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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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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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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.
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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.
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Cai Z, Wei F, Guo Y. A full Bayesian multilevel approach for modeling interaction effects in single-vehicle crashes. ACCIDENT; ANALYSIS AND PREVENTION 2023; 193:107331. [PMID: 37783161 DOI: 10.1016/j.aap.2023.107331] [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: 05/26/2023] [Revised: 08/30/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023]
Abstract
Interaction effects constitute crucial crash attributes that can be classified into two distinct categories: spatiotemporal interactions and factor interactions. These interactions are rarely addressed systematically in modeling the severity of single-vehicle (SV) crashes. This study focuses on uncovering these crash attributes by designing a full Bayesian spatiotemporal interaction multilevel logit (STIML-logit) approach with heterogeneity in means and variances (HMV). Meanwhile, a nested Gaussian conditional autoregressive (CAR) structure is proposed to fit the spatiotemporal interaction component and its effectiveness is verified by calibrating four different interaction patterns. A standard multilevel logit (with and without HMV), a multilevel logit with HMV, and a spatiotemporal multilevel logit with HMV are constructed for comparison. Risk factors are decomposed into traffic environment factors (group level) and individual crash factors (case level) to construct a multilevel structure and to capture possible interactions between risk factors from different levels (cross-level factor interactions). We perform regression modeling utilizing SV crash cases covering 96 major urban roads in Shandong, China. The modeling results underscore several significant findings: (1) the STIML-logit with HMV demonstrates the best regression performance, suggesting that systematically dealing with the interaction effects and the HMV is a trustworthy modeling perspective; (2) crash models with the nested CAR outperform those with the traditional CAR and the result is supported by all the spatiotemporal statistical functions, highlighting the potential advantages of the nested structure; (3) all the environment factors maintain significant interactions with the case factors, highlighting that the contribution of the environment factors to crash injuries is not constant but is rather influenced by the specific case-related crash factors. The study introduces a promising regression architecture for modeling crash injuries and revealing subtle crash attributes.
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Affiliation(s)
- Zhenggan Cai
- ITS Research Center, Wuhan University of Technology, Wuhan, PR China; School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, PR China.
| | - Fulu Wei
- School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, PR China.
| | - Yongqing Guo
- School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, PR China
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Se C, Champahom T, Wisutwattanasak P, Jomnonkwao S, Ratanavaraha V. Temporal instability and differences in injury severity between restrained and unrestrained drivers in speeding-related crashes. Sci Rep 2023; 13:9756. [PMID: 37328518 PMCID: PMC10276048 DOI: 10.1038/s41598-023-36906-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 06/12/2023] [Indexed: 06/18/2023] Open
Abstract
Upon detecting a crash impact, the vehicle restraint system locks the driver in place. However, external factors such as speeding, crash mechanisms, roadway attributes, vehicle type, and the surrounding environment typically contribute to the driver being jostled within the vehicle. As a result, it is crucial to model unrestrained and restrained drivers separately to reveal the true impact of the restraint system and other factors on driver injury severities. This paper aims to explore the differences in factors affecting injury severity for seatbelt-restrained and unrestrained drivers involved in speeding-related crashes while accounting for temporal instability in the investigation. Utilizing crash data from Thailand between 2012 and 2017, mixed logit models with heterogeneity in means and variances were employed to account for multi-layered unobserved heterogeneity. For restrained drivers, the risk of fatal or severe crashes was positively associated with factors such as male drivers, alcohol influence, flush/barrier median roadways, sloped roadways, vans, running off the roadway without roadside guardrails, and nighttime on unlit or lit roads. For unrestrained drivers, the likelihood of fatal or severe injuries increased in crashes involving older drivers, alcohol influence, raised or depressed median roadways, four-lane roadways, passenger cars, running off the roadway without roadside guardrails, and crashes occurring in rainy conditions. The out-of-sample prediction simulation results are particularly significant, as they show the maximum safety benefits achievable solely by using a vehicle's seatbelt system. Likelihood ratio test and predictive comparison findings highlight the considerable combined impact of temporal instability and the non-transferability of restrained and unrestrained driver injury severities across the periods studied. This finding also demonstrates a potential reduction in severe and fatal injury rates by simply replicating restrained driver conditions. The findings should be of value to policymakers, decision-makers, and highway engineers when developing potential countermeasures to improve driver safety and reduce the frequency of severe and fatal speeding-related single-vehicle crashes.
