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Crash modification factors for high friction surface treatment on horizontal curves of two-lane highways: A combined propensity scores matching and empirical Bayes before-after approach Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-03-05 Vikash V. Gayah, Eric T. Donnell, Pengxiang Zhang
Horizontal curves are locations that, as a result of the changing alignment, may be a contributing factor in roadway departure crashes. One low-cost countermeasure to mitigate crashes at these locations is the installation of the high friction surface treatment (HFST), which increases roadway friction and is intended to help keep drivers on the roadway when traversing a horizontal curve. This treatment
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Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-03-05 Shuli Wang, Kun Gao, Lanfang Zhang, Bo Yu, Said M. Easa
Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and
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A two-dimensional surrogate safety measure based on fuzzy logic model Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-03-04 Yueru Xu, Wei Ye, Yuanchang Xie, Chen Wang
Surrogate Safety Measures (SSM) are extensively applied in safety analysis and design of active vehicle safety systems. However, most existing SSM focus only on the one-dimensional interactions along the vehicle traveling direction and cannot handle the crash risks associated with vehicle lateral movements such as sideswipes and angle crashes. To bridge this important knowledge gap, this study proposes
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Spot speed cameras in a series - Effects on speed and traffic safety Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-03-04 Anna Vadeby, Christian Howard
Reduced speeds and increased speed compliance are crucial for achieving increased road traffic safety, cutting across most Safe System interventions. Speed cameras have been shown to be effective in increasing speed compliance and reducing the number of fatalities and seriously injured. The speed cameras system in Sweden is different compared to many other countries, spot speed cameras are almost always
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Quantification of safety improvements and human-machine tradeoffs in the transition to automated driving Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-03-04 Song Wang, Zhixia Li, Yi Wang, Wenjing Zhao, Heng Wei
The assumption of reduced human error-related crashes with increasing levels of automation in pursuing Level 5 automation lacks empirical evidence. As automation levels rise, human error-induced safety hazards are anticipated to decrease, while machine error-induced hazards will increase. However, a quantitative index capturing this tradeoff is absent. Additionally, theoretical modeling of safety improvements
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Revisiting the hybrid approach of anomaly detection and extreme value theory for estimating pedestrian crashes using traffic conflicts obtained from artificial intelligence-based video analytics Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-03-04 Fizza Hussain, Yasir Ali, Yuefeng Li, Md Mazharul Haque
Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches
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Safety performance evaluation of freeway merging areas under autonomous vehicles environment using a co-simulation platform Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-03-03 Peng Chen, Haoyuan Ni, Liang Wang, Guizhen Yu, Jian Sun
Merging areas serve as the potential bottlenecks for continuous traffic flow on freeways. Traffic incidents in freeway merging areas are closely related to decision-making errors of human drivers, for which the autonomous vehicles (AVs) technologies are expected to help enhance the safety performance. However, evaluating the safety impact of AVs is challenging in practice due to the lack of real-world
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High-risk event prone driver identification considering driving behavior temporal covariate shift Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-03-02 Ruici Zhang, Xiang Wen, Huanqiang Cao, Pengfei Cui, Hua Chai, Runbo Hu, Rongjie Yu
Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior
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What can we learn from the AV crashes? – An association rule analysis for identifying the contributing risky factors Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-29 Pei Liu, Yanyong Guo, Pan Liu, Hongliang Ding, Jiandong Cao, Jibiao Zhou, Zhongxiang Feng
The objective of this study is to explore the contributing risky factors to Autonomous Vehicle (AV) crashes and their interdependencies. AV crash data between 2015 and 2023 were collected from the autonomous vehicle collision report published by California Department of Motor Vehicles (DMV). AV crashes were categorized into four types based on vehicle damage. AV crashes features including crash location
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Crash risk estimation of Heavy Commercial vehicles on horizontal curves in mountainous terrain using proactive safety method Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-29 Pranab Kar, Suvin P. Venthuruthiyil, Mallikarjuna Chunchu
Heavy commercial vehicles (HCVs) face elevated crash risks in mountainous terrains due to the challenging topography and intricate geometry, posing a significant challenge for transportation agencies in mitigating these risks. While safety studies in such terrains traditionally rely on historical crash data, the inherent issues associated with crash data have led to a shift towards proactive safety
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Critical risk factors associated with fatal/severe crash outcomes in personal mobility device rider at-fault crashes: A two-step inter-cluster rule mining technique Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-29 Reuben Tamakloe, Kaihan Zhang, Ahmed Hossain, Inhi Kim, Shin Hyoung Park
