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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.
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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
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2
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Se C, Champahom T, Jomnonkwao S, Ratanavaraha V. Examining factors affecting driver injury severity in speeding-related crashes: a comparative study across driver age groups. Int J Inj Contr Saf Promot 2024; 31:234-255. [PMID: 38190335 DOI: 10.1080/17457300.2023.2300458] [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: 02/04/2023] [Accepted: 12/24/2023] [Indexed: 01/10/2024]
Abstract
This paper investigates the factors influencing the severity of driver injuries in single-vehicle speeding-related crashes, by comparing different driver age groups. This study employed a random threshold random parameter hierarchical ordered probit model and analysed crash data from Thailand between 2012 and 2017. The findings showed that young drivers face a heightened fatality risk when speeding in passenger cars or pickup trucks, hinting at the role of inexperience and risk-taking behaviours. Old drivers exhibit an increased fatality risk when speeding, especially in rainy conditions, on flush median roads, and during evening peak hours, attributed to reduced reaction times and vulnerability to adverse weather. Both young and elderly drivers face escalated fatality risks when speeding on road segments lacking guardrails during adverse weather, with older drivers being particularly vulnerable in rainy conditions. All age groups show an elevated fatality risk when speeding on barrier median roads, underscoring the significant role of speeding, which increases crash impact and limits margins of error and manoeuvrability, thereby highlighting the need for safety measures focusing on driver behaviour. These findings underscore the critical imperative for interventions addressing not only driver conduct but also road infrastructure, collectively striving to curtail the severity of speeding-related crashes.
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Affiliation(s)
- Chamroeun Se
- Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Thanapong Champahom
- Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, Nakhon Ratchasima, Thailand
| | - Sajjakaj Jomnonkwao
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Vatanavongs Ratanavaraha
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
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3
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Thombre A, Ghosh I, Agarwal A. Examining factors influencing the severity of motorized two-wheeler crashes in Delhi. Int J Inj Contr Saf Promot 2024; 31:111-124. [PMID: 37882684 DOI: 10.1080/17457300.2023.2267040] [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: 05/14/2023] [Accepted: 10/02/2023] [Indexed: 10/27/2023]
Abstract
Failure to meet road safety targets has necessitated urgent actions from stakeholders worldwide, especially in developing countries like India. Road safety of motorized two-wheelers (MTWs), one of India's most preferred travel modes for urban commutes, is in danger and witnessing threatening figures of fatalities and injuries. Most of the studies in the domain of MTW safety were conducted in developed countries, with very limited research in countries having a significant proportion of MTWs. The present work investigates police-reported crash data to identify the contributory factors of motorized two-wheeler crash severity. Data from MTW crash-prone areas were selected from Delhi, which is leading in road traffic fatalities among the million-plus urban cities in India. A binary logistic regression model was developed using the data for 2016-2018 period. The model results show that the odds of fatal motorized two-wheeler crashes increase when the following circumstances apply: crash occurs on underpasses; involves bus, truck, heavy motor vehicle (lorry, crane) as the striking vehicle; when hit-and-run type of crash occurs and when older age-group (> = 55) riders are involved. Finally, based on the findings, countermeasures were suggested to facilitate policymakers and traffic enforcement agencies, in improving the road safety situation of MTW users.
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Affiliation(s)
- Anurag Thombre
- Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Indrajit Ghosh
- Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Amit Agarwal
- Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India
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4
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Babaei Z, Metin Kunt M. A correlated random parameters ordered probit approach to analyze the injury severity of bicycle-motor vehicle collisions at intersections. ACCIDENT; ANALYSIS AND PREVENTION 2024; 196:107447. [PMID: 38157677 DOI: 10.1016/j.aap.2023.107447] [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: 06/20/2023] [Revised: 11/25/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
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.
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Affiliation(s)
- Zaniar Babaei
- Department of Civil Engineering, Eastern Mediterranean University (EMU), Gazimagusa, KKTC, Mersin 10, Turkey.
| | - Mehmet Metin Kunt
- Department of Civil Engineering, Eastern Mediterranean University (EMU), Gazimagusa, KKTC, Mersin 10, Turkey.
