Article
Public, Environmental & Occupational Health
Chenzhu Wang, Fei Chen, Yunlong Zhang, Jianchuan Cheng
Summary: This study investigates the heterogeneity and spatiotemporal stability of contributing factors affecting truck-involved and non-truck-involved crashes. The findings show remarkable differences between these two types of crashes and an overall spatiotemporal instability.
ANALYTIC METHODS IN ACCIDENT RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Mahyar Madarshahian, Aditya Balaram, Fahim Ahmed, Nathan Huynh, Chowdhury K. A. Siddiqui, Mark Ferguson
Summary: This study investigates the factors contributing to the severity of truck-involved work zone crashes in South Carolina. Two mixed logit models are developed, one for non-interstates and one for interstates, using crash data from 2014 to 2020. The factors found to contribute to injury in both models are dark lighting conditions, female (at-fault) drivers, and driving too fast for roadway conditions. Significant factors specific to non-interstates include roadway type, work zone activity area, younger at-fault drivers, sideswipe collision, presence of workers, and collision with fixed objects. Significant factors specific to interstates include multiple vehicles involved, rear-end collision, location before the first work zone sign, and weekdays.
Article
Ergonomics
Jing Li, Shouen Fang, Jingqiu Guo, Ting Fu, Min Qiu
Summary: This study examines the effects of rider characteristics, road conditions, precrash situations, and crash features on motorcycle severities with respect to different numbers of vehicles involved. The results show significant differences in severity between different numbers of vehicles involved in the crashes, and dividing crashes into homogeneous classes prior to modeling helps discover insightful information.
ACCIDENT ANALYSIS AND PREVENTION
(2021)
Article
Green & Sustainable Science & Technology
Zheng Chen, Huiying Wen, Qiang Zhu, Sheng Zhao
Summary: This study aims to address the gap in the literature that has only examined the individual effects of road alignment or grade on the severity of truck crashes without considering their combined effects. The findings suggest that the combination of curve and slope significantly increases the severity of truck crashes compared to curves and slopes alone on mountainous freeways. Additionally, factors such as crash type, vehicle type, surface condition, time of day, pavement structure, and guardrails have a significant impact on the severity of truck crashes. Based on these findings, policy recommendations are provided for reducing the severity of multi-truck collisions and improving transport sustainability on mountainous highways.
Article
Ergonomics
Aryan Hosseinzadeh, Amin Moeinaddini, Ali Ghasemzadeh
Summary: The study focused on predicting factors influencing crash injury severity in large truck-involved crashes using two techniques, RPBL and SVM. Results indicated that fatigue and deviation to the left were significant contributing factors to fatal crashes when the large truck-driver is at fault, with differences in significant factors between RPBL and SVM.
JOURNAL OF SAFETY RESEARCH
(2021)
Article
Engineering, Civil
Ghazaleh Azimi, Alireza Rahimi, Hamidreza Asgari, Xia Jin
Summary: This study examines the factors influencing crash injury severity in large truck-involved crashes where the truck driver is at fault. The results show that not using restraint systems, running red lights, wrong-way driving, failing to yield the right of way, tire or brake defects, and dark conditions are all associated with higher levels of crash injury severity. Additionally, the study finds significant random effects and interaction effects in variables such as straight alignment, paved shoulders, and unpaved shoulders, indicating their impact on crash severity.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Green & Sustainable Science & Technology
Fulu Wei, Danping Dong, Pan Liu, Yongqing Guo, Zhenyu Wang, Qingyin Li
Summary: This study reviewed 10 years of truck crash data in Shandong Province, China, and examined the factors influencing the severity of truck crash injuries from a quarterly perspective. The results showed instability among the quarterly variables and identified heavy vehicle and multiple-vehicle crashes as important factors affecting the severity of truck crash injuries.
Article
Transportation
Miao Yu, Changxi Ma, Changjiang Zheng, Zhen Chen, Tinghui Yang
Summary: This paper analyzes the severity of injuries in truck-involved crashes in work zones, considering various variables. The results show significant differences in injury severity between rural and urban highways. The findings contribute to understanding the outcomes of truck-related accidents in rural and urban work zones.
JOURNAL OF TRANSPORTATION SAFETY & SECURITY
(2022)
Article
Ergonomics
Seyed Alireza Samerei, Kayvan Aghabayk, Nirajan Shiwakoti, Amin Mohammadi
Summary: A study on factors affecting motor vehicle-bicycle crashes in Victoria, Australia found that elderly bicyclists, not using a helmet, and darkness condition are the key factors increasing the risk of fatalities and serious injuries to bicyclists. Recommendations to reduce this risk include improving road lighting, increasing exposure of bicyclists using reflective clothing, and monitoring helmet use.
