study has explained variation in the means of random parameters for a road attribute using the effect of a geometric variable, in which several stakeholders are primarily interested.

Configurations of underreported cyclist-motorised vehicle and single cyclist collisions: Analysis of a self-reported survey

Lower severity cycling collisions, and single cyclist collisions (or single bicycle crashes) are significantly underreported in police statistics, introducing biases into the types of collisions that are available for analysis. Furthermore, many lower severity collisions do not appear in other collision data sources (e.g. hospital and insurance data). This in turn affects priorities for cyclist safety and puts an underemphasis on certain collision types. Due to an absence of data, little is known of the configurations of unreported collisions. In this paper, data from a recent self-reporting survey of cycling collisions in Ireland is used to provide details of cyclist collisions with motorised vehicles and single cyclist collisions, with the inclusion of unreported collision types. Pre-crash scenarios and impact configurations for cyclist collisions with bonnet-type vehicles, and collision factors and fall types for single cyclist collisions are coded. Injury patterns and police underreporting levels are compared, and representative collision scenarios are identified.This study highlights the relative importance of collisions resulting from the cyclist and vehicle travelling in the same direction, specifically, nearside-hook, vehicle lane changing, and overtaking manoeuvres are emphasised. Furthermore, cases involving the cyclist struck from the side by vehicle fronts comprise a smaller share than previous studies. Specifically, side to side impacts, impacts between the front of the cyclist/bicycle and the side of the vehicle, and impacts with open(ing) doors emerge as important impact configurations with the inclusion of self-reported cases. For single cyclist collisions, the importance of loss of traction of the tyres due to slippery road conditions and interactions with tram tracks and kerbs are emphasised. Fall types differ between single cyclist collision scenarios and are related to differences in injury severity.These findings add to existing knowledge for fatal and higher severity collisions, demonstrating that cyclist safety priorities change with inclusion of underreported, and lower severity collisions. The findings are particularly relevant to road infrastructural planners, as well as in the fields of injury biomechanics, and automated vehicle safety (ADAS). Representative scenarios for collisions with bonnet-type vehicles and single cyclist collisions have been identified, allowing for their future inclusion in development of collision and injury prevention strategies. The dataset generated in this study is available from the authors on reasonable request.

Year-round monitoring at a Pacific coastal campus reveals similar winter and spring collision mortality and high vulnerability of the Varied Thrush

Abstract Bird–window collisions are a leading cause of direct anthropogenic avian mortality, yet our state of knowledge regarding this threat relies heavily on eastern North American studies. Seasonal patterns of collision mortality may differ along the Pacific coast, and western North American species remain understudied. We therefore surveyed a stratified random sample of 8 buildings for collisions at the University of British Columbia, Vancouver, Canada over 45-day periods during 2 winters, 1 spring, 1 summer, and 1 fall season between January 22, 2015 and March 15, 2017. After accounting for the rate of scavenging and efficiency of observers in finding carcasses, we estimated that 360 collision fatalities (95% CI: 281–486) occurred over 225 days of collision monitoring. Collision mortality was highest in fall, but in contrast to most published research, collision mortality was intermediate in both winter and spring and was lowest in summer. In winter 2017, we performed point-count surveys to assess whether individual species are disproportionately vulnerable to collisions when accounting for population size and found that the Varied Thrush (Ixoreus naevius) was 76.9 times more likely to collide with buildings, relative to average species vulnerability in winter. To our knowledge, this is the first study to report the Varied Thrush as a species that is disproportionately vulnerable to collisions. Further studies are needed to assess the vulnerability of Western North American species and subspecies, and to determine whether similar patterns of seasonal collision mortality are found elsewhere.

Drivers of fatal bird collisions in an urban center

Millions of nocturnally migrating birds die each year from collisions with built structures, especially brightly illuminated buildings and communication towers. Reducing this source of mortality requires knowledge of important behavioral, meteorological, and anthropogenic factors, yet we lack an understanding of the interacting roles of migration, artificial lighting, and weather conditions in causing fatal bird collisions. Using two decades of collision surveys and concurrent weather and migration measures, we model numbers of collisions occurring at a large urban building in Chicago. We find that the magnitude of nocturnal bird migration, building light output, and wind conditions are the most important predictors of fatal collisions. The greatest mortality occurred when the building was brightly lit during large nocturnal migration events and when winds concentrated birds along the Chicago lakeshore. We estimate that halving lighted window area decreases collision counts by 11× in spring and 6× in fall. Bird mortality could be reduced by ∼60% at this site by decreasing lighted window area to minimum levels historically recorded. Our study provides strong support for a relationship between nocturnal migration magnitude and urban bird mortality, mediated by light pollution and local atmospheric conditions. Although our research focuses on a single site, our findings have global implications for reducing or eliminating a critically important cause of bird mortality.