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5
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Li Z, Wang C, Liao H, Li G, Xu C. Efficient and robust estimation of single-vehicle crash severity: A mixed logit model with heterogeneity in means and variances. ACCIDENT; ANALYSIS AND PREVENTION 2024; 196:107446. [PMID: 38157676 DOI: 10.1016/j.aap.2023.107446] [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: 09/27/2023] [Revised: 11/16/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
This study delves into the factors that contribute to the severity of single-vehicle crashes, focusing on enhancing both computational speed and model robustness. Utilizing a mixed logit model with heterogeneity in means and variances, we offer a comprehensive understanding of the complexities surrounding crash severity. The analysis is grounded in a dataset of 39,788 crash records from the UK's STATS19 database, which includes variables such as road type, speed limits, and lighting conditions. A comparative evaluation of estimation methods, including pseudo-random, Halton, and scrambled and randomized Halton sequences, demonstrates the superior performance of the latter. Specifically, our estimation approach excels in goodness-of-fit, as measured by ρ2, and in minimizing the Akaike Information Criterion (AIC), all while optimizing computational resources like run time and memory usage. This strategic efficiency enables more thorough and credible analyses, rendering our model a robust tool for understanding crash severity. Policymakers and researchers will find this study valuable for crafting data-driven interventions aimed at reducing road crash severity.
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Affiliation(s)
- Zhenning Li
- State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering and Computer and Information Science, University of Macau, Macao Special Administrative Region of China.
| | - Chengyue Wang
- State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau, Macao Special Administrative Region of China
| | - Haicheng Liao
- State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau, Macao Special Administrative Region of China
| | - Guofa Li
- College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China
| | - Chengzhong Xu
- State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau, Macao Special Administrative Region of China.
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6
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Wang H, Cui P, Song D, Chen Y, Yang Y, Zhi D, Wang C, Zhu L, Yang X. Alternative approaches to modeling heterogeneity to analyze injury severity sustained by motorcyclists in two-vehicle crashes. ACCIDENT; ANALYSIS AND PREVENTION 2024; 195:107417. [PMID: 38061290 DOI: 10.1016/j.aap.2023.107417] [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: 04/07/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 12/30/2023]
Abstract
The presence of unobserved factors in the motorcycle involved two-vehicle crashes (MV) data could lead to heterogenous associations between observed factors and injury severity sustained by motorcyclists. Capturing such heterogeneities necessitates distinct methodological approaches, of which random and scale heterogeneity models are paramount. Herein, we undertake an empirical evaluation of random and scale heterogeneity models, exploring their efficacy in delineating the influence of external determinants on the degree of injury severity in crashes. Within the effects of scale heterogeneity, this study delves into two dominant models: the scaled multinomial logit model (S-MNL) and its generalized counterpart, the G-MNL, which encompasses both the S-MNL and the random parameters multinomial logit model (RPL). While the random heterogeneity domain is represented by the random parameters multinomial logit and an upgraded variant - the random parameters multinomial logit model with heterogeneity in means and variances (RPLHMV). Motorcycle involved two-vehicle crashes data were extracted from the UK STATS19 dataset from 2016 to 2020. Likelihood ratio tests are computed to assess the temporal variability of the significant factors. The test result demonstrates the temporal variations over a five-year study period. Some very important differences started to show up across the years based on the model estimation results: that the RPLHMV model statistically outperforms the G-MNL model in the 2016, 2018, and 2019 models, while the S-MNL model is statistically superior in the 2017 and 2020 years. These important findings suggest that the origin of heterogeneity in explaining factor weights can be captured by scale effects, not just random heterogeneity. In addition, the model results further show that motorcyclists' injury severities are significantly affected by motorcycle-related characteristics; there is the added factor of external influences, such as non-motorcycle drivers (males, young drivers, and elderly drivers) and vehicles (the moving status, age, and types of vehicles) that collide with motorcycles. The results of this paper are anticipated to help policymakers develop effective strategies to mitigate motorcycle involved two-vehicle crashes by implementing appropriate measures.