Personal Mobility Devices (PMDs) have witnessed an extraordinary surge in popularity, emerging as a favored mode of urban transportation. This has sparked significant safety concerns, paralleled by a stark increase in PMD-involved crashes. Research indicates that PMD user behavior, especially in urban areas, is crucial in these crashes, underscoring the need for an extensive investigation into key
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Real-time combined safety-mobility assessment using self-driving vehicles collected data Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-29 Ahmed Kamel, Tarek Sayed, Mohamed Kamel
The study presents a real-time safety and mobility assessment approach using data generated by autonomous vehicles (AVs). The proposed safety assessment method uses Bayesian hierarchical spatial random parameter extreme value model (BHSRP), which can handle the limited availability and uneven distribution of conflict data and accounts for unobserved spatial heterogeneity. The approach estimates two
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A framework for risk matrix design: A case of MASS navigation risk Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-28 Cunlong Fan, Jakub Montewka, Di Zhang, Zhepeng Han
Risk matrix, a tool for visualizing risk assessment results, is essential to facilitate the risk communication and risk management in risk-based decision-making processes related to new and unexplored socio-technical systems. The use of an appropriate risk matrix is discussed in the literature, but it is overlooked for emerging technologies such as Maritime Autonomous Surface Ships (MASS). In this
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Advancing traffic safety through the safe system approach: A systematic review Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-28 Md Nasim Khan, Subasish Das
The Safe System Approach (SSA) has emerged as a comprehensive framework for enhancing traffic safety through system-wide interventions. This systematic review, conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzes 82 relevant studies categorized based on the SSA pillars: safe road users, safe vehicles, safe speeds, safe roads, and post-crash
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Near-crash risk identification and evaluation for takeout delivery motorcycles using roadside LiDAR Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-26 Ciyun Lin, Shaoqi Zhang, Bowen Gong, Hongchao Liu
The proliferation of motorcycles in urban areas has raised concerns regarding traffic safety. However, traditional sensors struggle to obtain precise high-resolution trajectory data, which hinder the accurate identification and quantification of near-crash risks for takeout delivery motorcycles. To fill this gap, this study presents a novel approach utilizing roadside light detection and ranging (LiDAR)
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Young drivers’ early access to their own car and crash risk into early adulthood: Findings from the DRIVE study Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-24 Huei-Yang Chen, Holger Möller, Teresa M. Senserrick, Kris D. Rogers, Patricia Cullen, Rebecca Q. Ivers
Car ownership at early licensure for young drivers has been identified as a crash risk factor, but for how long this risk persists is unknown. This study examined crash hazard rates between young drivers with their own vehicle and those who shared a family vehicle at early licensure over 13 years. The DRIVE study, a 2003/04 survey of 20,806 young novice drivers in New South Wales, Australia was used
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Modeling the influence of connected vehicles on driving behaviors and safety outcomes in highway crash scenarios across varied weather conditions: A multigroup structural equation modeling analysis using a driving simulator experiment Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-23 Abdalziz Alruwaili, Kun Xie
Equipped with advanced sensors and capable of relaying safety messages to drivers, connected vehicles (CVs) hold the potential to reduce crashes. The goal of this study is to assess the impacts of CV technologies on driving behaviors and safety outcomes in highway crash scenarios under diverse weather conditions, including clear and foggy weather. A driving simulator experiment was conducted and the
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Network-wide road crash risk screening: A new framework Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-21 Michela Bonera, Benedetto Barabino, George Yannis, Giulio Maternini
Network-wide road crash risk screening is a crucial issue for road safety authorities in governing the impact of road infrastructures over road safety worldwide. Specifically, screening methods, which also enable a proactive approach (i.e. pinpointing critical segments before crashes occur), would be extremely beneficial. Existing literature provided valuable insights on road network screening and
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Personalizing automated driving speed to enhance user experience and performance in intermediate-level automated driving Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-20 Maxime Delmas, Valérie Camps, Céline Lemercier
In the context of high-level driving automation (SAE levels 4–5), several studies have shown that a personalized automated driving style, i.e., mimicking that of the human behind the wheel, can improve his experience. The objective of this simulator study was to examine the potential transfer of these benefits in the context of intermediate-level driving automation (SAE levels 2–3), focusing on driving
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Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-17 Ahmed Hossain, Xiaoduan Sun, Subasish Das, Monire Jafari, Ashifur Rahman
In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve ‘unintended pedestrians’, drivers who come out of their disabled vehicles, or due to the
