Crash Types Research Articles

Evaluation of a Novel Emergency Braking Task on a Driving Simulator with Haptic Anti-Lock Braking System Feedback

Rear-end crashes are one of the leading types of crashes today. The anti-lock braking system (ABS) was designed to assist drivers during emergency braking situations by preventing wheel lock up, allowing drivers to retain control of the vehicle. Not all drivers have knowledge and experience with ABS, especially the haptic brake pedal feedback produced by ABS. There studies introduce a new method to train ABS using an emergency braking task on a simulator and an interactive exercise, Pedals Emergency Stop©. The interactive exercise was designed to prompt participants to press the brake pedal in a motion that is consistent with emergency braking and experience haptic ABS feedback. Participants that depressed the brake pedal quickly to the maximum travel and held that pedal position “passed” the trial. In Study 1, participants (N = 63) completed 15 trials, where it took on average three trials to “pass” for the first time, and 85% of the participants experienced their first “pass” within the first four trials. There were no differences observed between participants with previous knowledge and experience feeling ABS versus those who did not have prior knowledge and experience with ABS. The participants in this study thought that they had enough trials, that the exercise was a practical tool, and recommended the task for new drivers and refresher training, as well as for evaluating fitness to drive. Suggested improvements included a practice followed by three tests, all with four trials each; passing criteria of three out of four trials; and modifications to the feedback presented immediately after completing each trail. Study 2 was conducted (N = 38) to investigate the proposed “passing” criteria, where 95% passed on Test 1. The results revealed that the criteria to pass was reasonable. Future research should examine how the Pedals Emergency Stop© interactive exercise generalizes to on-road driving.

Dynamic All-Red Extension: An Innovative Safety Countermeasure to Treat Red Light Running Crashes

The purpose of this project was to evaluate the safety effectiveness of Dynamic All-Red Extension (DARE) systems. North Carolina Department of Transportation has used DARE to combat red light running (RLR) crashes on major road approaches of selected rural, isolated traffic signals since 2011. Sixteen intersections were included in the before–after analysis. DARE systems in this study were implemented to target a specific crash type: frontal impact crashes (angle, left turn, right turn, head on) caused by vehicles traveling through the intersection and running the red light. Although there was some variability in the magnitude and significance of the results among treatment groups, there was a general reduction in the frequency of target RLR crashes. The crash modification factor (CMF) using empirical Bayes (EB) methodology was 0.93 (7% reduction in target RLR crashes) for all 16 treatment sites; for seven multilane at two-lane sites where DARE was the sole treatment, CMF was 0.65 (35% reduction in target RLR crashes), which was statistically significant at the 95% confidence level. The benefit–cost ratio of the treatment was 143:1, realized from low installation cost and the high cost of RLR crashes. The treatment appeared promising as a systemic safety treatment applied to isolated intersections with higher speed limits, especially for intersections with multilane major roads. Safety professionals should consider adding this safety countermeasure to the limited list of tools available to treat RLR crashes. DARE was inexpensive, demonstrated safety benefits, and caused minimal delays in rural applications without creating driver habituation.

Tabu-Search-Based Combinatorial Subset Selection Approach to Support Investigation of Built Environment and Traffic Safety Relationship

Traffic crashes are a leading cause of death globally, with an increasing rate in urban areas. Thus, this study focuses on the relationship between built environment (BE) and traffic safety (TS), by constructing a relationship model using BE variables. The aim of this paper is to determine the best subset of BE variables through a generalizable methodology. The BE is operationalized through the D-classification (e.g., density, diversity, and design), and various datasets are collected from different agencies. TS is operationalized through motor vehicle involved (MOT) and vulnerable road user (VRU)-involved crash frequencies at the zonal level. A preliminary GIS-based process is conducted to associate the crash data at the census block group (BG) level, followed by examining the BE-TS relationships through a series of negative binomial models optimized for subset selection. The model generation is performed automatically by an embedded Tabu Search procedure. Two case studies are presented: a single-county case (Leon County, Florida, U.S.) and a tri-county case (Miami-Dade, Broward, and Palm Beach Counties, Florida, U.S.). Results show that some BE variables such as total population, age of housing stock, number of bus stops, and traffic volume have consistently positive relationships with crash occurrences. In contrast, several factors show varying effects by crash type or location. For example, motorized mode percentage has a negative relation with crash occurrences in the single-county case whereas it is insignificant for the tri-county case where the non-motorized mode percentage has a positive effect on crash occurrences.