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Affiliation(s)
- Chamroeun Se
- Institute of Research and Development, Suranaree University of Technology, 111, University Avenue, Suranaree, Muang Nakhon Ratchasima, 30000, Thailand
| | - Thanapong Champahom
- Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, 744 Sura Narai Rd, Nai-Muang, Muang Nakhon Ratchasima, 30000, Thailand
| | - Panuwat Wisutwattanasak
- Institute of Research and Development, Suranaree University of Technology, 111, University Avenue, Suranaree, Muang Nakhon Ratchasima, 30000, Thailand
| | - Sajjakaj Jomnonkwao
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111, University Avenue, Suranaree, Muang Nakhon Ratchasima, 30000, Thailand.
| | - Vatanavongs Ratanavaraha
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111, University Avenue, Suranaree, Muang Nakhon Ratchasima, 30000, Thailand
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Li Z, Liao H, Tang R, Li G, Li Y, Xu C. Mitigating the impact of outliers in traffic crash analysis: A robust Bayesian regression approach with application to tunnel crash data. ACCIDENT; ANALYSIS AND PREVENTION 2023; 185:107019. [PMID: 36907031 DOI: 10.1016/j.aap.2023.107019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/05/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
Traffic crash datasets are often marred by the presence of anomalous data points, commonly referred to as outliers. These outliers can have a profound impact on the results obtained through the application of traditional methods such as logit and probit models, commonly used in the domain of traffic safety analysis, resulting in biased and unreliable estimates. To mitigate this issue, this study introduces a robust Bayesian regression approach, the robit model, which utilizes a heavy-tailed Student's t distribution to replace the link function of these thin-tailed distributions, effectively reducing the influence of outliers on the analysis. Furthermore, a sandwich algorithm based on data augmentation is proposed to enhance the estimation efficiency of posteriors. The proposed model is rigorously tested using a dataset of tunnel crashes, and the results demonstrate its efficiency, robustness, and superior performance compared to traditional methods. The study also reveals that several factors such as night and speeding have a significant impact on the injury severity of tunnel crashes. This research provides a comprehensive understanding of the outliers treatment methods in traffic safety studies and offers valuable recommendations for the development of appropriate countermeasures to effectively prevent severe injuries in tunnel crashes.
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Affiliation(s)
- Zhenning Li
- State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau SAR 999078, China.
| | - Haicheng Liao
- State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau, Macau SAR 999078, China
| | - Ruru Tang
- State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau SAR 999078, China
| | - Guofa Li
- College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
| | - Yunjian Li
- Institute of Applied Physics and Materials Engineering, University of Macau, Macao SAR 999078, China
| | - Chengzhong Xu
- State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau, Macau SAR 999078, China
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Cai Z, Wu X. Modeling spatiotemporal interactions in single-vehicle crash severity by road types. JOURNAL OF SAFETY RESEARCH 2023; 85:157-171. [PMID: 37330866 DOI: 10.1016/j.jsr.2023.01.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 10/04/2022] [Accepted: 01/31/2023] [Indexed: 06/19/2023]
Abstract
INTRODUCTION Spatiotemporal correlations have been widely recognized in single-vehicle (SV) crash severity analysis. However, the interactions between them are rarely explored. The current research proposed a spatiotemporal interaction logit (STI-logit) model to regression SV crash severity using observations in Shandong, China. METHOD Two representative regression patterns-mixture component and Gaussian conditional autoregression (CAR)-were employed separately to characterize the spatiotemporal interactions. Two existing statistical techniques-spatiotemporal logit and random parameters logit-were also calibrated and compared with the proposed approach with the aim of highlighting the best one. In addition, three road types-arterial road, secondary road, and branch road-were modeled separately to clarify the variable influence of contributors on crash severity. RESULTS The calibration results indicate that the STI-logit model outperforms other crash models, highlighting that comprehensively accommodating spatiotemporal correlations and their interactions is a recommended crash modeling approach. Additionally, the STI-logit using mixture component fits crash observations better than that using Gaussian CAR and this finding remains stable across road types, suggesting that simultaneously accommodating stable and unstable spatiotemporal risk patterns can further strengthen model fit. According to the significance of risk factors, there is a significant positive correlation between distracted diving, drunk driving, motorcycle, dark (without street lighting), and collision with fixed object and serious SV crashes. Truck and collision with pedestrian significantly mitigate the likelihood of serious SV crashes. Interestingly, the coefficient of roadside hard barrier is significant and positive in branch road model, but it is not significant in arterial road model and secondary road model. PRACTICAL APPLICATIONS These findings provide a superior modeling framework and various significant contributors, which are beneficial for mitigating the risk of serious crashes.
