Crash Types Research Articles

An integrated multi-resolution framework for jointly estimating crash type and crash severity

The current research effort contributes to safety literature by developing an integrated framework that allows for the influence of independent variables from crash type and severity components at the disaggregate level to be incorporated within the aggregate level propensity to estimate crash frequency by crash type and severity. The empirical analysis is based on the crash data drawn from the city of Orlando, Florida for the year 2019. The disaggregate level analysis uses 15,518 crash records of three crash types including rear end, angular and sideswipe. Each crash record contains crash specific factors, driver and vehicle factors, roadway attributes, road environmental and weather information. For aggregate level model analysis, the study aggregates the crash records by crash type over 300 traffic analysis zones. An exhaustive set of independent variables including roadway and traffic characteristics, land-use attributes, built environment and sociodemographic factors are considered in this level. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. A validation exercise is also conducted using a holdout sample to highlight the superiority of the proposed integrated model relative to the non-integrated model system. The proposed framework can also incorporate unobserved heterogeneity in the model system. The findings of the study indicate that the proposed framework is advantageous for capturing the variable effects simultaneously across the aggregate and disaggregate levels.

Assessing non-motorist safety in motor vehicle crashes – a copula-based approach to jointly estimate crash location type and injury severity

Non-motorist injury severity can be affected by various observed and unobserved attributes related to the crash location type (segment or intersection). Recognizing the distinct non-motorist injury severity profiles by crash location type, we propose a joint modeling framework to study crash location type and non-motorist injury severity as two dimensions of the severity process. We employ a copula-based joint framework that ties the crash location type (represented as a binary logit model) and injury severity (represented as a generalized ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The proposed approach also accommodates the potential heterogeneity (across non-motorists) in the dependency structure. The data for our analysis is drawn from the Central Florida region for the years of 2015 to 2021. The model system explicitly accounts for temporal heterogeneity across the two dimensions. A comprehensive set of independent variables including non-motorist user characteristics, driver and vehicle characteristics, roadway attributes, weather and environmental factors, temporal and socio-demographic factors are considered for the analysis. We also conducted an elasticity analysis to show the actual magnitude of the independent variables on non-motorist injury severity for the two locations. The results highlight the importance of examining the effect of various independent variables on non-motorist injury severity outcome by crash location type.

Fatal Crash Occurrence Prediction and Pattern Evaluation by Applying Machine Learning Techniques

Background Highway safety remains a significant issue, with road crashes being a leading cause of fatalities and injuries. While several studies have been conducted on crash severity, few have analyzed and predicted specific types of crashes, such as fatal crashes. Identifying the key factors associated with fatal crashes and predicting their occurrence can help develop effective preventative measures. Objective This study intended to develop cluster analysis and ML-based models using crash data to extract the prominent factors behind fatal crash occurrences and analyze the inherent pattern of variables contributing to fatal crashes. Methods Several branches and categories of supervised ML models have been implemented for fatality prediction and their results have been compared. SHAP analysis was conducted using the ML model to explore the contributing factors of fatal crashes. Additionally, the underlying hidden patterns of fatal crashes have been evaluated using K-means clustering, and specific fatal crash scenarios have been extracted. Results The deep neural networks model achieved 85% accuracy in predicting fatal crashes in Kansas. Factors, such as speed limits, nighttime, darker road conditions, two-lane highways, highway interchange areas, motorcycle and tractor-trailer involvement, and head-on collisions were found to be influential. Moreover, the clusters were able to discern certain scenarios of fatal crashes. Conclusion The study can provide a clear image of the important factors related to fatal crashes, which can be utilized to create new safety protocols and countermeasures to reduce fatal crashes. The results from cluster analysis can facilitate transportation professionals with representative scenarios, which will benefit in identifying potential fatal crash conditions.

