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
Accid Anal Prev. 2024 Feb 23;199:107514. doi: 10.1016/j.aap.2024.107514. Online ahead of print.ABSTRACTEquipped 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 multigroup structural equation modeling (SEM) was employed to explore the complex interrelationships between the propensity ...
Source: Accident; Analysis and Prevention. - February 24, 2024 Category: Accident Prevention Authors: Abdalziz Alruwaili Kun Xie Source Type: research
Fatigue at the wheel: A non-visual approach to truck driver fatigue detection by multi-feature fusion
CONCLUSIONS: Our study is among the first to harness the natural driving dataset to delve into the real-life fatigue pattern of long-haul truck drivers without disruptions on routine driving tasks. The proposed method holds pragmatic prospects by providing a privacy-preserving, robust, real-time, and non-intrusive technical pathway for truck driver fatigue monitoring.PMID:38387154 | DOI:10.1016/j.aap.2024.107511 (Source: Accident; Analysis and Prevention.)
Source: Accident; Analysis and Prevention. - February 22, 2024 Category: Accident Prevention Authors: Chen He Pengpeng Xu Xin Pei Qianfang Wang Yun Yue Chunyang Han Source Type: research
Network-wide road crash risk screening: A new framework
This study covers this gap by a new framework. It integrates road safety factors, prediction models and a risk-based method, and returns the risk value on each road segment as a function of the probability of a crash occurrence and the related severity as well as the exposure model. Next, road segments are ranked according to the risk value and classified by a five-level scale, to show the parts of road network with the highest crash risk. Experiments show the capability of this framework by integrating base map data, context information, road traffic data and five years of real-world crash data records of the whole non-ur...
Source: Accident; Analysis and Prevention. - February 22, 2024 Category: Accident Prevention Authors: Michela Bonera Benedetto Barabino George Yannis Giulio Maternini Source Type: research
Fatigue at the wheel: A non-visual approach to truck driver fatigue detection by multi-feature fusion
CONCLUSIONS: Our study is among the first to harness the natural driving dataset to delve into the real-life fatigue pattern of long-haul truck drivers without disruptions on routine driving tasks. The proposed method holds pragmatic prospects by providing a privacy-preserving, robust, real-time, and non-intrusive technical pathway for truck driver fatigue monitoring.PMID:38387154 | DOI:10.1016/j.aap.2024.107511 (Source: Accident; Analysis and Prevention.)
Source: Accident; Analysis and Prevention. - February 22, 2024 Category: Accident Prevention Authors: Chen He Pengpeng Xu Xin Pei Qianfang Wang Yun Yue Chunyang Han Source Type: research
Network-wide road crash risk screening: A new framework
This study covers this gap by a new framework. It integrates road safety factors, prediction models and a risk-based method, and returns the risk value on each road segment as a function of the probability of a crash occurrence and the related severity as well as the exposure model. Next, road segments are ranked according to the risk value and classified by a five-level scale, to show the parts of road network with the highest crash risk. Experiments show the capability of this framework by integrating base map data, context information, road traffic data and five years of real-world crash data records of the whole non-ur...
Source: Accident; Analysis and Prevention. - February 22, 2024 Category: Accident Prevention Authors: Michela Bonera Benedetto Barabino George Yannis Giulio Maternini Source Type: research
Fatigue at the wheel: A non-visual approach to truck driver fatigue detection by multi-feature fusion
CONCLUSIONS: Our study is among the first to harness the natural driving dataset to delve into the real-life fatigue pattern of long-haul truck drivers without disruptions on routine driving tasks. The proposed method holds pragmatic prospects by providing a privacy-preserving, robust, real-time, and non-intrusive technical pathway for truck driver fatigue monitoring.PMID:38387154 | DOI:10.1016/j.aap.2024.107511 (Source: Accident; Analysis and Prevention.)
Source: Accident; Analysis and Prevention. - February 22, 2024 Category: Accident Prevention Authors: Chen He Pengpeng Xu Xin Pei Qianfang Wang Yun Yue Chunyang Han Source Type: research
Network-wide road crash risk screening: A new framework
This study covers this gap by a new framework. It integrates road safety factors, prediction models and a risk-based method, and returns the risk value on each road segment as a function of the probability of a crash occurrence and the related severity as well as the exposure model. Next, road segments are ranked according to the risk value and classified by a five-level scale, to show the parts of road network with the highest crash risk. Experiments show the capability of this framework by integrating base map data, context information, road traffic data and five years of real-world crash data records of the whole non-ur...