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Affiliation(s)
- Zhenggan Cai
- Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430000, PR China.
| | - Xiaoyan Wu
- Department of Transportation Engineering, Shandong University of Technology, Zibo 255000, PR China
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Cai Z, Wei F. Modelling injury severity in single-vehicle crashes using full Bayesian random parameters multinomial approach. ACCIDENT; ANALYSIS AND PREVENTION 2023; 183:106983. [PMID: 36696745 DOI: 10.1016/j.aap.2023.106983] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/10/2023] [Accepted: 01/17/2023] [Indexed: 06/17/2023]
Abstract
Single-vehicle (SV) crash severity model considering spatiotemporal correlations has been extensively investigated, but spatiotemporal interactions have not received sufficient attention. This research is dedicated to propose a superior spatiotemporal interaction correlated random parameters logit approach with heterogeneity in means and variances (STICRP-logit-HMV) for systematically characterizing unobserved heterogeneity, spatiotemporal correlations, and spatiotemporal interactions. Four flexible interaction formulations are developed to uncover the spatiotemporal interactions, including linear structure, Kronecker product, mixture-2 model, and mixture-5 model. Four candidate approaches-random parameters logit (RP-logit), RP-logit with heterogeneity in means and variances (RP-logit-HMV), correlated RP-logit-HMV (CRP-logit-HMV), and spatiotemporal CRP-logit-HMV (STCRP-logit-HMV)-are also established and compared with the proposed model. SV crash observations in Shandong Province, China, are employed to calibrate regression parameters. The model comparison results show that (1) the performance of the RP-logit-HMV model outperforms the RP-logit model, implying that capturing heterogeneity in the means and variances can strengthen model fit; (2) the CRP-logit-HMV model and the RP-logit-HMV model are comparable; (3) the STCRP-logit-HMV model outperforms the CRP-logit-HMV model, implying that addressing the spatiotemporal crash mechanisms is beneficial to the overall fitting of the crash model; (4) the STICRP-logit-HMV model performs better than the STCRP-logit-HMV model and this finding remains stable across different interaction formulations, indicating that comprehensively reflecting the spatiotemporal correlations and their interactions is a promising approach to model SV crashes. Among the four interaction models, the STICRP-logit-HMV model with mixture-5 component maintains the best fit, which is a recommended approach to model crash severity. The regression coefficients for young driver, male driver, and non-dry road surface are random across observations, suggesting that the influence of these factors on SV crash severity maintains significant heterogeneity effects. The research results provide transportation professionals with a superior statistical framework for diagnosing crash severity, which is beneficial for improving traffic safety.
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Affiliation(s)
- Zhenggan Cai
- Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430000, PR China; School of Transportation, Shandong University of Technology, Zibo 255000, PR China.
| | - Fulu Wei
- School of Transportation, Shandong University of Technology, Zibo 255000, PR China.
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Wen H, Ma Z, Chen Z, Luo C. Analyzing the impact of curve and slope on multi-vehicle truck crash severity on mountainous freeways. ACCIDENT; ANALYSIS AND PREVENTION 2023; 181:106951. [PMID: 36586161 DOI: 10.1016/j.aap.2022.106951] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/10/2022] [Accepted: 12/25/2022] [Indexed: 06/17/2023]
Abstract
Many studies examine the road characteristics that impact the severity of truck crash accidents. However, some only analyze the effect of curves or slopes separately, ignoring their combination. Therefore, there are nine types of the combination of curve and slope in this study. The combination of curve and slope factor that affected the injury severity of truck crashes on mountainous freeways was examined using a correlated random parameter logit model. This method is applied to evaluate the correlation between the random parameters and those that exhibit unobserved heterogeneity. Also, the multinomial logit model and traditional random parameter logit model are used. The study's data were collected from multi-vehicle truck crashes on mountainous freeways in China. The results showed that the correlated random parameters logit model was better than the others. In addition, they demonstrated a correlation between the random parameters. Based on the estimation coefficients and marginal effects, the combination of curve and slope has a great influence on the injury severity of truck crashes. The main finding is that curve with medium radius and medium slope will significantly increase the probability of medium severity comparing to curve with high radius and flat slope. On the other hand, the injury severity of truck accidents was significantly impacted by crash type, vehicle type, surface condition, time of day, season, lighting condition, pavement type, and guardrail. Variables such as sideswipe, head-on, medium trucks, morning, dawn or dusk and summertime reduced the probability of truck crashes. Rollover, winter, gravel, and guardrail variables increased the risk of truck crashes. Correlations were also discovered between a rollover and dry surface condition and rollover and gravel pavement type. The research findings will help traffic officials determine effective countermeasures to decrease the severity of truck crashes on mountainous freeways.