Motorcycle curve assist: A novel approach based on active speed control for crash injury reduction

Objective Safely negotiating curves with a powered-two-wheeler (PTW) requires a high level of skill, and a significant proportion of PTW crashes have a curve involvement. This study aimed to estimate the applicability, potential benefits and feasibility of novel Motorcycle Curve Assist (MCA). The system is designed to operate an emergency control of the speed of a motorcycle approaching a bend at an inappropriate speed. Methods First, the MCA system intervention was defined. Second, the applicability of the system and an estimate of its potential benefits was performed based on a PTW crash database. Motorcyclists’ injury risk estimates, MCA working parameters and timing of intervention were employed to estimate the potential injury reduction of applicable crash types. Third, a field test campaign involving 29 common riders as participants was conducted to investigate the real-world applicability and acceptability among end-users of the system deployment in one relevant riding condition adopting a range of parameters of intervention. Results In the crash database, 23% of cases had curve involvement and after detailed analysis, 14% resulted to be suitable for MCA (60% of cases with curve involvement). The potential relative injury risk reduction considering only the benefits due to crash speed reduction ranged from 3–9% for MAIS2+ to 9–27% for MAIS3+ injuries. Field tests were performed in corners approached at an average speed of 28.7 km/h and an average lean angle of 20°. The system provided a mean deceleration of 0.33 g reached with a fade-in jerk of 1.73 g/s, for an average total duration of 0.59 s. For the field test component, participants reported good controllability of the system, with no incipient loss of control recorded nor reported by participants. Conclusions The proposed approach for MCA implementation showed considerable potential benefits in terms of injury reduction. The intervention with the defined working parameters was considered feasible by a sample of end-users. When integrated with an intervention logic capable of predicting emergency situations while approaching curves, MCA will be a technology capable of assisting PTW riders in conditions where other available active safety systems do not.

Relationship of pedestrian crash types and passenger vehicle types

Introduction: This is the first known study that examines the association between common pedestrian crash types and passenger vehicle types. Method: The analysis included single-vehicle, single-pedestrian crashes from two data sets: North Carolina state crash data and the Fatality Analysis Reporting System (FARS). We performed separate multinomial logistic regression analyses of major pedestrian crash types occurring at or near intersections and at nonintersections. Results: At or near intersections, minivans, large vans, pickups, and SUVs (collectively known as light truck vehicles, or LTVs) were more likely than cars to be involved in crossing-roadway–vehicle-turning-left crashes versus crossing-roadway–vehicle-not-turning crashes. LTVs were also more likely involved in fatal crossing-roadway–vehicle-turning-right crashes at or near intersections versus crossing-roadway–vehicle-not-turning crashes when compared with cars. At nonintersections, LTVs were associated with increased odds of walking-along-roadway crashes relative to crossing-roadway–vehicle-not-turning crashes when compared with cars. Conclusions: LTVs were more likely to be involved in certain pedestrian crash types, implying a potentially problematic visibility of pedestrians near the front corners of these vehicles. Practical applications: More research is needed to examine A-pillar blind zones by vehicle type. If it is found that LTVs have larger blind zones, automakers should consider ways to design the A-pillars of these vehicles to minimize blind zones while maintaining pillar strength. Doing this could improve pedestrian safety around these increasingly popular larger vehicles.

Machine learning based real-time prediction of freeway crash risk using crowdsourced probe vehicle data