Safety Performance Functions for Mountain Two-lane Rural Roads in Ecuador

The two-lane rural road on mountainous roads has various design and operational challenges. This complexity could produce a road with specific geometric configurations or segments where the probability of a road accident increases. The Highway Safety Manual (HSM) offers a predictive model to detect such sites using Safety Performance Functions (SPF). These functions predict the number of road crashes at the different roadway elements and apply to all project phases. Many functions or equations have been developed worldwide; however, in Ecuador, they have not yet been estimated. Therefore, this study aims to propose Safety Performance Functions for two-lane rural roads in Ecuador in mountainous environments. To do this, design and operation variables were collected from 25 roads in the Ecuadorian Andes. The SPFs were developed with crash data from 2017-2019 and validated with information from 2020-2021. As a result, 12 SPFs were developed for three types of crashes (total, fatal + injury, property damage only) and various ranges of annual average daily traffic (AADT). These equations estimate the average expected frequency of accidents for existing conditions, alternatives for existing conditions, or proposed new road projects. This study will improve the road safety of the built roads and new projects.

A systematic review and meta-analysis of data linkage between motor vehicle crash and hospital-based datasets

Motor vehicle crash data linkage has emerged as a vital tool to better understand the injury outcomes and the factors contributing to crashes. This systematic review and meta-analysis aims to explore the existing knowledge on data linkage between motor vehicle crashes and hospital-based datasets, summarize and highlight the findings of previous studies, and identify gaps in research. A comprehensive and systematic search of the literature yielded 54 studies for a qualitative analysis, and 35 of which were also considered for a quantitative meta-analysis. Findings highlight a range of viable methodologies for linking datasets, including manual, deterministic, probabilistic, and integrative methods. Designing a linkage method that integrates different algorithms and techniques is more likely to result in higher match rate and fewer errors. Examining the results of the meta-analysis reveals that a wide range of linkage rates were reported. There are several factors beyond the approach that affect the linkage rate including the size and coverage of both datasets and the linkage variables. Gender, age, crash type, and roadway geometry at the crash site were likely to be associated with a record's presence in a linked dataset. Linkage rate alone is not the only important metric and when linkage rate is used as a metric in research, both police and hospital rates should be reported. This study also highlights the importance of examining and accounting for population and bias introduced by linking two datasets.

Traffic Performance of Successive Freeway Merge Segments

At freeway interchanges with high entering flows, implementing two successive merges can improve traffic flow and capacity by distributing the merging maneuvers. The German Guidelines for the Design of Motorways define different types of successive (“double”) merges. According to the quality of service assessment procedure for freeway merge segments given in the German Highway Capacity Manual, successive merge segments can be evaluated separately. However, depending on the distance between the merges, the capacity of the second (downstream) merge might be affected by the entering traffic at the first (upstream) merge, which influences the lane-flow distribution. For a more precise evaluation of traffic flow performance and traffic safety depending on the geometric design characteristics, vehicle interactions, lane changes, the resulting capacity of successive merge segments, accident rates, and crash types were analyzed in the study. The research was based on loop detector data as well as trajectory data obtained from video measurements, which provide an insight into the interactions between mainline and entering vehicles. For the analysis of traffic safety, crash data were evaluated over a period of 3 years. Furthermore, successive merge segments with different geometric and traffic characteristics were modeled with the microscopic traffic simulation tool BABSIM to analyze the influence on traffic flow and capacity. As a result, capacity models and recommendations for the geometric design of successive freeway merge segments are provided.

Effect of emergency medical service response time on fatality risk of freeway crashes: Bayesian random parameters spatial logistic approach.

Emergency medical service (EMS) serves as a pivotal role in linking injured road users to hospitals via offering first aid measures and transportation. This paper aims to investigate the effect of emergency medical service (EMS) response time on the fatality risk of freeway crashes. Crash injury severity data from Kaiyang Freeway, China in 2014 and 2015 are employed for the empirical investigation. A Bayesian random parameters spatial logistic model is developed for analyzing crash severity. Bayesian inference of the random parameters spatial logistic model demonstrates the importance of reducing EMS response time on minimizing the fatality risk of freeway crashes. Fatality odds would increase by 2.6% for 1 min increase in EMS response time. Additionally, vehicle type, crash type, time of day, horizontal curvature, vertical grade, and precipitation are also found to have significant effects on the fatality probability of freeway crashes. It is crucial to reduce EMS response time to decrease the fatality likelihood of freeway crashes. Some countermeasures have been proposed to shorten EMS response time.

Comparing fatal crash risk factors by age and crash type by using machine learning techniques.