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Affiliation(s)
- Huiying Wen
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641 PR China.
| | - Zhaoliang Ma
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641 PR China.
| | - Zheng Chen
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641 PR China.
| | - Chenwei Luo
- Guangzhou Transport Planning Research Institute Co., LTD, Guangzhou, Guangdong 510030 PR China.
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Adeyemi O, Paul R, Delmelle E, DiMaggio C, Arif A. Road environment characteristics and fatal crash injury during the rush and non-rush hour periods in the U.S: Model testing and cluster analysis. Spat Spatiotemporal Epidemiol 2023; 44:100562. [PMID: 36707195 DOI: 10.1016/j.sste.2022.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 10/13/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
This study aims to assess the relationship between county-level fatal crash injuries and road environmental characteristics at all times of the day and during the rush and non-rush hour periods. We merged eleven-year (2010 - 2020) data from the Fatality Analysis Reporting System. The outcome variable was the county-level fatal crash injury counts. The predictor variables were measures of road types, junction types and work zone, and weather types. We tested the predictiveness of two nested negative binomial models and adjudged that a nested spatial negative binomial regression model outperformed the non-spatial negative binomial model. The median county crash mortality rates at all times of the day and during the rush and non-rush hour periods were 18.4, 7.7, and 10.4 per 100,000 population, respectively. Fatal crash injury rate ratios were significantly elevated on interstates and highways at all times of the day - rush and non-rush hour periods inclusive. Intersections, driveways, and ramps on highways were associated with elevated fatal crash injury rate ratios. Clusters of high fatal crash injury rates were observed in counties located in Montana, Nevada, Colorado, Kansas, New Mexico, Oklahoma, Texas, Arkansas, Mississippi, Alabama, Georgia, and Nevada. The built and natural road environment factors are associated with county-level fatal crash injuries during the rush and non-rush hour periods. Understanding the association of road environment characteristics and the cluster distribution of fatal crash injuries may inform areas in need of focused intervention.
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Affiliation(s)
- Oluwaseun Adeyemi
- Department of Public Health Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA.
| | - Rajib Paul
- Department of Public Health Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA; School of Data Science, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
| | - Eric Delmelle
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA; Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu Campus, P.O.Box 111, FI-80101 Finland.
| | - Charles DiMaggio
- Department of Public Health Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA; Department of Surgery, NYU Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA; Department of Population Health, NYU Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Ahmed Arif
- Department of Public Health Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
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Sawtelle A, Shirazi M, Garder PE, Rubin J. Driver, roadway, and weather factors on severity of lane departure crashes in Maine. JOURNAL OF SAFETY RESEARCH 2023; 84:306-315. [PMID: 36868659 DOI: 10.1016/j.jsr.2022.11.006] [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: 01/24/2022] [Revised: 09/03/2022] [Accepted: 11/09/2022] [Indexed: 06/18/2023]
Abstract
INTRODUCTION In Maine, lane departure crashes account for over 70% of roadway fatalities. The majority of roadways in Maine are rural. Moreover, Maine has aging infrastructure, houses the oldest population in the United States, and experiences the third coldest weather in the United States. METHODS This study analyzes the impact of roadway, driver, and weather factors on the severity of single-vehicle lane departure crashes occurring from 2017 to 2019 on rural roadways in Maine. Rather than using police reported weather, weather station data were utilized. Four facility types: Interstates, minor arterials, major collectors, and minor collectors were considered for analysis. The Multinomial Logistic Regression model was used for the analysis. The property damage only (PDO) outcome was considered as the reference (or base) category. RESULTS The modeling results show that the odds of a crash leading to major injury or fatality (KA outcome) increases by 330%, 150%, 243%, and 266% for older drivers (65 or above) compared to young drivers (29 or less) on Interstates, minor arterials, major collectors, and minor collectors, respectively. During the winter period (October to April), the odds of KA severity outcome (with respect to the PDO) decreases by 65%, 65%, 65%, and 48% on Interstates, minor arterials, major collectors, and minor collectors, respectively, presumably due to reduced speeds during winter weather events. CONCLUSION In Maine, factors such as older drivers, operating under the influence, speeding, precipitation, and not wearing a seatbelt showed higher odds of leading to injury. PRACTICAL APPLICATIONS This study provides safety analysts and practitioners in Maine a comprehensive study of factors that influence the severity of crashes in Maine at different facilities to improve maintenance strategies, enhance safety using proper safety countermeasures, or increase awareness across the state.