Real-time prediction of crash risk can support traffic incident management by generating critical information for practitioners to allocate resources for responding to anticipated traffic crashes proactively. Unlike previous studies using archived traffic data covering a limited highway environment such as a segment or corridor, this study uses a statewide live traffic database from HERE to develop real-time traffic crash prediction models. This database provides crowdsourced probe vehicle data that are high-resolution real-time traffic speed for the entire freeway network (nearly 2,000 miles) in Alabama. This study aims to use machine learning models to predict crash risk on freeways according to pre-crash traffic dynamics (e.g., mean speed, speed reduction) along with static freeway attributes. Traffic speed characteristics were extracted from the HERE database for both pre-crash and crash-free traffic conditions. Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were developed and compared. Separate models were estimated for three major crash types: single-vehicle, rear-end, and sideswipe crashes. The model prediction accuracy indicated that the RF models outperform other models. Models for rear-end crashes are found to have greater accuracy than other models, which implies that rear-end crashes have a significant relationship with pre-crash traffic dynamics and are more predictable. The traffic speed factors that are ranked high in terms of feature importance are the speed variance and speed reduction prior to crashes. According to partial dependence plots, the rear-end crash risk is positively related to the speed variance and speed reductions. More results are discussed in the paper.

Characterizing Incident Responder Crashes Involving Move Over Law Violations

This research sought to understand secondary crashes involving incident responders in which a citation was issued for violation of Florida’s Move Over Law. From 2011 to 2020, there were 519 crashes involving a Move Over Law citation, for which the circumstances of the crashes had required a driver to slow or change lanes because of a stopped responder vehicle. The majority occurred during times other than daylight, and driver distraction and alcohol were notably present in the data. Alcohol increased crash and injury severity and the likelihood of injury. Law enforcement traffic stops were the most common precipitating activity, followed by previous crashes and vehicle disablements. Over two-thirds of crashes involved a law enforcement vehicle, the remainder comprised other responder vehicle types. A distribution of posted speed limits showed that more than half of move over violation crashes occurred on roadways with a posted speed of 45 mph or less. In 41 crashes, a pedestrian was struck, including 32 involving incident responders. In responder struck-by crashes, law enforcement officers, lack of high-visibility safety apparel, and operating on the traffic side of incident scenes were prominent. Local roadways comprised a significant number of struck-by crashes, but higher-speed roadways were more dangerous for towing operators and had higher incidence of serious injuries. The move over violation approach to filtering crash data for responder-involved incidents was valid, however, not every crash involving a responder resulted in a move over citation, so the approach was more representative of the responder-involved crash type than comprehensive.

Exploring Relationships between Months and Different Crash Types on Mountainous Freeways Using a Combined Modeling Approach

Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions.

On the evaluation of visual nudges to promote safe cycling: Can we encourage lower speeds at intersections?

Objective Crashes between cars and cyclists at urban intersections are common, and their consequences are often severe. Typical causes for this type of crashes included the excessive speed of the cyclist as well as car drivers failing to see the cyclist. Measures that decrease the cyclists’ speed may lead to safer car-cyclist interactions. This study aimed to investigate the extent to which cyclists may approach intersections at a lower speed when nudged to do so. Methods Visual flat-stripe nudges were placed on bicycle lanes in the proximity of uncontrolled intersections (with a history of car-cyclist crashes) in two locations in Gothenburg, Sweden. This specific nudge was the one obtaining the best results from a previous study that tested different nudges in controlled experiments. Video data from the intersections were recorded with a site-based video recording system both before (baseline), and after (treatment), the nudge was installed. The video data was processed to extract trajectory and speed for cyclists. The baseline and treatment periods were equivalent in terms of day of the week, light, and weather conditions. Furthermore, two treatment periods were recorded to capture the effect of the nudge over time in one of the locations. Results Leisure cyclists showed lower speeds in treatment than in baseline for both locations. Commuters were less affected by the nudge than leisure cyclists. This study shows that visual nudges to decrease cyclist speed at intersections are hard to evaluate in the wild because of the many confounders. We also found that the effect of visual nudges may be smaller than the effect of environmental factors such as wind and demographics, making their evaluation even harder. Conclusions The observed effect of speed might not be very high, but the advantage both in terms of cyclist acceptance and monetary cost makes an investment in the measure very low risk. This study informs policymakers and road authorities that want to promote countermeasures to intersection crashes and improve the safety of cyclists at urban intersections.

