1
|
Fu C, Tu HT. Investigating vehicle-vehicle and vehicle-pedestrian crash severity at street intersections with the latent class parameterized correlation bivariate generalized ordered probit. ACCIDENT; ANALYSIS AND PREVENTION 2024; 207:107745. [PMID: 39153423 DOI: 10.1016/j.aap.2024.107745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 08/05/2024] [Accepted: 08/05/2024] [Indexed: 08/19/2024]
Abstract
Street intersection crashes often involve two parties: either two vehicles hitting each other (i.e., a vehicle-vehicle crash) or a vehicle colliding with a pedestrian (i.e., a vehicle-pedestrian crash). In such crashes, the severity of injuries can vary considerably between the parties involved. It is necessary to understand the injuries of both parties simultaneously to identify the causality of a vehicle-pedestrian or two-vehicle crash. While the latent class ordinal model has been used in crash severity studies to capture heterogeneity in crash propensity, most of these studies are univariate, which is inappropriate for crashes involving two parties. This study proposes a latent class parameterized correlation bivariate generalized ordered probit (LCp-BGOP) model to examine 32,308 vehicle-vehicle and vehicle-pedestrian crashes at intersections in Taipei City, Taiwan. The model parameterizes thresholds and within-crash correlations of crash severity involving two parties and classifies these crashes into two distinct risk groups: the "Ordinary Crash Severity" (OCS) group and the "High Crash Severity" (HCS) group. The OCS group is mainly two-vehicle crashes involving motorcycles. The HCS group comprises vulnerable road users such as pedestrians and cyclists, mainly in mixed traffic with heavy volumes. The results also show that the effects of party-specific factors contributing to injury severity are greater than those of generic factors. Our study provides invaluable insight into intersection crashes, helping to reduce the severity of injuries in vehicle-vehicle and vehicle-pedestrian crashes.
Collapse
Affiliation(s)
- Chiang Fu
- Department of Transportation and Communication Management Science, National Cheng Kung University, Tainan, Taiwan.
| | - Hsin-Tung Tu
- Department of Transportation and Communication Management Science, National Cheng Kung University, Tainan, Taiwan
| |
Collapse
|
2
|
Yakubu MA, Aidoo EN, Ampofo RT, Ackaah W. Bivariate ordered probit modelling of motorcycle riders and pillion passengers' injury severities relationship and associated risk factors. Int J Inj Contr Saf Promot 2024; 31:499-507. [PMID: 38712985 DOI: 10.1080/17457300.2024.2349554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 04/06/2024] [Accepted: 04/26/2024] [Indexed: 05/08/2024]
Abstract
This study simultaneously modelled the injury severity of motorcycle riders and their pillion passengers and determine the associated risk factors. The analysis is based on motorcycle crashes data in Ashanti region of Ghana spanning from 2017 to 2019. The study implemented bivariate ordered probit model to identify the possible risk factors under the premise that the injury severity of pillion passenger is endogenously related to that of the rider in the event of crash. The model provides more efficient estimates by considered the common unobserved factors shared between rider and pillion passenger. The result shows a significant positive relationship between the two injury severities with a correlation coefficient of 0.63. Thus, the unobservable factors that increase the probability of the rider to sustain more severe injury in the event of crash also increase that of their corresponding pillion passenger. The rider and their pillion passenger injury severities have different propensity to some of the risk factors including passengers' gender, day of week, road width and light condition. In addition, the study found that time of day, weather condition, collision type, and number of vehicles involved in the crash jointly influence the injury severity of both rider and pillion passenger significantly.