Identifying modifiable factors related to novice adolescent driver fault in motor vehicle collisions

Objective Novice adolescent drivers have a higher propensity to engage in risky driving and are at higher odds of being involved in collisions. Graduated driver licensing programs have been instituted to help novice drivers gain experience while avoiding higher risk driving circumstances. This study examines modifiable risk factors contributing to novice adolescent driver fault in collisions. Methods Police traffic collision report data from municipalities in Alberta for the years 2010–2016, inclusive, were used. Fault in collision was assigned using an automated and previously validated tool for assigning culpability. Factors contributing to novice adolescent (16-19 years of age) fault in collision were examined using multivariable logistic regression. Results Novice adolescent drivers had higher adjusted odds ratios (aOR) of being at-fault in collision when driving from 01:00-05:00 (aOR = 1.38; 95% Confidence Interval [CI]: 1.26-1.50). Novice adolescent drivers had lower odds of fault when driving with an adult (aOR= 0.62; 95% CI: 0.57-0.68) or a single peer (aOR= 0.87; 95% CI: 0.80-0.94), but higher odds of causing a severe collision with a single peer present (aOR= 2.23; 95% CI: 1.21-4.11). Impairment of the teen driver was reported in 25% of all fatal collisions, and 40% of late-night fatal collisions. Conclusions The findings support policies that allow driving with a single adult or peer passenger during daytime hours. Driving during late-night hours should be restricted for novice adolescent drivers.

Pedestrian deaths and large vehicles

Traffic fatalities in the US have been rising among pedestrians even as they fall among motorists. Contemporaneously, the US has undergone a significant shift in consumer preferences for motor vehicles, with larger Sport Utility Vehicles comprising an increased market share. Larger vehicles may pose a risk to pedestrians, increasing the severity of collisions. I use data covering all fatal vehicle collisions in the US and exploit heterogeneity in changing vehicle fleets across metros for identification. Between 2000 and 2019, I estimate that replacing the growth in Sport Utility Vehicles with cars would have averted 1,100 pedestrian deaths. I find no evidence that the shift towards larger vehicles improved aggregate motorist safety.

Fatal train accidents on Europe’s railways: An update to 2019

This paper presents an analysis of fatal train accident rates and trends on Europe’s main line railways from 1990–2019. It is a sequel to the paper Fatal train accidents on Europe’s railways: 1980–2009 (Evans (2011), which covers the three decades 1980–2009. The present paper discards the data for the 1980s, but adds the data for 2010−2019. The data cover the 28 countries of the European Union as in 2019, together with Norway and Switzerland. The source of the recent data is largely the European Union Agency for Railways.The estimated overall trend in the number of fatal train collisions and derailments per train-kilometre was –5.6 % per year from 1990–2019, with a 95 % confidence interval of –7.1 % to -4.2 %. The estimated accident rate in 2019 was 0.85 fatal collisions or derailments per billion train-kilometres, which represents a fall of 78 % since 1990. This gives an estimated mean number of fatal accidents in Europe in 2019 of 3.89. The data and results for 2010−2019 closely match the extrapolation of the results for 1990−2009, so that in 2009 extrapolation would have given a good forward projection for 2019. By the same argument this paper gives a forward projection of the mean number of accidents in 2029 of 2.12, assuming no change in train-kilometres, or pro-rata changes with changes in train-kilometres.The paper investigates the causes of accidents. A notable finding is that the proportion of accidents caused by signals passed at danger (SPADs) fell from 40 % in 1990−1999 to 21 % in 2010−2019. This is probably due to the increasing deployment of train protection systems.The number of fatalities in individual accidents has a skew distribution: most accidents have a small number of fatalities, but a few have a large number. The overall observed number of fatalities per accident is 4.23, and there is no indication that this mean changes with time. This implies that the mean number of fatalities per year has the same downward trend of 5.6 % per year as the mean number of accidents per year.

Multi-scale temporal variation in bird-window collisions in the central United States

Expansion of urbanization and infrastructure associated with human activities has numerous impacts on wildlife including causing wildlife-structure collisions. Collisions with building windows represent a top bird mortality source, but a lack of research into timing of these collisions hampers efforts to predict them and mitigate effects on avian populations. In Stillwater, Oklahoma, USA, we investigated patterns of bird-window collisions at multiple temporal scales, from within-day to monthly and seasonal variation. We found that collisions peaked during overnight and early morning hours, a pattern that was consistent across seasons. Further, temporal variation in fatal collisions was explained by an interaction between season and avian residency status. This interaction illustrated the expected pattern that more migrant individuals than residents collided in fall, but we also documented unexpected patterns. For example, the highest monthly total of collisions occurred in spring migration during May. We also found similarly high numbers of resident and migrant collisions in spring, and a roughly similar amount of migrant mortality in spring and fall migration. These findings, which provide unprecedented quantitative information regarding temporal variation in bird-window collisions, have important implications for understanding mechanisms by which birds collide and improving timing of measures to reduce this major bird mortality source.