Severity modeling of work zone crashes in New Jersey using machine learning models

In the United States, the probability of work zone crashes has increased due to an increase in renovation works by transportation infrastructures. The severity of work zone crashes is associated with multiple contributing factors such as the roadway’s geometric design features, temporal variables, environmental conditions, types of vehicles, and driver behaviors. For this study, we acquired and analyzed three years (2016–2018) of work zone crash data from the state of New Jersey. We investigated the performance of several machine learning methods, including Support Vector Machine, Random Forest, Catboost, Light GBM, and XGBoost to predict the type of injury severity resulting from work zone crashes. To evaluate models’ performances, some statistical evaluation parameters such as accuracy, precision, and recall scores were calculated. In addition, a sensitivity analysis was conducted to assess the impact of the most influential factors in work zone-related crashes. Random Forest and Catboost outperformed the other models in terms of predicting fatal, major, and minor injuries. According to the sensitivity analysis, crash type and speed limit were the most significantly associated variables with crash severity. The findings of this study are expected to facilitate the identification of appropriate countermeasures for reducing the severity of work zone crashes.

Factors influencing injury severity to belted drivers in car crashes in India

Objective The 2019 revised Indian seat belt regulation resulted in a significant increase in driver belt use. This study examines the factors influencing Maximum Abbreviated Injury Scale (MAIS) 2+ injuries to belted car drivers using Indian crash data. Methods Crash data obtained from Road Accident Sampling System–India (RASSI; 2011 to 2020) were used. Analysis was performed on a sample of 662 belted car drivers, representing 1.7 million drivers nationally. Survey logistic regression was used to identify factors influencing the odds of MAIS 2+ injuries to belted car drivers. Factors including delta-V, mass ratio (striking vehicle/struck vehicle), crash type, and driver age and gender were examined. The model fits and the statistical significance of each factor included in the models were examined. In addition, clinical review of crashes with belted drivers sustaining MAIS 3+ injury is presented. Results Delta-V was the most significant factor (odds ratio = 12%; 95% confidence interval 6%–19%) influencing belted driver injuries. Older drivers (55+ years) have a higher odds of MAIS 2+ injury. By adding mass ratio to the model, the odds ratio for delta-V increased to 1.16. If the mass ratio is 2.5 (striking vehicle weight = 2.5 times struck vehicle weight), then the odds of MAIS 2+ injury for the struck vehicle driver doubles. The average Delta-V for MAIS 2+ crashes is 34 mph and injury risk increases significantly when delta-V approaches 30 mph. Seventy percent of crashes with injured drivers had a mass ratio >2. For drivers with minor/no injuries, 88% of crashes had a mass ratio <2. Review of MAIS 3+ injury crashes showed that these crashes had a high delta-V (38 mph) and high mass ratio (5). Sixty percent of belted drivers sustained MAIS 3+ head or chest injury resulting from reduction in survival space. About 37% of AIS 3+ head injuries were subdural hemorrhages and 20% were crush injury to head. AIS 3+ chest injuries were rib fractures (40%) and lung lacerations/ruptures (27%). Conclusion Delta-V and mass ratio are significant factors of belted driver injuries in India. Serious injuries are mainly to the head and chest. Increased rear underrun protection device (RUPD) fitment rate, improved airbag and RUPD design, safe speed enforcement, and improved road infrastructure will reduce injury severity.

Caught in the blind spot of a truck: A choice model on driver glance behavior towards cyclists at intersections

Vulnerable road users (VRUs) constitute an increasing proportion of the annual road fatalities across Europe. One of the crash types involved in these fatalities are blind spot crashes between trucks and bicyclists. Despite the presence of mandatory blind spot mirrors, truck drivers are often reported to have overlooked the presence of a bicyclist. This raises the question if and when truck drivers check their blind spot mirrors for the presence of bicyclists, and which factors contribute to such glance behavior. The current study presents the results of an analysis of naturalistic glance behavior by 39 truck drivers in 1,903 right-turning maneuvers at urban intersections, where in each maneuver there was a chance of crossing the path of a bicyclist. The descriptive analysis revealed that most often truck drivers did not cast a glance upon their blind spot mirrors as recommended by the examination guidelines. Furthermore, a choice model was developed with the main factors that have an impact on glance behavior. Drivers were more likely to glance with a priority regulation that allowed conflicts, with lower speed limits, with zebra crossings, without cyclist facilities, without a lead vehicle making the same maneuver, in presence of VRUs, without adverse sight conditions, in lower age groups, without certain non-driving related activities, when driving a truck with more direct vision on VRUs, and without a camera providing a view on the blind spot, and with less time between a standstill and starting the maneuver. Three factors did not significantly improve the choice model and were therefore left out, despite showing significant effects in bivariate tests: intersection layout (e.g., three vs. four legs), presence of advanced stopping lanes, and visual obstruction. Implications of the choice model are discussed for driver education (in terms of timely glances, reducing inattention, and hazard anticipation) and vehicle design (in terms of direct vision).