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Modeling occupant injury severities for electric-vehicle-involved crashes using a vehicle-accident bi-layered correlative framework with matched-pair sampling Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-16 Qi Yu, Lu Ma, Xuedong Yan
This study seeks to investigate occupant injury severities for electric-vehicle-involved crashes and inspect if electric vehicles lead to more serious injuries than fuel-powered vehicles, which have commonly been neglected in past studies. A Bayesian random slope model is proposed aiming to capture interactions between occupant injury severity levels and electric vehicle variable. The random slope
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Investigating the dynamics of collective behavior among pedestrians crossing roads: A multi-user virtual reality approach Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-15 Jae-Hong Kwon, Jinho Won, Gi-Hyoug Cho
The utility maximization theory, based on the rationality of human beings, has proven effective in modeling pedestrians' decision-making processes while crossing roads. However, there are still unexplained variations in crossing behavior, and deviations from the rational utility model frequently occur in real-life scenarios. This experimental study sheds new light on the presence of inter-individual
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Assessing the effectiveness of psychoeducational interventions on driving behavior: A systematic review and meta-analysis Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-15 Lorena Tirla, Paul Sârbescu, Andrei Rusu
This review aimed to quantitatively summarize the evidence concerning the effectiveness of psychoeducational interventions on driving behavior. A final pool of 138 studies, totaling approximately 97,000 participants, was included in the analyses and covered all types of driving behavior targeted by the interventions. Using a random effects model, significant results were found for almost all driving
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Investigation on the dynamic characteristic of occupant during the frontal collision between high-speed train and obstacle Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-15 Shuguang Yao, Lingxiang Kong, Ping Xu, Xianliang Xiao, Yong Peng
High-speed train may collide with many obstacles, which can cause serious occupant injury. This study aims to investigate the dynamic characteristic of occupant during the frontal collision between high-speed train and obstacle. The finite element method was used to establish the collision model between the head vehicle of the train and obstacle. The frontal collision simulation tests under three collision
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Short-term Safety Performance Functions by Random Parameters Negative Binomial-Lindley model for Part-time Shoulder Use Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-14 Tarek Hasan, Mohamed Abdel-Aty
Part-time Shoulder Use (PTSU) is a traffic management and operation strategy that allows the use of the left or right shoulder as a travel lane, typically during the peak hours of the day. Though PTSU is an effective strategy for increasing roadway capacity in congested traffic conditions, there is very limited quantitative information about PTSU design elements and operational strategy in the existing
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Identifying contributing factors and locations of pedestrian severe crashes using hazard-based duration model Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-10 Anahita Kakhani, Mohammad Jalayer, Emmanuel Kidando, Carlos Roque, Deep Patel
Pedestrian safety remains a significant concern, with the growing number of severe pedestrian crashes resulting in substantial human and economic costs. Previous research into pedestrian crashes has extensively analyzed the influences of weather, lighting, and pedestrian demographics. However, these studies often overlook the critical spatial variables that contribute to pedestrian crashes. Our study
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Investigating the impact of HMI on drivers' merging performance in intelligent connected vehicle environment Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-09 Yugang Wang, Nengchao Lyu, Chaozhong Wu, Zijun Du, Min Deng, Haoran Wu
Intelligent Connected Vehicle (ICV) is considered one of the most promising active safety technologies to address current transportation challenges. Human-Machine Interface (HMI) plays a vital role in enhancing user driving experience with ICV technology. However, in an ICV environment, drivers may exhibit excessive reliance on HMI, resulting in diminished proactive observation and analysis of the
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Optimization of Forward Collision Warning Algorithm Considering Truck Driver Response Behavior Characteristics Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-09 Yanli Bao, Xuesong Wang
Forward collision warning (FCW) systems have been widely used in trucks to alert drivers of potential road situations so they can reduce the risk of crashes. Research on FCW use shows, however, that there are differences in drivers’ responses to FCW alerts under different scenarios. Existing FCW algorithms do not take differences in driver response behavior into account, with the consequence that the
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Modeling and analyzing self-resistance of connected automated vehicular platoons under different cyberattack injection modes Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-08 Dongyu Luo, Jiangfeng Wang, Yu Wang, Jiakuan Dong
The high-level integration and interaction between the information flow at the cyber layer and the physical subjects at the vehicular layer enables the connected automated vehicles (CAVs) to achieve rapid, cooperative and shared travel. However, the cyber layer is challenged by malicious attacks and the shortage of communication resources, which makes the vehicular layer suffer from system nonlinearity