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Affiliation(s)
- Huanhuan Wang
- School of Economics and Management, Beijing Jiaotong University, Beijing 100044, PR China
| | - Pengfei Cui
- School of Systems Science, Beijing Jiaotong University, Beijing 100044, PR China.
| | - Dongdong Song
- School of Systems Science, Beijing Jiaotong University, Beijing 100044, PR China.
| | - Yan Chen
- School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, PR China
| | - Yitao Yang
- School of Systems Science, Beijing Jiaotong University, Beijing 100044, PR China
| | - Danyue Zhi
- School of Systems Science, Beijing Jiaotong University, Beijing 100044, PR China; TUM School of Engineering and Design, Technical University of Munich, Munich 80333, Germany
| | - Chenzhu Wang
- School of Transportation, Southeast University. 2 Sipailou, Nanjing, Jiangsu 210096, PR China
| | - Leipeng Zhu
- Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, PR China
| | - Xiaobao Yang
- School of Systems Science, Beijing Jiaotong University, Beijing 100044, PR China.
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Salehian A, Aghabayk K, Seyfi M, Shiwakoti N. Comparative analysis of pedestrian crash severity at United Kingdom rural road intersections and Non-Intersections using latent class clustering and ordered probit model. ACCIDENT; ANALYSIS AND PREVENTION 2023; 192:107231. [PMID: 37531856 DOI: 10.1016/j.aap.2023.107231] [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: 05/16/2023] [Revised: 07/08/2023] [Accepted: 07/20/2023] [Indexed: 08/04/2023]
Abstract
Pedestrian safety is a critical issue in the United Kingdom (UK) as pedestrians are the most vulnerable road users. Despite numerous studies on pedestrian-vehicle crashes globally, limited research has been conducted to explore the factors contributing to such incidents in the UK, especially on rural roads. Therefore, this study aimed to investigate the severity of pedestrian injuries sustained on rural roads in the UK, including crashes at intersections and non-intersections. We utilized the STATS19 dataset, which provided comprehensive road safety data from 2015 to 2019. To overcome the challenges posed by heterogeneity in the data, we employed a Latent Class Analysis to identify homogeneous clusters of crashes. Additionally, we utilized the Ordered Probit model to identify contributing factors within each cluster. Our findings revealed that various factors had distinct effects on the severity of pedestrian injuries at intersections and non-intersections. Several parameters like the pedestrian location in footway and one-way roads are only statistically significant in the intersection section. Certain factors such as the day of the week, the pedestrian's location in a refuge, and minor roads (class B roads) were found to be significant only in the non-intersection section.Parameters includingpedestrians aged over 65 years and under 15 years, drivers under 25 years, male drivers and pedestrians, darkness, heavy vehicles, speed limits exceeding 96 km/h (60 mph), major roads (class A roads), and single carriageway roadsare significant in both sections. The study proposes various measures to mitigate the severity of pedestrian-vehicle crashes, such as improving lighting conditions, enhancing pedestrian infrastructure, reducing speed limits in crash-prone areas, and promoting education and awareness among pedestrians and drivers. The findings and suggested measures could help policymakers and practitioners develop effective strategies and interventions to reduce the severity of these incidents and enhance pedestrian safety.