This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver's age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

Identification of crash-prone segment on Cipularang toll road

Traffic safety is an important issue in the field of transportation. Toll roads are one of the infrastructures that are prone to crashes, because on toll roads drivers can drive their vehicles at high speeds so there is a high risk of having an crash, as happens every year on Cipularang Toll Road. This research will determine the crash-prone segments on Cipularang Toll Road section using the network screening method. Crash and traffic data for the last five years (2018-2022) were obtained from PT Jasa Marga Purbaleunyi. Identification of crash-prone segments carried out using ten performance indicators contained in HSM (2010), namely average crash frequency, crash rate, critical rate, Equivalent Property Damage Only average crash frequency, relative severity index, excess predicted average crash frequency using method of moment, Level of Service of Safety, excess predicted average crash frequency using SPF, probability of specific crash type exceeding threshold proportion, and expected average crash frequency with Empirical Bayes adjustment. The analysis results show that the five segments with the highest priority for treatment are KM 91-92, KM 92-93, and KM 86-87 (Jakarta-bound direction), KM 120-121 and KM 101-102 (Bandung-bound direction).

Study on design and impact energy absorption of Voronoi porous structure with tunable Poisson's ratio

As the inner core of crash box, the porous structure could cushion impact load through its own deformation. The aims of this study were to propose a series of Voronoi porous structures with tunable Poisson's ratio (VP-VPS) by using the periodic array of unit cells based on Voronoi-Tessellation and to develop crash boxes filled with porous structures for strong crashworthiness and high energy absorption. VP-VPS structures through the periodic arrays of unit cells were constructed according to 4 independent unit cell design factors. The quasi-static and dynamic compressive mechanical responses of VP-VPS structures prepared by selective laser melting (SLM) were studied through the combinations of mechanical test and simulation. Relationships between VP-VPS unit cell design factors and mechanical properties of structures were determined by establishing regression equations using the response surface method (RSM). Finally, the energy absorption of new crash boxes filled with VP-VPS structures was investigated through simulation. Results showed that VP-VPS structures had high load-bearing capacities and energy absorption under quasi-static and dynamic compressive conditions. Regression equations based on RSM could well predict mechanical properties of structures. The mechanical properties of VP-VPS structures were enhanced with the increase of global strain rate, which showed different deformation modes at low and high global strain rates. Compared with typical crash boxes filled with negative Poisson's ratio (NPR) inner cores, new type VP-VPS crash boxes had stronger impact resistance and higher energy absorption. This investigation provided a feasible design solution and theoretical basis for the construction and engineering application of impact resistance protective structures.

A Poisson Lognormal-Lindley model for simultaneous estimation of multiple crash-types: Application of multivariate and pooled univariate models

Challenges addressing overdispersion, unobserved heterogeneity, the preponderance of zeros, and correlation in the dependent variables of crash count models are of significant interest. Accounting for all these data issues simultaneously is few and far between. This study proposes a new mixing distribution model that accounts for overdispersion and the preponderance of zeros in crash count models. The proposed mixing distribution model extends to the multivariate structure to account for correlations between dependent variables and unobserved heterogeneity. The empirical analysis is conducted on crash data of Bruce highway involving single-vehicle and multi-vehicle crash types by “fatal and severe injury” and “moderate and minor injury” severity levels on aggregated data over three analysis years (2016, 2017, and 2018). The study demonstrates superior goodness of fit of the proposed multivariate random parameters Poisson lognormal-Lindley model compared to its restricted models. Moreover, pooling the crash data as repeated measures of crash types helped formulate a pooled-univariate random parameters Poisson-Lindley model to estimate multiple crash types by severity. The results showed the pooled-univariate model offers comparable goodness of fit and averaged marginal effects as the complex multivariate modeling structure. Moreover, the proposed pooled-univariate model reduced the model complexity to a one-dimensional integral and offered more efficient parameter estimates. In the empirical context, the modeling results showed that single-vehicle and multi-vehicle crashes by severity are linked with different causality.