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A spatiotemporal deep learning approach for pedestrian crash risk prediction based on POI trip characteristics and pedestrian exposure intensity Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-08 Manze Guo, Bruce Janson, Yongxin Peng
Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian exposure, considering factors such as Point of Interest (POI) attributes, POI intensity, traffic volume, and pedestrian walkability. Following risk interpolation
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Integrating visual large language model and reasoning chain for driver behavior analysis and risk assessment Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-07 Kunpeng Zhang, Shipu Wang, Ning Jia, Liang Zhao, Chunyang Han, Li Li
Driver behavior is a critical factor in driving safety, making the development of sophisticated distraction classification methods essential. Our study presents a Distracted Driving Classification (DDC) approach utilizing a visual Large Language Model (LLM), named the Distracted Driving Language Model (DDLM). The DDLM introduces whole-body human pose estimation to isolate and analyze key postural features—head
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Evaluation of driver navigational errors and acceptance of a simulated J-turn intersection Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-06 Nichole L. Morris, Katelyn R. Schwieters, Disi Tian, Curtis M. Craig
The J-turn intersection is a novel roadway design which decreases the points of conflict at an intersection, by restricting straight crossing and left-turning movements from the minor road across the highway. The novelty of the intersection design may lead to driver errors and dissatisfaction. This study provides an examination of how naïve or first-time drivers may initially navigate J-turns during
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Lane-change intention recognition considering oncoming traffic: Novel insights revealed by advances in deep learning Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-06 Hao Liu, Tao Wang, Wenyong Li, Xiaofei Ye, Quan Yuan
Lane-changing (LC) intention recognition models have seen limited real-world application due to a lack of research on two-lane two-way road environments. This study constructs a high-fidelity simulated two-lane two-way road to develop a Transformer model that accurately recognizes LC intention. We propose a novel LC labelling algorithm combining vehicle dynamics and eye-tracking (VEL) and compare it
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Safety-oriented automated vehicle longitudinal control considering both stability and damping behavior Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-03 Yulu Dai, Chen Wang, Yuanchang Xie
Extensive research has examined the potential benefits of Automated Vehicles (AVs) for increasing traffic capacity and improving safety. However, previous studies on AV longitudinal control have focused primarily on control stability and instability or tradeoffs between safety and stability, neglecting the importance of vehicle damping characteristics. This study aims to demonstrate the significance
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Understanding the relationship between road users and the roadway infrastructure in Ghana Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-02-03 Brianna P. Lawton, Shauna L. Hallmark, Guillermo Basulto-Elias, Daniel Atuah Obeng, Williams Ackaah
Ghana exemplifies the contribution of road crashes to mortality and morbidity in Africa, partly due to a growing population and increasing car ownership, where fatalities have increased by 12 to 15 % annually since 2008 (National Road Safety Authority (NRSA), 2017). The study described in this paper focused on understanding driver behavior at unsignalized junctions in the Ashanti Region of Ghana. Understanding
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Assessing the collective safety of automated vehicle groups: A duration modeling approach of accumulated distances between crashes Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-29 Soheil Sohrabi, Dominique Lord, Bahar Dadashova, Fred Mannering
Ideally, the evaluation of automated vehicles would involve the careful tracking of individual vehicles and recording of observed crash events. Unfortunately, due to the low frequency of crash events, such data would require many years to acquire, and potentially place the motorized public at risk if defective automated technologies were present. To acquire information on the safety effectiveness of
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Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-30 Johan Engström, Shu-Yuan Liu, Azadeh Dinparastdjadid, Camelia Simoiu
There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not
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Use of smartphone apps while driving: Variations on driving performances and perceived risks Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-29 Juana Perez, Kate Hyun, Jobaidul Alam Boni
Distracted driving increases the crash frequencies on the road and subsequently leads to fatalities involved with crashes. As technology has evolved, drivers are continuously exposed to newer technology in their vehicles and applications in their phones, which has led to technology representing one of the main secondary tasks that distract drivers on the road. The impact of technology-involved distraction
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Enhancing autonomous vehicle hyperawareness in busy traffic environments: A machine learning approach Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-26 Abdul Razak Alozi, Mohamed Hussein