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Affiliation(s)
- Alireza Salehian
- School of Civil Engineering, College of Engineering, University of Tehran, Iran
| | - Kayvan Aghabayk
- School of Civil Engineering, College of Engineering, University of Tehran, Iran
| | - MohammadAli Seyfi
- School of Civil Engineering, College of Engineering, University of Tehran, Iran
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8
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Sun Z, Wang D, Gu X, Abdel-Aty M, Xing Y, Wang J, Lu H, Chen Y. A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes. ACCIDENT; ANALYSIS AND PREVENTION 2023; 192:107235. [PMID: 37557001 DOI: 10.1016/j.aap.2023.107235] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 07/12/2023] [Accepted: 07/23/2023] [Indexed: 08/11/2023]
Abstract
Vulnerable road users (VRUs) involved crashes are a major road safety concern due to the high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity models separately have limitations in crash data analysis. This study develops a hybrid approach of Random Forest based SHAP algorithm (RF-SHAP) and random parameters logit modeling framework to explore significant factors and identify the underlying interaction effects on injury severity of VRUs-involved crashes in Shenyang (China) from 2015 to 2017. The results show that the hybrid approach can uncover more underlying causality, which not only quantifies the impact of individual factors on injury severity, but also finds the interaction effects between the factors with random parameters and fixed parameters. Seven factors are found to have significant effect on crash injury severity. Two factors, including primary roads and rural areas produce random parameters. The interaction effects reveal interesting combination features. For example, even though rural areas and primary roads increase the likelihood of fatal crash occurrence individually, the interaction effect of the two factors decreases the likelihood of being fatal. The findings form the foundation for developing safety countermeasures targeted at specific crash groups for reducing fatalities in future crashes.
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Affiliation(s)
- Zhiyuan Sun
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
| | - Duo Wang
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
| | - Xin Gu
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China.
| | - Mohamed Abdel-Aty
- Department of Civil, Environmental and Construction Engineering, University of Central Florida Orlando, FL 32826-2450, United States
| | - Yuxuan Xing
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
| | - Jianyu Wang
- Beijing Key Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
| | - Huapu Lu
- Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China
| | - Yanyan Chen
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
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9
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Sun Z, Wang D, Gu X, Xing Y, Wang J, Lu H, Chen Y. A hybrid clustering and random forest model to analyse vulnerable road user to motor vehicle (VRU-MV) crashes. Int J Inj Contr Saf Promot 2023; 30:338-351. [PMID: 37643462 DOI: 10.1080/17457300.2023.2180804] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/28/2022] [Accepted: 02/11/2023] [Indexed: 02/24/2023]
Abstract
The main goal of this study is to investigate the unobserved heterogeneity in VRU-MV crash data and to determine the relatively important contributing factors of injury severity. For this end, a latent class analysis (LCA) coupled with random parameters logit model (LCA-RPL) is developed to segment the VRU-MV crashes into relatively homogeneous clusters and to explore the differences among clusters. The random-forest-based SHapley Additive exPlanation (RF-SHAP) approach is used to explore the relative importance of the contributing factors for injury severity in each cluster. The results show that, vulnerable group (VG), intersection or not (ION) and road type (RT) clearly distinguish the crash clusters. Moto-vehicle type and functional zone have significant impact on the injury severity among all clusters. Several variables (e.g. ION, crash type [CT], season and RT) demonstrate a significant effect in a specific sub-cluster model. Results of this study provide specific and insightful countermeasures that target the contributing factors in each cluster for mitigating VRU-MV crash injury severity.
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Affiliation(s)
- Zhiyuan Sun
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, PRChina
| | - Duo Wang
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, PRChina
| | - Xin Gu
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, PRChina
| | - Yuxuan Xing
- China Academy of Urban Planning and Design, Beijing, PRChina
| | - Jianyu Wang
- Beijing Key Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing, PRChina
| | - Huapu Lu
- Institute of Transportation Engineering, Tsinghua University, Beijing, PRChina
| | - Yanyan Chen
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, PRChina
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Wisutwattanasak P, Jomnonkwao S, Se C, Champahom T, Ratanavaraha V. Correlated random parameters model with heterogeneity in means for analysis of factors affecting the perceived value of road accidents and travel time. ACCIDENT; ANALYSIS AND PREVENTION 2023; 183:106992. [PMID: 36731255 DOI: 10.1016/j.aap.2023.106992] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
Road safety funding and management have become important issues in improving the quality of life of road users and there is evidence of a difference in driving behavior and the factor of road use between urban and rural areas, which is, in turn, reflected in different road safety evaluations. The purpose of this study is to assess the financial losses caused by road accidents on Thailand's highways and the related factors empirically, deploying the willingness-to-pay (WTP) approach. Data were obtained from 640 urban and 960 rural car drivers using a stated choice questionnaire and face-to-face interviews. This study used Correlated Random Parameters Binary Logit with Heterogeneity in Means (CRPBLHM) approach to analyze factors affecting WTP. According to the results, the value of a statistical life and injury for urban drivers was 1.63 times higher than that for rural drivers, and the value of travel time reduction per hour for urban drivers is ∼1.14 times higher than that found for rural drivers. Furthermore, the results of the CRPBLHM model reported that there are significant differences between urban and rural drivers' safety intentions and WTP. In the urban model, it was found that driving behavior (ticket, seatbelts, and speed) and weekday trips significantly increase the WTP, whereas household size and gender are unobserved characteristics among drivers. The rural model revealed that a driver's education (bachelor's and master's degrees held), and a necessary trip were significantly associated with drivers' valuation of safety; moreover, it was found that household size, sole earner status, own accident, in possession of a doctoral degree, and being young were significant in acting as unobserved characteristics. The results demonstrated differences in the value of road safety and unobserved heterogeneity among drivers, which influence risk perception and valuation with reference to the area context. Relevant agencies can use the results as a guideline for budget allocation and practical policy-related road safety management.