Collapse
Affiliation(s)
- Mohammed A Yakubu
- Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Eric N Aidoo
- School of Mathematical and Computer Sciences, Heriot-Watt University, Dubai Campus, UAE
| | - Richard T Ampofo
- Department of Mathematics and Statistics, Wright State University, Dayton, OH, USA
| | - Williams Ackaah
- Division of Traffic and Transportation Engineering, Building and Road Research Institute of CSIR, Kumasi, Ghana
| |
Collapse
|
3
|
Mu F, Wang W, Liu L, Hu N, Wang F. Impact of omega-3 fatty acids supplementation on lipid levels in pregnant women with previous pregnancy losses: a retrospective longitudinal study. Front Nutr 2024; 11:1439599. [PMID: 39267857 PMCID: PMC11390446 DOI: 10.3389/fnut.2024.1439599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 08/19/2024] [Indexed: 09/15/2024] Open
Abstract
Objective This research aims to investigate the impact of omega-3 fatty acids supplementation on the lipid levels of pregnant women who have experienced pregnancy losses. Methods This retrospective study analyzed data from pregnant women with previous pregnancy losses from two medical centers. Their lipid profiles were measured at least twice during pregnancy. According to the use of omega-3 soft gel capsules, participants were divided into the omega-3 group and the control group. We assessed the relationship between omega-3 fatty acids supplementation and longitudinal lipid levels during pregnancy using generalized estimating equations (GEE). Subsequently, we conducted subgroup analyses to delineate the profile of beneficiaries who received omega-3 fatty acids based on body mass index (BMI), age, menstrual regularity, number of previous pregnancy losses, number of previous live births, and educational level. Results The omega-3 group included 105 participants, while the control group comprised 274 participants. Women in the omega-3 group started supplementation between 3.43 and 17.14 weeks of gestation. According to GEE analysis, supplementing omega-3 fatty acids significantly reduced triglyceride (TG) levels during pregnancy (adjusted β = -0.300, 95% CI -0.445 to -0.154, p < 0.001). No associations between omega-3 fatty acids supplementation and total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), or high-density lipoprotein cholesterol (HDL-C) levels were observed. Subgroup analyses revealed that omega-3 fatty acids supplementation was related to a reduction in TG levels among pregnant women with age of ≤35 years, a normal BMI (18.5-24.9 kg/m2), 1-2 previous pregnancy losses, no previous live births, or an educational level above high school. Conclusion Supplementation with omega-3 fatty acids may significantly reduce TG levels, yet it does not seem to improve TC, LDL-C, or HDL-C levels in pregnant women with previous pregnancy losses.
Collapse
Affiliation(s)
- Fangxiang Mu
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, China
| | - Weijing Wang
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, China
| | - Lin Liu
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, China
| | - Ning Hu
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, China
| | - Fang Wang
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, China
| |
Collapse
|
4
|
Gao D, Zhang X. Injury severity analysis of single-vehicle and two-vehicle crashes with electric scooters: A random parameters approach with heterogeneity in means and variances. ACCIDENT; ANALYSIS AND PREVENTION 2024; 195:107408. [PMID: 38043213 DOI: 10.1016/j.aap.2023.107408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 11/18/2023] [Accepted: 11/24/2023] [Indexed: 12/05/2023]
Abstract
In recent years, the electric scooter has become one of the most popular means of transportation on short trips. Due to the lag in the formulation of transportation policies and regulations, coupled with the increasing number of electric scooter crashes, there has been growing concern about the safety of pedestrians and electric scooter riders. For the first time in the extant literature, this study aims to analyze injury severity of electric scooter crashes by unobserved heterogeneity modeling approaches. A random parameters approach with heterogeneity in means and variances is utilized to examine the factors influencing injury severity, using data collected from the STATS19 road safety database. Electric scooter crashes are classified as single-vehicle crashes and two-vehicle crashes, with injury severity categorized into two groups: fatalities or serious injuries, and slight injuries. The model estimation was conducted by considering several variables including roadway, environment, temporality, vehicle, and rider characteristics, as well as second-party vehicle and driver characteristics and manners of collision specific to two-vehicle crashes. The results of the model estimation reveal that certain factors had relatively stable effects with the varying degree of crash injury severity outcomes in both single-vehicle crashes and two-vehicle crashes. These factors include nighttime incidents, weekdays, male riders, and an increase in rider age, all of which are associated with more severe injury outcomes. Moreover, the random parameters logit model with heterogeneity in means and variances is more flexible in accounting for unobserved heterogeneity and exhibits better goodness of fit. This study improves the understanding of electric scooter safety, and the finding can better inform public policy regarding electric scooter use to improve road safety and reduce injury severity of electric scooter crashes.