Exploring the Determinants of School Bus Crash Severity

Although the school bus is considered a safe form of transportation, school bus crashes are a major safety concern. School bus crashes are a result of driver error of either the at-fault school bus or another at-fault vehicle (where the school bus is not at fault). To examine the risk factors associated with school bus related crashes and crash outcomes, this study segments and develops two binary logit models for each school bus crash type. A total of 1702 school bus related crashes recorded between 2009 and 2016 were used to estimate the model. According to the model results, sideswipe collisions were less likely to result in injury outcomes for at-fault school buses than rear-end and side-impact collisions. Speeding, driving impaired or under the influence, and negotiating a curve all have significant positive associations with injury outcomes in not-at-fault school bus crashes. This study’s practical implications include enforcing the school bus safe driving guidelines, training programs for school bus drivers that include elements of nonroutine trips, training for crossing guards, and awareness programs for drivers of other vehicles to instill safe driving practices around school buses.

Investigation of Inlaid Pavement Marker Performance and Safety Effectiveness

The objectives of this study were to assess inlaid pavement marker (IPM) performance and estimate their safety effectiveness in St. Louis, Missouri. IPM performance was evaluated through a count of marker presence and a feedback survey from participants who viewed dry and wet night videos of IPM sections in the St Louis area. The safety effectiveness of IPMs was evaluated using a rigorous state-of-the-art empirical Bayes (EB) before–after crash analysis. The marker presence assessment, unfortunately, did not provide any conclusive trends in IPM performance. Newer IPM sections had higher percentages of missing markers than older sections, and there were too many unknown variables to determine the source of the unexpected differences. The unanimous conclusion of the nighttime video visibility survey was that drivers and passengers traveling on a wet night feel that IPMs are very important to the visibility of the roadway’s lane lines. For the EB analysis, installation and reference site data were used to examine the effects for specific crash types, including total, fatal and injury, wet pavement, nighttime, nighttime wet pavement, lane departure, wet pavement lane departure, and nighttime lane departure. Based on the aggregate results, IPMs, when installed with pavement resurfacing, significantly reduce all crash types examined.

로지스틱회귀모형을 이용한 위험물수송차량 교통사고확률 분석 연구

Hazardous materials transport vehicle crashes on highways occur steadily with an average of 30 cases per year, and leakage of Hazardous materials due to traffic accident causes enormous socioeconomic losses. Studies related to truck safety have been conducted for a long time in Korea, but studies on traffic accidents and influencing factors of hazardous materials transport vehicles have not received attention from researchers. Therefore, this study aims to predict the cause of hazardous materials transport vehicle accidents on highways and the probability of accidents. For this purpose, the highway crash dataset from 2007 to 2020 provided to the Korea Expressway Corporation was collected, and we extracted hazardous materials transport vehicle accidents from the dataset. The logistic regression model was applied for predicting crash probability by selecting four types of crashes, such as fatal and injury, human factor, night-time, and ramp section. As a result of this study, fatal and injury crashes of hazardous materials transport vehicles were derived the most from driver

Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis

Purpose This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs. Design/methodology/approach The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously. Findings The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability. Originality/value The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

Injury severity assessment of rear-end crashes via approaches based on generalized estimating equations

Rear-end crashes constitute the predominant type of crashes on highways and may lead to severe injuries and high property damage. Available statistical models primarily focus on injury severity and analyze potential factors that affect it. However, rear-end crashes may also be potentially correlated to vehicle, roadway, environmental, temporal, spatial, traffic, and crash characteristics. Additionally, unobserved heterogeneity regarding the effects may be present, which may be different in different crashes. In this context, multiple generalized estimating equation (GEE)-based models, developed using different working matrices and distributions, are proposed in this study to examine factors that affect injury severity. The proposed models account for both crash-related correlations and unobserved heterogeneity, thereby outperforming traditional models in terms of prediction accuracy. Among the explanatory variables considered in this study, the passenger car, minibus, curvature ratio, rainy weather, foggy weather, early morning, Thursday, autumn, winter, and average annual daily traffic volume were identified as contributing factors. However, significant differences were observed between the elasticity effects measured by different models, especially in terms of minibus and foggy weather. Thus, this study verifies that GEE-based models account for a greater amount of unobserved heterogeneity, yield better performance in terms of precision, and exhibit more consistent explanatory power compared to traditional models.