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Affiliation(s)
- Panuwat Wisutwattanasak
- Institute of Research and Development, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand.
| | - Sajjakaj Jomnonkwao
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand.
| | - Chamroeun Se
- Institute of Research and Development, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand.
| | - Thanapong Champahom
- Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, 744 Sura Narai Rd, Nai-Muang Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand.
| | - Vatanavongs Ratanavaraha
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand.
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11
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Champahom T, Se C, Jomnonkwao S, Boonyoo T, Leelamanothum A, Ratanavaraha V. Temporal Instability of Motorcycle Crash Fatalities on Local Roadways: A Random Parameters Approach with Heterogeneity in Means and Variances. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3845. [PMID: 36900855 PMCID: PMC10001501 DOI: 10.3390/ijerph20053845] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Motorcycle accidents can impede sustainable development due to the high fatality rate associated with motorcycle riders, particularly in developing countries. Although there has been extensive research conducted on motorcycle accidents on highways, there is a limited understanding of the factors contributing to accidents involving the most commonly used motorcycles on local roads. This study aimed to identify the root causes of fatal motorcycle accidents on local roads. The contributing factors consist of four groups: rider characteristics, maneuvers prior to the crash, temporal and environmental characteristics, and road characteristics. The study employed random parameters logit models with unobserved heterogeneity in means and variances while also incorporating the temporal instability principle. The results revealed that the data related to motorcycle accidents on local roads between 2018 and 2020 exhibited temporal variation. Numerous variables were discovered to influence the means and variances of the unobserved factors that were identified as random parameters. Male riders, riders over 50 years old, foreign riders, and accidents that occurred at night with inadequate lighting were identified as the primary factors that increased the risk of fatalities. This paper presents a clear policy recommendation aimed at organizations and identifies the relevant stakeholders, including the Department of Land Transport, traffic police, local government organizations, and academic groups.
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Affiliation(s)
- Thanapong Champahom
- Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand
| | - Chamroeun Se
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Sajjakaj Jomnonkwao
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Tassana Boonyoo
- Traffic and Transport Development and Research Center (TDRC), King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
| | - Amphaphorn Leelamanothum
- Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand
| | - Vatanavongs Ratanavaraha
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
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12
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Se C, Champahom T, Jomnonkwao S, Ratanavaraha V. Motorcyclist injury severity analysis: a comparison of Artificial Neural Networks and random parameter model with heterogeneity in means and variances. Int J Inj Contr Saf Promot 2022; 29:500-515. [PMID: 35666153 DOI: 10.1080/17457300.2022.2081985] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
In Thailand, the motorcyclist mortality rate is steadily on the rise and remains a serious concern for highway administrators and burden on both economic and local people. Using motorcycle-crash data in Thailand from 2016 to 2019, this study empirically employed and compared the Artificial Neural Networks (ANN) model and random parameters binary probit model with heterogeneity in means and variances (RPBPHM) to explore the effects of a wide range of associated risk characteristics on the severity outcomes of the motorcyclist. Study results revealed that probabilities of injury or fatal crash increase for crashes that involve male riders, riding with pillion, speeding, improper overtaking, riders under influence of alcohol, fatigue riders, undivided road and so on. The probability of non-injury crash increases for crashes on main or frontage traffic lane, four-lane road, concrete road, during rain, involving collision with other motorcycles, rear-end crashes, sideswipe crashes, single-motorcycle crashes and crashes within urban areas. The RPBPHM models were found to outperform the ANN model (quadratic support vector machine) in all performance metrics. The findings could potentially assist policymaker, safety professionals, practitioners, trainers, government agencies or highway designers in future planning and serve as guidance for mitigation policies directed at safety improvement for motorcyclists.