Collapse
Affiliation(s)
- Dongsheng Gao
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, People's Republic of China.
| | - Xiaoqiang Zhang
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, People's Republic of China; National Engineering Laboratory of Application Technology of Integrated Transportation Big Data, Southwest Jiaotong University, Chengdu 610031, People's Republic of China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 610031, People's Republic of China.
| |
Collapse
|
5
|
Song D, Yang X, Yang Y, Cui P, Zhu G. Bivariate joint analysis of injury severity of drivers in truck-car crashes accommodating multilayer unobserved heterogeneity. ACCIDENT; ANALYSIS AND PREVENTION 2023; 190:107175. [PMID: 37343458 DOI: 10.1016/j.aap.2023.107175] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/11/2023] [Accepted: 06/12/2023] [Indexed: 06/23/2023]
Abstract
Truck-involved crashes, especially truck-car crashes, are associated with serious and even fatal injuries, thus necessitating an in-depth analysis. Prior research focused solely on examining the injury severity of truck drivers or developed separate performance models for truck and car drivers. However, the severity of injuries to both drivers in the same truck-car crash may be interrelated, and influencing factors of injury severities sustained by the two parties may differ. To address these concerns, a random parameter bivariate probit model with heterogeneity in means (RPBPHM) is applied to examine factors affecting the injury severity of both drivers in the same truck-car crash and how these factors change over the years. Using truck-car crash data from 2017 to 2019 in the UK, the dependent variable is defined as slight injury and serious injury or fatality. Factors such as driver, vehicle, road, and environmental characteristics are statistically analyzed in this study. According to the findings, the RPBPHM model demonstrated a remarkable statistical fit, and a positive correlation was observed between the two drivers' injury severity in truck-car crashes. More importantly, the effects of the explanatory factors showing relatively temporal stability vary across different types of vehicle crashes. For example, car driver improper actions and lane changing by trucks, have a significant interactive effect on the severity of injuries sustained by drivers involved collisions between trucks and cars. Male truck drivers, young truck drivers, older truck drivers, and truck drivers' improper actions, elevate the estimated odds of only truck drivers; while older car and unsignalized crossing increase the possibility of injury severity of only car drivers. Finally, due to shared unobserved crash-specific factors, the 30-mph speed limit, dark no lights, and head-on collision, significantly affect the severity of injuries sustained by drivers involved in collisions between trucks and cars. The modeling approach provides a novel framework for jointly analyzing truck-involved crash injury severities. The findings will help policymakers take the necessary actions to reduce truck-car crashes by implementing appropriate and accurate safety countermeasures.
Collapse
Affiliation(s)
- Dongdong Song
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China.
| | - Xiaobao Yang
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China.
| | - Yitao Yang
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China; Department of Transport & Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg1, Delft 2628 CN, the Netherlands.
| | - Pengfei Cui
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China.
| | - Guangyu Zhu
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China.