JOURNAL OF SAFETY RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Kanghyun Kim, Jungyeol Hong
Summary: In this study, the Random Parameter Ordered Logit model was used to analyze the factors influencing the severity of intercity bus accidents and to examine the heterogeneity of accident characteristics. The results showed that driver's condition, vehicle size, crash type, road condition, and traffic volume were important factors affecting accident severity.
Article
Engineering, Civil
Rajesh Gupta, Hamidreza Asgari, Ghazaleh Azimi, Alireza Rahimi, Xia Jin
Summary: This study analyzed large truck-involved work zone fatal crashes in Florida using seven-year crash data. The combination of over-sampling techniques with ensemble random forests significantly improved the prediction of fatal crashes. Factors such as pedestrian involvement, lighting conditions, driver status, and others were identified as important contributors to crash severity outcomes.
TRANSPORTATION RESEARCH RECORD
(2021)
Article
Green & Sustainable Science & Technology
Ming Sun, Ronggui Zhou
Summary: This study analyzes ten years of hazardous material (HAZMAT) truck-involved crash data to identify patterns and associations between risk factors. The study finds distinguishable characteristics in terms of collision types, roadway geometry, driver behavior, lighting conditions, and adverse weather. The findings will assist HAZMAT carriers, transportation management authorities, and policymakers in developing targeted countermeasures to reduce HAZMAT-truck-involved crashes and improve safety.
Article
Ergonomics
Yalong Yuan, Min Yang, Yanyong Guo, Soora Rasouli, Zuoxian Gan, Yifeng Ren
Summary: The study utilized a five-year dataset to analyze and classify risk factors of truck drivers in fatal crashes. Different risk factors were found to have varying impacts on different driver groups, with a recommendation to pay more attention to drivers with high risk of driving violations and high historical crash records.
JOURNAL OF SAFETY RESEARCH
(2021)
Article
Ergonomics
Zijing Lin, Wei (David) Fan
Summary: It is crucial to investigate factors contributing to bicyclist injury severity for better biking environment and safety. A latent class clustering analysis was conducted on crash data to identify heterogeneity, revealing different impacts of variables on injury severity across clusters. The study highlights the importance of considering unobserved features and suggests regulations on drinking and provision of lights to improve biking safety.
JOURNAL OF SAFETY RESEARCH
(2021)
Article
Public, Environmental & Occupational Health
Fangrong Chang, Shamsunnahar Yasmin, Helai Huang, Alan H. S. Chan, Md. Mazharul Haque
Summary: This study compares the performance of latent class clustering and latent segmentation-based random parameter models in examining crash injury severity outcomes, finding that the latent segmentation approach is superior to the latent class clustering-based approach; random parameter variants also outperform fixed parameters, highlighting the need to consider both across- and within-group heterogeneity; the effects of exogenous variables on rider injury severity are different across year-wise models, indicating temporal instability.
ANALYTIC METHODS IN ACCIDENT RESEARCH
(2021)
Article
Engineering, Civil
Xiaoning Wang, Li Song, Zhitao Wu, Peijie Wu
Summary: A traffic noise prediction model based on the equivalent sound source at the road shoulder was developed in this study. The proposed model performed better than the current model in terms of prediction accuracy.
PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT
(2022)
Article
Public, Environmental & Occupational Health
Li Song, Yang Li, Wei (David) Fan, Pengfei Liu
Summary: This research aims to identify and compare factors contributing to pedestrian injury severities in pedestrian-vehicle crashes considering time-of-day and day-of-week. The study analyzed pedestrian-vehicle crash data in North Carolina from 2007 to 2018 using mixed logit models, revealing significant factors that influence pedestrian injury severity. Major factors such as involvement of large vehicles, elderly pedestrians, hit and run incidents, drunk pedestrians, lighting conditions, and land use were found to increase the probability of fatal injury. The study also found that factors have a greater impact on severe injuries at night compared to daytime, and more severe injuries occur on weekends than on weekdays.
TRAFFIC INJURY PREVENTION
(2021)
Article
Transportation Science & Technology
Li Song, Wei (David) Fan, Pengfei Liu
Summary: The study found that CAVs with the CACC system outperformed AVs with ACC or IDM systems at intersections in mixed traffic flows, reducing average delay by 49% to 96% under low and high traffic demands. Additionally, CAVs with CACC/ACC systems significantly improved the performance of actuated signal-controlled intersections under medium traffic demand.