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Affiliation(s)
- Chamroeun Se
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Thanapong Champahom
- Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, Nakhon Ratchasima, Thailand
| | - Sajjakaj Jomnonkwao
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Vatanavongs Ratanavaraha
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
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13
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Se C, Champahom T, Jomnonkwao S, Karoonsoontawon A, Ratanavaraha V. Analysis of driver-injury severity: a comparison between speeding and non-speeding driving crash accounting for temporal and unobserved effects. Int J Inj Contr Saf Promot 2022; 29:475-488. [PMID: 35653656 DOI: 10.1080/17457300.2022.2081983] [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
This paper aims to investigate the differences between temporal stability of factors influencing driver-injury severities in crashes involving speeding and non-speeding driving using a six-year (2012-2017) crash data in Thailand. With two possible driver injury severity outcomes (no/minor and severe/fatal), random parameter binary logit models, that allow for heterogeneity in means and variances, were estimated to fully account for unobserved heterogeneities (i.e., allow crash-level factors to vary across crashes and to influence random parameter distribution). While most factors were unstable over time, speeding crash models result showed that stable factors decreasing probability of severe/fatal injury were restraint, van, passenger car, pickup truck, running-off-road on straight and hitting guardrail and mounting traffic island; whereas stable factor increasing probability of severe/fatal injury were central/eastern/southern regions. In non-speeding driving crash model, stable factors decreasing probability of severe/fatal injury were restraint, truck, and running-off-road on straight and hitting guardrail; whereas stable factors increasing probability of severe and fatal injury were under influence of alcohol and van. The findings of this research could potentially be utilized to improve highway safety and facilitate the development of more effective crash injury mitigation policies. Practical-related recommendation based on the results is also provided.
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Affiliation(s)
- Chamroeun Se
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Thanapong Champahom
- Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, Nakhon Ratchasima, Thailand
| | - Sajjakaj Jomnonkwao
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Ampol Karoonsoontawon
- Department of Civil Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
| | - Vatanavongs Ratanavaraha
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
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14
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Ijaz M, Liu L, Almarhabi Y, Jamal A, Usman SM, Zahid M. Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10526. [PMID: 36078241 PMCID: PMC9518049 DOI: 10.3390/ijerph191710526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/12/2022] [Accepted: 08/14/2022] [Indexed: 06/15/2023]
Abstract
Not wearing a helmet, not properly strapping the helmet on, or wearing a substandard helmet increases the risk of fatalities and injuries in motorcycle crashes. This research examines the differences in motorcycle crash injury severity considering crashes involving the compliance with and defiance of helmet use by motorcycle riders and highlights the temporal variation in their impact. Three-year (2017-2019) motorcycle crash data were collected from RESCUE 1122, a provincial emergency response service for Rawalpindi, Pakistan. The available crash data include crash-specific information, vehicle, driver, spatial and temporal characteristics, roadway features, and traffic volume, which influence the motorcyclist's injury severity. A random parameters logit model with heterogeneity in means and variances was evaluated to predict critical contributory factors in helmet-wearing and non-helmet-wearing motorcyclist crashes. Model estimates suggest significant variations in the impact of explanatory variables on motorcyclists' injury severity in the case of compliance with and defiance of helmet use. For helmet-wearing motorcyclists, key factors significantly associated with increasingly severe injury and fatal injuries include young riders (below 20 years of age), female pillion riders, collisions with another motorcycle, large trucks, passenger car, drivers aged 50 years and above, and drivers being distracted while driving. In contrast, for non-helmet-wearing motorcyclists, the significant factors responsible for severe injuries and fatalities were distracted driving, the collision of two motorcycles, crashes at U-turns, weekday crashes, and drivers above 50 years of age. The impact of parameters that predict motorcyclist injury severity was found to vary dramatically over time, exhibiting statistically significant temporal instability. The results of this study can serve as potential motorcycle safety guidelines for all relevant stakeholders to improve the state of motorcycle safety in the country.