Deep Learning Model for Crash Injury Severity Analysis Using Shapley Additive Explanation Values

Analysis of traffic crash and associated data provides insights and assists with identification of cause-and-effect relationships with crash probabilities and outcomes. This study utilized eight years of police-reported Nebraska crash data using a deep neural network (DNN) to model crash injury severity outcomes. Prediction performances and model interpretability were examined. The developed DNN excelled in prediction accuracy, precision, and recall but was computationally intensive compared with a baseline multinomial logistic regression model. While the lack of interpretability power of deep learning models limits their usage, the adoption of SHapley Additive exPlanation (SHAP) values was an improvement. Conclusions drawn from the DNN model are generally consistent with the estimated baseline model. For instance, the variable total number of pedestrians was found significant in both scenarios of the multinomial logit model indicating a strong relationship with more severe crash injury outcomes. It was also found important in all three sets of parameters in DNN. SHAP values also allow in-depth analysis of prediction results on a single observation, such as the variable crash type (same direction sideswipe) contributing to classifying a single observation as property damage only. These findings are beneficial for making more informed transportation safety-related decisions.

Involvement of Road Users from the Productive Age Group in Traffic Crashes in Saudi Arabia: An Investigative Study Using Statistical and Machine Learning Techniques

Road traffic crashes (RTCs) are a major problem for authorities and governments worldwide. They incur losses of property, human lives, and productivity. The involvement of teenage drivers and road users is alarmingly prevalent in RTCs since traffic injuries unduly impact the working-age group (15–44 years). Therefore, research on young people’s engagement in RTCs is vital due to its relevance and widespread frequency. Thus, this study focused on evaluating the factors that influence the frequency and severity of RTCs involving adolescent road users aged 15 to 44 in fatal and significant injury RTCs in Al-Ahsa, Saudi Arabia. In this study, firstly, descriptive analyses were performed to justify the target age group analysis. Then, prediction models employing logistic regression and CART were created to study the RTC characteristics impacting the target age group participation in RTCs. The most commonly observed types of crashes are vehicle collisions, followed by multiple-vehicle and pedestrian crashes. Despite its low frequency, the study area has a high severity index for RTCs, where 73% of severe RTCs include individuals aged 15 to 44. Crash events with a large number of injured victims and fatalities are more likely to involve people in the target age range, according to logistic regression and CART models. The CART model also suggests that vehicle overturn RTCs involving victims in the target age range are more likely to occur as a result of driver distraction, speeding, not giving way, or rapid turning. As compared with the logistic regression model, the CART model was more convenient and accurate for understanding the trends and predicting the involvement probability of the target age group in RTCs; however, this model requires a higher processing time for its development.

In-Depth Understanding of Near-Crash Events Through Pattern Recognition

Studying near-crashes can help safety researchers understand the nature of crashes from different perspectives. Conventional crash data sets lack information about what occurred directly before the crash event. This study used a near-crash data set extracted from a naturalistic driving study that includes features describing the vehicles, drivers, and information on other vehicles involved before and during the near-crash incidents. This data set provides us with a unique perspective to understand the patterns of near-crashes. This study applied the cluster correspondence analysis (cluster CA) algorithm to explore the patterns and the magnitude of each feature’s dominance within and between the clusters through dimension reduction. The analysis identifies six clusters with four types of near-crashes: near-crash with adjacent vehicles; near-crash with the following or leading vehicles; near-crash with turning vehicles; and near-crash with objects on the roadway. The results also show that the first two types of near-crash are the most common. The patterns for these two most common types of near-crash are different with or without the engagement of secondary tasks. The findings of this study provide a fresh perspective to understand different types of crash and associated patterns. Furthermore, these findings could help transportation agencies or vehicle designers develop a more effective countermeasure to mitigate the risk of collision.