A correlated random parameters ordered probit approach to analyze the injury severity of bicycle-motor vehicle collisions at intersections

Bicycle-motor vehicle (BMV) accidents hold paramount importance due to their substantial impact on public safety. Specifically, road intersections, being critical conflict points, demand focused attention to reduce BMV crashes effectively and mitigate their severity. The existing research on the severity analysis of these crashes appears to have certain gaps that warrant further contribution. To address the mentioned limitations, this study first integrates multiple pre-collision features of the bicycles and vehicles to classify crash types based on the mechanism of the crashes. Then, the correlated random parameters ordered probit (CRPOP) model is employed to examine the factors influencing injury severity among bicyclists involved in intersection BMV crashes in Pennsylvania from 2013 to 2018. To gain deeper insights, this study conducts a separate analysis of crash data from 3-leg intersections, 4-leg intersections, and their combined scenarios, followed by a comparative examination of the results. The findings revealed that the presented crash typing approach yields new insights regarding injury severity outcomes. Moreover, in addition to exhibiting a comparable statistical performance contrasting to the more restricted models, the CRPOP model identified hidden correlations between three random parameters. Furthermore, the study demonstrated that analyzing combined crash data from the two intersection types obscured certain factors that were found significantly influential in the injury outcomes through analyzing sub-grouped data. Consequently, it is recommended to implement tailored countermeasures for each type of intersection.

The effects of Electronic Stability Control (ESC) on fatal crash rates in the United States

Problem: Electronic Stability Control (ESC) is believed to be among the most efficient vehicle safety interventions with reported effects around 50% for fatal single and rollover crashes. However, such estimates have used sample data, which have not controlled for the possibilities of self-selection, behavioral adaptation, increased access to the technology by less safe drivers, and the calculation of effects on very specific categories of crashes. Effects of ESC in the population can therefore be expected to be smaller than is currently believed. Method: National U.S. data for fatal crashes, driving exposure and other control factors, and market penetration of ESC over 1991–2021 were used to calculate whether the trends in fatalities over time in crash rates for singles, rollovers, and fatal crashes in general matched projections from estimates of effectiveness. Results: It was found that downward trends in the relevant crash types were generally present before ESC was introduced, and that the trends thereafter were weaker. Although some trends were consistent with effects of ESC, they were markedly smaller than the projected ones, and could be explained by other factors such as the number of vehicles per capita. At best, the effect for rollovers could be up to two-thirds of previous estimates, no effect was detected for singles, while for all fatal crashes results depended upon the type of analysis performed. These results conflict with conclusions in all published ESC crash sample studies, which have compared vehicles with and without ESC. This discrepancy can be explained by methodological errors in the previous studies using induced exposure methods and self-selected samples. Practical applications: Traffic safety may not be as much improved by technological interventions as believed. Alternative approaches to traffic safety are needed, which do not rely on technology that interferes with driver behavior.

Do Expressway Interchanges Increase Crash Injury Severities? Insights Using Temporal Instability and Unobserved Heterogeneity

Expressway crashes in interchange areas are a critical concern in China, posing significant economic and social challenges. Utilizing three years of crash data from the Beijing–Shanghai Expressway, this study investigates the transferability and heterogeneity of crash characteristics between interchange and noninterchange areas, as well as the temporal shifts in factors influencing injury severity levels. The research employs four series of random parameters logit models to estimate three potential crash injury severity outcomes of severe injury, minor injury, and no injury (based on the most severely injured individual in each crash) and to identify key determinants, encompassing driver, vehicle, roadway, environmental, temporal, traffic, and crash attributes. Likelihood ratio tests and out-of-sample predictions are utilized to assess the temporal stability and transferability of crash area characteristics. Additionally, the marginal effects of various determinants are calculated to understand their influence across different year periods and crash types. Key variables such as overspeed, single-vehicle, AADT (annual average daily traffic volume), Ls min , and other crash type indicators are identified as significant random parameters, demonstrating heterogeneity in means and variances. Notable distinctions are observed between interchange and noninterchange crashes, indicating nontransferability, with most significant indicators revealing temporal instabilities. Furthermore, factors such as multivehicle involvement, buses, and nighttime conditions are identified as risk indicators, notably increasing the likelihood of severe injuries. These insights are invaluable for expressway designers and decision-makers, aiding in understanding the contributing mechanisms of various elements. This study suggests that stricter enforcement measures are crucial to prevent random lane changes, particularly at interchange entrances and exits. Additionally, effective management strategies and enforcement countermeasures should be implemented to mitigate crash injury outcomes in both interchange and noninterchange areas.