As autonomous vehicles (AVs) advance from theory into practice, their safety and operational impacts are being more closely studied. This study aims to contribute to the ever-evolving algorithms used by AVs during travel in busy urban districts, as well as explore the potential utilization of AV sensor data to identify safety hazards to surrounding road users in real time. Accordingly, the study incorporates
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Risk of motor vehicle collision associated with cannabis and alcohol use among patients presenting for emergency care Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-25 Esther K. Choo, Stacy A. Trent, Daniel K. Nishijima, Angela Eichelberger, Steve Kazmierczak, Yu Ye, Karen J. Brasel, Ariane Audett, Cheryl J Cherpitel
Background The objective of this study was to examine the relationship between cannabis and alcohol use and occurrence of motor vehicle collision (MVC) among patients in the emergency department (ED). Methods This was a cross-sectional study of visits to EDs in Denver, CO, Portland, OR, and Sacramento, CA by drivers who were involved in MVCs and presented with injuries (cases) and non-injured drivers
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Novice driver crashes: The relation between putative causal factors, countermeasures, real world implementations, and policy – A case study in simple, scalable solutions Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-24 Donald L. Fisher, Ravi Agrawal, Gautam Divekar, Malek Abdul Hamid, Akhilesh Krishnan, Hasmik Mehranian, Jeff Muttart, Anuj Pradhan, Shannon Roberts, Matthew Romoser, Siby Samuel, Willem Vlakveld, Yusuke Yamani, Jared Young, Tracy Zafian, Lisa Zhang
Novice drivers are at a greatly inflated risk of crashing. This led in the 20th century to numerous attempts to develop training programs that could reduce their crash risk. Yet, none proved effective. Novice drivers were largely considered careless, not clueless. This article is a case study in the United States of how a better understanding of the causes of novice driver crashes led to training countermeasures
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Exploring the influence of drivers’ visual surroundings on speeding behavior Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-20 Mohamed Abdel-Aty, Jorge Ugan, Zubayer Islam
Despite awareness campaigns and legal consequences, speeding is a significant cause of road accidents and fatalities globally. To combat this issue, understanding the impact of a driver's visual surroundings is crucial in designing roadways that discourage speeding. This study investigates the influence of visual surroundings on drivers in 15 US cities using 3,407,253 driver view images from Lytx,
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Evaluating safety and compliance of pedestrian crossings in rural contexts: A before and after study of RRFBs and LED-embedded signs Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-19 Parsa Pezeshknejad, Dana Rowangould
Improving the safety of pedestrian and cyclist infrastructure is critical for reducing traffic-related injuries and fatalities. Pedestrian traffic safety risks are heightened in rural contexts. A key area of focus is the protection of pedestrians crossing roadways between intersections and in high-risk areas such as rural to urban transition zones. One way to reduce safety risks for pedestrians is
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Can we trust our eyes? Interpreting the misperception of road safety from street view images and deep learning Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-12 Xujing Yu, Jun Ma, Yihong Tang, Tianren Yang, Feifeng Jiang
Road safety is a critical concern that impacts both human lives and urban development, drawing significant attention from city managers and researchers. The perception of road safety has gained increasing research interest due to its close connection with the behavior of road users. However, safety isn't always as it appears, and there is a scarcity of studies examining the association and mismatch
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Predicting pedestrian-involved crash severity using inception-v3 deep learning model Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-13 Md Nasim Khan, Subasish Das, Jinli Liu
This research leverages a novel deep learning model, Inception-v3, to predict pedestrian crash severity using data collected over five years (2016–2021) from Louisiana. The final dataset incorporates forty different variables related to pedestrian attributes, environmental conditions, and vehicular specifics. Crash severity was classified into three categories: fatal, injury, and no injury. The Boruta
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Risk factors as causes of accidents: Criterion of causality, logical structure of relationship to accidents and completeness of explanations Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-12 Rune Elvik
The causes of accidents are studied in the belief that by finding causes, accidents can be prevented by removing or controlling their causes. It follows that the risk factors that have traditionally been regarded as contributing to accidents can only be regarded as causes if it is possible to alter them by means of one or more road safety measures. Risk factors are causes if their relationship to accidents
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Identifying the latent relationships between factors associated with traffic crashes through graphical models Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-13 Mehmet Baran Ulak, Eren Erman Ozguven
Traffic safety field has been oriented toward finding the relationships between crash outcomes and predictor variables to understand crash phenomena and/or predict future crashes. In the literature, the main framework established for this purpose is based on constructing a modelling equation in which crash outcome (e.g., frequencies) is examined in relation to explanatory variables chosen based on