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Affiliation(s)
- Muhammad Ijaz
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
| | - Lan Liu
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
| | - Yahya Almarhabi
- Center of Excellence in Trauma and Accidents, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Arshad Jamal
- Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia
| | - Sheikh Muhammad Usman
- Department of Civil Engineering, CECOS University of I.T. & Emerging Sciences, Peshawar 25000, Pakistan
| | - Muhammad Zahid
- College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
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15
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Lin HY, Li JS, Pai CW, Chien WC, Huang WC, Hsu CW, Wu CC, Yu SH, Chiu WT, Lam C. Environmental Factors Associated with Severe Motorcycle Crash Injury in University Neighborhoods: A Multicenter Study in Taiwan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10274. [PMID: 36011909 PMCID: PMC9407754 DOI: 10.3390/ijerph191610274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/20/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
University neighborhoods in Taiwan have high-volume traffic, which may increase motorcyclists' risk of injury. However, few studies have analyzed the environmental factors affecting motorcycle crash injury severity in university neighborhoods. In this multicenter cross-sectional study, we explored the factors that increase the severity of such injuries, especially among young adults. We retrospectively connected hospital data to the Police Traffic Accident Dataset. Areas within 500 m of a university were considered university neighborhoods. We analyzed 4751 patients, including 513 with severe injury (injury severity score ≥ 8). Multivariate analysis revealed that female sex, age ≥ 45 years, drunk driving, early morning driving, flashing signals, and single-motorcycle crashes were risk factors for severe injury. Among patients aged 18-24 years, female sex, late-night and afternoon driving, and flashing signals were risk factors. Adverse weather did not increase the risk. Time to hospital was a protective factor, reflecting the effectiveness of urban emergency medical services. Lifestyle habits among young adults, such as drunk driving incidents and afternoon and late-night driving, were also explored. We discovered that understanding chaotic traffic in the early morning, flashing signals at the intersections, and roadside obstacles is key for mitigating injury severity from motorcycle crashes in university neighborhoods.
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Affiliation(s)
- Heng-Yu Lin
- School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Jian-Sing Li
- Department of Emergency Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Chih-Wei Pai
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei 11031, Taiwan
| | - Wu-Chien Chien
- Department of Medical Research, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 11490, Taiwan
- School of Public Health, National Defense Medical Center, Taipei 11490, Taiwan
- Taiwanese Injury Prevention and Safety Promotion Association, Taipei 11490, Taiwan
| | - Wen-Cheng Huang
- Emergency Department, Department of Emergency and Critical Care Medicine, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan
- Department of Emergency, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Center for Education in Medical Simulation, Taipei Medical University, Taipei 11031, Taiwan
- Department of Education and Humanities in Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Chin-Wang Hsu
- Emergency Department, Department of Emergency and Critical Care Medicine, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan
- Department of Emergency, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Chia-Chieh Wu
- Department of Emergency, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Emergency Department, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan
| | - Shih-Hsiang Yu
- Institute of Transportation, Ministry of Transportation and Communications, Taipei 10548, Taiwan
| | - Wen-Ta Chiu
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei 11031, Taiwan
- AHMC Health System, Alhambra, CA 91801, USA
| | - Carlos Lam
- Department of Emergency, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Emergency Department, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan
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16
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Chan TC, Pai CW, Wu CC, Hsu JC, Chen RJ, Chiu WT, Lam C. Association of Air Pollution and Weather Factors with Traffic Injury Severity: A Study in Taiwan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19127442. [PMID: 35742691 PMCID: PMC9223547 DOI: 10.3390/ijerph19127442] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 02/01/2023]
Abstract