Safety impact of highway geometrics and pavement parameters on crashes along mountainous roads

Having the capability of estimating both the number of crashes and their severity levels, crash prediction models are a precious tool in highway safety. However, there hasn't been any research on predicting traffic crashes on Indian mountainous rural highways. The primary objective of this research is to develop safety performance functions (SPFs) for traffic crashes occurring on rural roads located in the mountainous region of Uttarakhand, India.For analysis, the study utilized five years of crash data collected from different types of rural roads. The road network was divided into constant segments of 500m each, and separate models were developed for single (TSVC) and multi-vehicle (TMVC) crashes using the negative binomial regression approach. These SPFs highlight important significant variables in terms of positive and negative association and a potential change in subject crash frequencies. The results concluded that different types of risk factors impact both types of crashes, with horizontal (HC) and vertical curves (VC) in common. For instance, spot speed increases TSVC crashes by 3.87 %, whereas HC and VC tend to increase subject crashes by 8.32 % and 29.95 %, respectively.Similarly, TMVC is influenced by carriageway (CW) and shoulder width (SW). The result proposed that an increase in CW and SW can decrease frequencies by 0.668 times and 0.819, respectively. Additionally, the model highlighted the importance of rut-depth and the presence of pavement markings in the road safety analysis. At last, further research scope is suggested based on the limitations of this study.

Investigating the severity of single-vehicle truck crashes under different crash types using mixed logit models

Introduction: Almost 90% of fatal road crashes occur in developing countries. Among these countries, Iran has a noticeable fatal crash rate of 21.47 deaths per 100,000 persons. Improving the safety of trucks is of particular importance in Iran where road freight is used to transport almost 90% of the commodities. Researchers have suggested dichotomizing crashes into single- and multi-vehicle categories and found that when this is performed vast differences can be identified between the mechanisms behind these categories of crashes, particularly when investigating truck crashes. Method: This study investigated single-vehicle truck crashes in Khorasan Razavi province in Iran from 2013 to 2021. Likelihood ratio tests were employed to show that separate models are statistically valid for different crash types. Subsequently, three mixed logit crash-type models were developed to investigate 5,703 single-vehicle truck crashes. Results: Four significant variables were exclusive to collisions with an object (brake failure, ABS, primary roads, and rainy or snowy weather), five significant variables were associated with run-off-road crashes (driving a loaded truck, speed limit (>60 km/h), paved shoulders, driving uphill, and inability to control the truck), and three significant variables were associated with overturn crashes (overloaded truck, curved roads, and changing direction suddenly). In all crash types, both fastening the seatbelt and speeding were found to be significant factors. Conclusion: The research highlights the need to analyze single-vehicle truck crashes using distinct crash type models and highlights the unique contributing factors of three common single-vehicle crash types. Practical applications: The study presents recommendations for policy to address key crash risks for trucks in Iran, including education and training to improve driver experience, compliance with seat belt usage, enforcement of speeding, and vehicle technologies to monitor drivers.

Exploring differences in injury severity between occupant groups involved in fatal rear-end crashes: a correlated random parameter logit model with mean heterogeneity

ABSTRACT Rear-end crashes are one of the most common crash types. Passenger cars involved in rear-end crashes frequently produce severe outcomes. However, no study investigated the differences in the injury severity of occupant groups when cars are involved as following and leading vehicles in rear-end crashes. Therefore, the focus of this investigation is to compare the key factors affecting the injury severity between the front- and rear-car occupant groups in rear-end crashes. First, data is extracted from the Fatality Analysis Reporting System (FARS) for two types of rear-end crashes, including passenger cars as rear-end and rear-ended vehicles. Significant injury severity difference between front- and rear-car occupant groups is found by conducting likelihood ratio test. Moreover, the front- and rear-car occupant groups are modeled by the correlated random parameter logit model with heterogeneity in means (CRPLHM) and the random parameter logit model with heterogeneity in means (RPLHM), respectively. This study provides an insightful knowledge of mechanism of occupant injury severity in rear-end crashes.