| |
Collapse
|
6
|
Zhao W, Gong S, Zhao D, Liu F, Sze NN, Huang H. Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments. ACCIDENT; ANALYSIS AND PREVENTION 2023; 186:107053. [PMID: 37030178 DOI: 10.1016/j.aap.2023.107053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 01/31/2023] [Accepted: 03/28/2023] [Indexed: 06/19/2023]
Abstract
With the emerging connected vehicle (CV) technologies, a novel in-vehicle omni-direction collision warning system (OCWS) is developed. For example, vehicles approaching from different directions can be detected, and advanced collision warnings caused by vehicles approaching from different directions can be provided. Effectiveness of OCWS in reducing crash and injury related to forward, rear-end and lateral collision is recognized. However, it is rare that the effects of collision warning characteristics including collision types and warning types on micro-level driver behaviors and safety performance is assessed. In this study, variations in drivers' responses among different collision types and between visual only and visual plus auditory warnings are examined. In addition, moderating effects by driver characteristics including drivers' demographics, years of driving experience, and annual driving distance are also considered. An in-vehicle human-machine interface (HMI) that can provide both visual and auditory warnings for forward, rear-end, and lateral collisions is installed on an instrumented vehicle. 51 drivers participate in the field tests. Performance indicators including relative speed change, time taken to accelerate/decelerate, and maximum lateral displacement are adopted to reflect drivers' responses to collision warnings. Then, generalized estimation equation (GEE) approach is applied to examine the effects of drivers' characteristics, collision type, warning type and their interaction on the driving performance. Results indicate that age, year of driving experience, collision type, and warning type can affect the driving performance. Findings should be indicative to the optimal design of in-vehicle HMI and thresholds for the activation of collision warnings that can increase the drivers' awareness to collision warnings from different directions. Also, implementation of HMI can be customized with respect to individual driver characteristics.
Collapse
Affiliation(s)
- Wenjing Zhao
- School of Information and Engineering, Chang'an University, Xi'an 710064, China; Department of Civil & Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
| | - Siyuan Gong
- School of Information and Engineering, Chang'an University, Xi'an 710064, China.
| | - Dezong Zhao
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
| | - Fenglin Liu
- School of Information and Engineering, Chang'an University, Xi'an 710064, China
| | - N N Sze
- Department of Civil & Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
| | - Helai Huang
- School of Traffic and Transportation Engineering, Central South University, Changsha 410000, China
| |
Collapse
|
7
|
Yuan R, Gan J, Peng Z, Xiang Q. Injury severity analysis of two-vehicle crashes at unsignalized intersections using mixed logit models. Int J Inj Contr Saf Promot 2022; 29:348-359. [PMID: 35276053 DOI: 10.1080/17457300.2022.2040540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The severity of the two-vehicle crash is closely related to the characteristics of both the struck and striking vehicles. Ignoring vehicle roles may lead to biased results. Thus, this study used mixed logit models to determine the factors that influence injury severity in the two-vehicle crash, taking into account the vehicle characteristics of the different crash roles. The data used is collected from Pennsylvania Department of Transportation (PennDOT) Open Data Portal. First, the synthetic minority oversampling technique and nearest neighbors (SMOTE-ENN) strategy was selected to address the class imbalance problem of crash data. Then, two separated mixed logit models were developed for four- and three-legged unsignalized intersections. The results suggest that the type and movement of vehicles have significant effects on crash severity. For example, right-turn vehicles being struck can lead to more serious crashes than striking other vehicles. Large trucks striking other vehicles are found to increase crash severity, but being struck is found to decrease crash severity. Additionally, several factors were also identified to affect crash severity in both models and effective countermeasures suggestions were proposed to mitigate crash severity.Supplemental data for this article is available online at at .
Collapse
Affiliation(s)
- Renteng Yuan
- Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, Jiangsu, P. R. China
| | - Jing Gan
- School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Zhipeng Peng
- College of Transportation Engineering, Chang'an University, Xi'an, Shaanxi, P. R. China
| | - Qiaojun Xiang
- Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, Jiangsu, P. R. China
| |
Collapse
|
8
|
Farah H, Hagenzieker M, Brijs T. Special issue on road safety and simulation 2017. ACCIDENT; ANALYSIS AND PREVENTION 2021; 161:106384. [PMID: 34474335 DOI: 10.1016/j.aap.2021.106384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Affiliation(s)
- Haneen Farah
- Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, P.O. Box 5048, 2600 GA Delft, Netherlands.
| | - Marjan Hagenzieker
- Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, P.O. Box 5048, 2600 GA Delft, Netherlands
| | - Tom Brijs
- School of Transportation Sciences, UHasselt-Hasselt University, Agoralaan, 3590 Diepenbeek, Belgium
| |
Collapse
|