TRANSPORTATION PLANNING AND TECHNOLOGY
(2021)
Article
Transportation
Peijie Wu, Xianghai Meng, Li Song
Summary: The aim of the study is to use the space-time cube method to identify high-risk crash spots at the microzone-level and analyze the factors contributing to their formation using latent class analysis. The cumulative frequency curve method was applied to identify high-risk crash spots, and it was found that key parameter selection is crucial in space-time cube construction.
JOURNAL OF TRANSPORTATION SAFETY & SECURITY
(2022)
Article
Physics, Multidisciplinary
Peijie Wu, Xianghai Meng, Li Song
Summary: This study examines the crash risk of cyclists and pedestrians using three multivariate Bayesian space-time models. Long-term spatiotemporal effects, as well as contributing risk factors such as demographic features and weather characteristics, are identified. The findings offer valuable insights for policymakers to enhance the safety of cyclists and pedestrians.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Public, Environmental & Occupational Health
Tianjia Yang, Wei (David) Fan, Li Song
Summary: The objective of this study is to identify and compare the contributing factors to pedestrian injury severity in pedestrian-vehicle crashes considering different land use patterns. Two mixed logit models were developed to analyze the crash dataset with segmentations of two dominant land use areas. The study found that factors such as elder or drunk pedestrians have more impacts on severe injuries in residential areas, while large and mid-size vehicles increase the probability of severe injuries in commercial areas.
TRAFFIC INJURY PREVENTION
(2023)
Article
Transportation
Yang Li, Wei (David) Fan, Li Song, Shaojie Liu
Summary: Pedestrians are at a higher risk of danger and severe injuries in crashes. Proper modeling approaches can help identify the causes of pedestrian-vehicle crashes and improve pedestrian safety.
JOURNAL OF TRANSPORTATION SAFETY & SECURITY
(2023)
Article
Engineering, Civil
Chengying Hua, Wei Fan, Li Song, Shaojie Liu
Summary: The purpose of this study is to identify potential factors that affect the injury severity of overturn crashes involving SUVs and develop adequate preventive strategies. Crash data in North Carolina is analyzed and divided into six relatively homogeneous groups based on the heterogeneity existing in the data set. Variables such as females, people over fifty years old, improper or aggressive behavior, rural areas, and adverse weather are associated with the injury severity of the overturn crashes involving SUVs. The findings can provide decision makers with insightful countermeasures to improve transportation safety and mitigate the injuries of these crashes.
JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS
(2023)
Article
Engineering, Civil
Li Song, Wei David Fan
Summary: The emerging technologies of connected and automated vehicles (CAVs) and deep reinforcement learning (DRL) have the potential to improve intersection efficiency. CAVs with cooperative adaptive cruise control (CACC) can form platoons and smoothly traverse intersections. The use of DRL enables intelligent traffic signal controls based on CAVs' traffic information. This research explores the performance of a state-shared multiagent deep reinforcement learning (MADRL) controlled signal corridor with platooning-based CAVs, which shows significant improvements in waiting time, queue length, and CO2 emissions.
JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS
(2023)
Article
Computer Science, Information Systems
Li Song, Wei Fan
Summary: This study improves the training efficiency of deep Q network by transferring well-trained action policy into a target model, and analyzes the performance of transfer-based models under different traffic demands and CAVs market penetration rates. Results show that transfer-based models significantly reduce waiting time, CO2 emission, and fuel consumption in high traffic scenarios compared to directly trained models.
Article
Ergonomics
Li Song, Wei (David) Fan, Yang Li, Peijie Wu
Summary: This study successfully identifies pedestrian-vehicle crash hotspots and contributing factors using various spatial analysis methods and a latent class model, providing important insights for developing effective countermeasures in the future.
JOURNAL OF SAFETY RESEARCH
(2021)
Article
Public, Environmental & Occupational Health
Yang Li, Li Song, Wei (David) Fan
Summary: This study utilizes pedestrian-vehicle crash data from North Carolina between 2007 and 2018 to examine the influence of various factors on pedestrian injury severity, revealing stable effects for some factors across different time periods but strong temporal instabilities for most factors. The model accounts for heterogeneity in means and variances of random parameters, offering insights for policymakers to improve pedestrian safety within the transportation system.
ANALYTIC METHODS IN ACCIDENT RESEARCH
(2021)
Article
Public, Environmental & Occupational Health
Li Song, Yang Li, Wei (David) Fan, Peijie Wu
ANALYTIC METHODS IN ACCIDENT RESEARCH
(2020)
Article
Transportation
Peijie Wu, Xianghai Meng, Li Song
JOURNAL OF TRANSPORTATION SAFETY & SECURITY
(2020)