Safety Evaluation of the Combined Effect of Offset Left-Turn Lanes and Flashing-Yellow-Arrow Signals at Signalized Intersections on Multilane Divided Highways in Alabama Using the Empirical Bayes Method

The present study conducted an empirical Bayes (EB) before–after analysis to investigate the combined effects of the offset left-turn lanes and flashing-yellow-arrow (FYA) signals implemented at signalized intersections on multilane, divided highways in Alabama. A total of 35 signalized intersections were selected for the EB safety analysis. Among them, 30 intersections were classified as a reference group and five were classified as a treatment group. The reference group includes intersections which have not undergone any left-turn treatments from the period of 2010 to 2020, while the treatment group includes those improved with offset left-turn lanes and FYA signal implementation concurrently during years in that period. Safety performance functions were developed with data collected at reference-group intersections to predict crashes at such intersections under a no-treatment scenario. A study focus was then given to understanding the change in crash frequency before and after the combined treatments for the treatment-group intersections, using the EB method. Results show that the combined left-turn treatments (i.e., implementing offset left-turn lanes coupled with FYA signals) could reduce different types of crashes effectively. There was a substantial reduction of 27% in total crashes (crash modification factor [CMF] = 0.73), a 43% decrease in left-turn crashes (CMF = 0.57), and a 36% reduction in total injury crashes (CMF = 0.64) after the treatments. These findings were supported by their respective standard errors, which are 0.060, 0.101, and 0.106 for total, left-turn, and total injury crashes, respectively, and all the CMFs are statistically significant at 95% confidence intervals.

Assessing regional road traffic safety in Sweden through dynamic panel data analysis: Influence of the planned innovative policies and the unplanned COVID-19 pandemic

Efficient transport infrastructures are crucial to improve traffic safety and reduce negative effects of human activities, where effective transport policies play a vital role. It is also important to evaluate the effects of the policies to document and update them with time. The purpose of this study is to examine road traffic crashes with fatalities and severe injuries at the county level in Sweden, considering both planned innovative policies and the unexpected influence of the COVID-19 pandemic. Data on road traffic crashes with fatalities and severe injuries from 2010 to 2021 in twenty-one counties in Sweden were collected, as well as road network data, vehicle registration data, land use data and socio-demographic data. Negative binomial autoregressive models with region fixed effect were proposed to estimate the effects of explanatory variables on crash frequency, along with Generalized Method of Moments (GMM) models. The findings demonstrate that the speed limit changes and the introduction of safety cameras in Sweden is effective in reducing fatalities and severe injuries. Importantly, different regions exhibit distinct responses to these interventions, with varying effects on different types of crashes. Additionally, findings show that other factors, such as land use characteristics, presence of trucks, and population density also influence the crash frequencies. Moreover, the effect of the COVID-19 pandemic was investigated, which shows a reduction in traffic crashes during the pandemic period. Important policy implications based on the findings are discussed to enhance transport safety and resilience. By considering regional variations, this study provides insights into the spatial dynamics of road safety patterns, and supports the Vision Zero policy in Sweden.

Investigating the impact of influential factors on crash types for autonomous vehicles at intersections

Autonomous Vehicles (AVs) are being promoted as an emerging technology with the potential to improve traffic efficiency and safety. However, the scarcity of publicly available AV crash data contributes to a limited understanding of safety issues for deploying AVs. To gain a deeper understanding of the factors contributing to AV crashes, this study created a unique dataset by combining 445 intersection AV crashes from the California Department of Motor Vehicles with detailed roadway geometric and traffic characteristic data. To address the issue of imbalanced data, the Borderline synthetic minority oversampling technique (BL-SMOTE) was utilized in this study. A framework for analyzing AV crash types at intersections was also developed using the Categorical gradient boosting (Catboost) and Bayesian networks (BNs) models. The results show that inappropriate vehicle maneuvers, such as sudden lane changes or speed control, can increase the likelihood of an AV being involved in a sideswipe crash. Additionally, in scenarios where the AADT of the major road is lower, AVs are more prone to rear-end crashes when proceeding. These findings can provide valuable insights for creating safety management strategies in different situations and improve the development of innovative car warning technologies, such as rear-end collision warnings.