Source: Accident; Analysis and Prevention. - February 22, 2024 Category: Accident Prevention Authors: Michela Bonera Benedetto Barabino George Yannis Giulio Maternini Source Type: research
Personalizing automated driving speed to enhance user experience and performance in intermediate-level automated driving
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 speed personalization. In the first phase of the study, the driving speed of 52 participants was recorded. In the second phase, the same participants were driven by an automated car on a highway twice, and sometimes had to takeover during the drive because of a stationary vehicle on the lane. On these two drives, the automated car drove either at the same speed as them (personalized) or 20 km/h faster. The results showed that using a pers...
Source: Accident; Analysis and Prevention. - February 20, 2024 Category: Accident Prevention Authors: Maxime Delmas Val érie Camps C éline Lemercier Source Type: research
Personalizing automated driving speed to enhance user experience and performance in intermediate-level automated driving
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 speed personalization. In the first phase of the study, the driving speed of 52 participants was recorded. In the second phase, the same participants were driven by an automated car on a highway twice, and sometimes had to takeover during the drive because of a stationary vehicle on the lane. On these two drives, the automated car drove either at the same speed as them (personalized) or 20 km/h faster. The results showed that using a pers...
Source: Accident; Analysis and Prevention. - February 20, 2024 Category: Accident Prevention Authors: Maxime Delmas Val érie Camps C éline Lemercier Source Type: research
Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means
This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014-2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian's FS injury in crashes on interstate hi...
Source: Accident; Analysis and Prevention. - February 18, 2024 Category: Accident Prevention Authors: Ahmed Hossain Xiaoduan Sun Subasish Das Monire Jafari Ashifur Rahman Source Type: research
Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means
This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014-2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian's FS injury in crashes on interstate hi...
Source: Accident; Analysis and Prevention. - February 18, 2024 Category: Accident Prevention Authors: Ahmed Hossain Xiaoduan Sun Subasish Das Monire Jafari Ashifur Rahman Source Type: research
Multi-objective deep reinforcement learning approach for adaptive traffic signal control system with concurrent optimization of safety, efficiency, and decarbonization at intersections
This study introduces a novel approach to adaptive traffic signal control (ATSC) by leveraging multi-objective deep reinforcement learning (DRL) techniques. The proposed scheme aims to optimize control strategies at intersections while concurrently addressing the objectives of safety, efficiency, and decarbonization. Traditional ATSC schemes primarily emphasize traffic efficiency and often lack the ability to adapt to real-time dynamic traffic conditions. To overcome these limitations, the study proposes a DRL-based ATSC algorithm that integrates the Dueling Double Deep Q Network (D3QN) framework. The performance of the pr...
Source: Accident; Analysis and Prevention. - February 17, 2024 Category: Accident Prevention Authors: Gongquan Zhang Fangrong Chang Jieling Jin Fan Yang Helai Huang Source Type: research
Multi-objective deep reinforcement learning approach for adaptive traffic signal control system with concurrent optimization of safety, efficiency, and decarbonization at intersections
This study introduces a novel approach to adaptive traffic signal control (ATSC) by leveraging multi-objective deep reinforcement learning (DRL) techniques. The proposed scheme aims to optimize control strategies at intersections while concurrently addressing the objectives of safety, efficiency, and decarbonization. Traditional ATSC schemes primarily emphasize traffic efficiency and often lack the ability to adapt to real-time dynamic traffic conditions. To overcome these limitations, the study proposes a DRL-based ATSC algorithm that integrates the Dueling Double Deep Q Network (D3QN) framework. The performance of the pr...
Source: Accident; Analysis and Prevention. - February 17, 2024 Category: Accident Prevention Authors: Gongquan Zhang Fangrong Chang Jieling Jin Fan Yang Helai Huang Source Type: research
Investigating the dynamics of collective behavior among pedestrians crossing roads: A multi-user virtual reality approach
Accid Anal Prev. 2024 Feb 15;199:107477. doi: 10.1016/j.aap.2024.107477. Online ahead of print.ABSTRACTThe 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 interactions among pedestrians and the nature of collective behaviors during road crossings. The present study develops a mu...
Source: Accident; Analysis and Prevention. - February 16, 2024 Category: Accident Prevention Authors: Jae-Hong Kwon Jinho Won Gi-Hyoug Cho Source Type: research
Modeling occupant injury severities for electric-vehicle-involved crashes using a vehicle-accident bi-layered correlative framework with matched-pair sampling
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 model is developed under a vehicle-accident bi-layered correlative framework, which can account for the interactive effects of vehicles in the same accident. Based on the crash report sampling system (CRSS) 2020 and 2021 database...
Source: Accident; Analysis and Prevention. - February 16, 2024 Category: Accident Prevention Authors: Qi Yu Lu Ma Xuedong Yan Source Type: research