Prediction model for autopsy diagnosis of driver and front passenger in fatal road traffic collisions

Road traffic collisions (RTC) analysis is almost a daily activity in many autopsy room. Especially when analyzing an RTC with multiple occupants in the car, it can be necessary to distinguish driver from front seat passenger in order to provide the judicial authority with elements useful to understand and to prove who was driving, considering the criminal and civil responsabilities that may derive from it. Despite this, it is beyond doubt that there is enormous difficulty in providing such information. The aim of this paper is then to evaluate whether it is possible to differentiate driver and front seat passenger in case of fatal collisions using a mathematical model based on injury pattern alone. Autopsy reports concerning 90 drivers and 60 front-seat passengers were analyzed. Statistical analysis was used to detect injuries capable of discriminating between driver and passenger, considering skin, skeletal and visceral injuries. Results show that certain skin injuries, fractures and internal organ lesions are possibly associated with drivers and front seat passenger status and the overall injury pattern seems to be able to provide useful information. A mathematical model is presented. The process to distinguishing driver from front seat passenger following fatal motor vehicle collision may use multiple sources of information, including autopsy injury pattern analysis.

Motor Vehicle Collisions during Adolescence: The Role of Alexithymic Traits and Defense Strategies.

International literature has shown that adolescents represent the population most at risk of fatal and nonfatal motor vehicle collisions (MVCs). Adolescents’ alexithymic traits and significant use of immature defense strategies have been seen to play a key role. This study aimed to investigate the possible mediation role played by defense strategies use in the relationship between alexithymia and MVCs. Our sample consisted of 297 adolescents divided into four subgroups, based on the number of visits to the emergency department due to an MVC. We assessed adolescents’ alexithymic traits and defense strategies use through self-report instruments. Results showed that males reported a higher rate of MVCs than females. Higher rates of MVCs are associated with more alexithymic traits and maladaptive defense strategies use. Adolescents’ Acting Out and Omnipotence use significantly mediated the relationship between alexithymia and MVCs. Our findings suggest the recidivism of MVCs as an attempt to cope with emotional difficulties, with important clinical implications.

Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway.

Detecting high-collision-concentration locations based solely on collision frequency may produce different results compared to those considering the severities of the collisions. In particular, it can lead government agencies focusing sites with a high collision frequency while neglecting those with a lower collision frequency but a higher percentage of injury and fatal collisions. This study developed systematic ways of detecting reproducible fatal collision locations (R) using the naïve Bayes approach and a continuous risk profile (CRP) that estimates the true collision risk by filtering out random noise in the data. The posterior probability of fatal collisions being reproducible at a location is estimated by the relationship between the spatial distribution of fatal-collision locations (i.e., likelihood) and the CRP (i.e., prior probability). The proposed method can be used to detect sites with the highest proxy measure of the posterior probability (PMP) of observing R. An empirical evaluation using 5-year traffic collision data from six routes in California shows that detecting R based on the PMP outperform those based on the SPF-based approaches or random selection, regardless of various conditions and parameters of the proposed method. This method only requires traffic collision and annual traffic volume data to estimate PMP that prioritize sites being R and the PMPs can be compared across multiple routes. Therefore, it helps government agencies prioritizing sites of multiple routes where the number of fatal collisions can be reduced, thus help them to save lives with limited resources of data collection.

Intuition, the Accimap, and the question “why?” Identifying and classifying higher‐order factors contributing to road traffic collisions

AbstractAlthough the physical and environmental factors contributing to road traffic collisions are often the most straightforward to identify, they may not necessarily be the most useful in terms of guiding road safety intervention design. From a sociotechnical systems perspective, it is the higher system factors that, if addressed, would provide the widest‐reaching benefits to system outcomes. That said, the identification of these higher‐order factors often requires a level of intuition or imagination; they are not always objectively obvious given the facts of a particular case. Here, we argue for the use of the Accimap approach to accident (or collision) analysis, supplemented with a modified version of the five whys causal analysis technique, for the support and structuring of this intuiting process. We use an analysis of a fatal motorcycle collision to illustrate how coroners in the United Kingdom could be supported in their task of identifying and summarizing factors in accordance with the levels of the Accimap, and according to a categorization scheme indicating how abstract those factors are (i.e., immediate, proximal, or distal). Our analysis identified 21 immediate, 12 proximal, and 33 distal factors contributing to the collision. It is these distal factors that are most likely to lead to longer‐term road safety improvements.