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A systematic review and meta-analysis of data linkage between motor vehicle crash and hospital-based datasets Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-09 Sajjad Karimi, Aryan Hosseinzadeh, Robert Kluger, Teng Wang, Reginald Souleyrette, Ed Harding
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
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Impact of alcohol driving-while-impaired license suspension duration on future alcohol-related license events and motor vehicle crash involvement in North Carolina, 2007 to 2016 Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-10 Bhavna Singichetti, Yvonne M. Golightly, Yudan Chen Wang, Stephen W. Marshall, Rebecca B. Naumann
Background/Purpose License suspensions are a strategy to address alcohol-impaired driving behavior and recidivism following an alcohol driving while impaired (alcohol-DWI) conviction. Little is known about the specific impacts of conviction-related suspensions on safety outcomes and given recent fluctuations in alcohol-impaired driving behavior, crashes, and suspension trends, updated and focused assessments
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The effect of rear bicycle light configurations on drivers’ perception of cyclists’ presence and proximity Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-05 Daniel T. Bishop, Huma Waheed, Tamara S. Dkaidek, David P. Broadbent
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Police and hospital data linkage for traffic injury surveillance: A systematic review Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-05 Ali Soltani, James Edward Harrison, Courtney Ryder, Joanne Flavel, Angela Watson
This systematic review examines studies of traffic injury that involved linkage of police crash data and hospital data and were published from 1994 to 2023 worldwide in English. Inclusion and exclusion criteria were the basis for selecting papers from PubMed, Web of Science, and Scopus, and for identifying additional relevant papers using PRISMA (Preferred Reporting Items for Systematic Reviews and
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Dynamic short-term crash analysis and prediction at toll plazas for proactive safety management Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-06 Weiwei Mo, Jaeyoung Lee, Mohamed Abdel-Aty, Suyi Mao, Qianshan Jiang
Toll plazas are commonly recognized as bottlenecks on toll roads, where vehicles are prone to crashes. However, there has been a lack of research analyzing and predicting dynamic short-term crash risk specifically at toll plazas. This study utilizes traffic, geometric, and weather data to analyze and predict dynamic short-term collision occurrence probability at mainline toll plazas. A random-effects
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Software-defined traffic light preemption for faster emergency medical service response in smart cities Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-03 Nazila Bagheri, Saleh Yousefi, Gianluigi Ferrari
Proper management of rescue operations following an accident is one of the most fundamental challenges faced by today's smart cities. Taking advantage of vehicular communications, in this paper we propose novel mechanisms for the acceleration of the rescue operation resulting in a reduction in fatalities in accidents. We propose a Software-Defined Traffic Light Preemption (SD-TLP) mechanism that enables
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Safety assessment of on-road cycling lanes: A comparative study of different layouts using driving simulator Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-03 Mustafa Almallah, Wael K.M. Alhajyaseen, Charitha Dias
Over the past few decades, a growing attention has been directed toward cycling due to its positive impacts on social, economic, and health aspects. Various countries are adopting and implementing strategies to promote cycling as a daily mode of transport. The main objective of this study is to improve cyclists’ safety by investigating the impact of different layouts of on-road cycle lanes at two-lane
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Impact of real-time weather conditions on crash injury severity in Kentucky using the correlated random parameters logit model with heterogeneity in means Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-04 Bharat Kumar Pathivada, Arunabha Banerjee, Kirolos Haleem
The present study investigated the impact of real-time weather (air temperature, relative humidity, precipitation, wind speed, and solar radiation) on crash injury severity. Recent crash data (January 2016 to April 2021) on Interstate-75 in the state of Kentucky were merged with real-time weather information (retrieved from Kentucky Mesonet stations) at the 1-hour level. The severity index “SI” (i
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Unraveling the differences in distracted driving injury severities in passenger car, sport utility vehicle, pickup truck, and minivan crashes Accident Analysis & Prevention (IF 6.376) Pub Date : 2024-01-01 Mouyid Islam
Distracted driving poses a significant risk on the roadway users, with the level of distraction and crash outcomes varying depending on the type of vehicle. Drivers of passenger cars, sport utility vehicles (SUVs), pickup trucks, minivans experience distinct levels of distraction, leading to potential crashes. This study investigates into the severity of driver injuries resulting from distracted driving
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Are crash causation studies the best way to understand system failures – Who can we blame? Accident Analysis & Prevention (IF 6.376) Pub Date : 2023-12-31 A. Lie, C. Tingvall
The search for common and serious single causes of road crashes naturally leads to a concentration on the road user. This is supported by a legal framework in the search for the main cause and the suspect for this cause. In prevention, we have for decades been more inclined to look for systematic improvements of all elements of the road transport system, and we direct the recommendations for actions