Exposure to air pollutants may elevate the injury severity scores (ISSs) for road traffic injuries (RTIs). This multicenter cross-sectional study aimed to investigate the associations between air pollution, weather conditions, and RTI severity. This retrospective study was performed in Taiwan in 2018. The location of each road traffic accident (RTA) was used to determine the nearest air quality monitoring and weather station, and the time of each RTA was matched to the corresponding hourly air pollutant concentration and weather factors. Five multiple logistic regression models were used to compute the risk of sustaining severe injury (ISS ≥ 9). Of the 14,973 patients with RTIs, 2853 sustained severe injury. Moderate or unhealthy air quality index, higher exposure to particulate matter ≤2.5 μm in diameter, bicyclists or pedestrians, greater road width, nighttime, and higher temperature and relative humidity were significant risk factors for severe injury. Exposure to nitrogen oxide and ozone did not increase the risk. Auto occupants and scene-to-hospital time were the protective factors. Sensitivity analyses showed consistent results between air pollutants and the risk of severe injury. Poor air quality and hot and humid weather conditions were associated with severe RTIs. Active commuters were at higher risk of sustaining severe RTI.
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Affiliation(s)
- Ta-Chien Chan
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei 11529, Taiwan;
- Institute of Public Health, School of Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Chih-Wei Pai
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei 11031, Taiwan; (C.-W.P.); (W.-T.C.)
| | - Chia-Chieh Wu
- Emergency Department, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan;
- Department of Emergency, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Jason C. Hsu
- International PhD Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei 10675, Taiwan;
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei 10675, Taiwan
- Research Center of Data Science on Healthcare Industry, College of Management, Taipei Medical University, Taipei 10675, Taiwan
| | - Ray-Jade Chen
- Division of General Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei 11031, Taiwan;
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 10675, Taiwan
| | - Wen-Ta Chiu
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei 11031, Taiwan; (C.-W.P.); (W.-T.C.)
- AHMC Health System, Alhambra, CA 91801, USA
| | - Carlos Lam
- Emergency Department, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan;
- Department of Emergency, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Correspondence:
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17
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What Factors Would Make Single-Vehicle Motorcycle Crashes Fatal? Empirical Evidence from Pakistan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105813. [PMID: 35627360 PMCID: PMC9140359 DOI: 10.3390/ijerph19105813] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/19/2022] [Accepted: 04/29/2022] [Indexed: 11/17/2022]
Abstract
The existing research on motorcycle safety has shown that single-vehicle motorcycle crashes (SVMC) account for a higher fatality rate than other types of crashes. Also, motorcycle safety has become one of the critical traffic safety issues in many developing countries, such as Pakistan, due to the growing number of motorcycles and lack of sufficient relevant infrastructure. However, the available literature on SVMC and motorcycle safety in developing countries is limited. Therefore, the present study attempted to investigate the factors that contribute to the injury severity of SVMC in a developing country, Pakistan. For this purpose, a random parameter logit model with heterogeneity in means and variances is developed using two years of data extracted from the road traffic injury research project in Karachi city, Pakistan. The study's findings show that the presence of pillion passengers and young motorcyclists indicators result in random parameters with heterogeneity in their means and variances. The study's results also reveal that the summer, morning time, weekends, older motorcyclists, collisions with fixed objects, speeding, and overtaking are positively, while younger motorcyclists and the presence of pillion passengers are negatively associated with fatal crashes. More importantly, in the particular Pakistan's context, female pillion passenger clothes trapped in the wheel, riding under the influence, intersections, U-turns, and collisions due to loss of control are also found to significantly influence the injury severity of SVMC. Based on these research findings, multiple appropriate countermeasures are recommended to enhance motorcycle safety in Pakistan and other developing countries with similar problems.
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18
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Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach. SUSTAINABILITY 2021. [DOI: 10.3390/su131810086] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.
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