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Affiliation(s)
- Alainie Sawtelle
- Department of Civil and Environmental Engineering, University of Maine, Orono, ME 04469, United States.
| | - Mohammadali Shirazi
- Department of Civil and Environmental Engineering, University of Maine, Orono, ME 04469, United States.
| | - Per Erik Garder
- Department of Civil and Environmental Engineering, University of Maine, Orono, ME 04469, United States.
| | - Jonathan Rubin
- School of Economics, University of Maine, Orono, ME 04469, United States.
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Wang C, He J, Yan X, Zhang C, Chen Y, Ye Y. Temporal-spatial evolution analysis of severe traffic violations using three functional forms of models considering the diurnal variation of meteorology. ACCIDENT; ANALYSIS AND PREVENTION 2022; 174:106731. [PMID: 35696853 DOI: 10.1016/j.aap.2022.106731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 05/05/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Traffic violations and crashes are inherently associated. Analysis of traffic violation frequency is a prerequisite for improvements in crash prevention and corresponding countermeasures. One of the essential works in the field of traffic violations relates to the exploration of the correlations between a certain violation type (e.g., speeding or safety belt use) and its causal factors (e.g., demographics and road types). Till now, the effects of spatiotemporal and meteorological factors on severe traffic violations, a general term for dangerous driving behaviors, have not been fully considered. Using the dataset consisting of daily severe traffic violations and meteorological conditions during 12 months in Jiangsu Province, China, violation performance functions were developed for three violation types (total violations, driving under the influence, and speeding) based on three models (Poisson regression, zero-inflated Poisson regression, and negative binomial model). The findings indicate that the negative binomial model has a better performance for traffic violation frequency estimation. Additionally, elastic analysis for three violation types relying on the negative binomial model was conducted to present the relationships between the explanatory variables and the expected violation frequency. The effects of spatiotemporal factors have revealed that the violation situations are significantly different in varying cities and the frequency of drunk driving shows a significant time instability. It is also found that rainy days will generate a decrease in the possibility of violation occurrence. With regard to temperature, a significant negative effect is found and the decrease in temperature will bring about an increase in violation frequency. Besides, traffic violation frequency is significantly increased during holidays with comfortable weather conditions. The conclusion of this study can provide insightful suggestions for the department of traffic enforcement to adjust the patrol plans according to the specified periods (weeks, months, or holidays) and weather conditions. Special rectification actions and targeted educational activities are also advised to be put forward simultaneously.
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Affiliation(s)
- Chenwei Wang
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing 210096, PR China.
| | - Jie He
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing 210096, PR China.
| | - Xintong Yan
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing 210096, PR China.
| | - Changjian Zhang
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing 210096, PR China.
| | - Yikai Chen
- School of Automotive and Transportation Engineering, Hefei University of Technology, 193 # Tunxi Road, 230009 Hefei, PR China.
| | - Yuntao Ye
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing 210096, PR China.
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Analysis of Crash Severity of Texas Two Lane Rural Roads Using Solar Altitude Angle Based Lighting Condition. SUSTAINABILITY 2022. [DOI: 10.3390/su14031692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Many studies have examined the impact of factors affecting accident severity in rural areas; however, little attention has been paid to different lighting conditions (LCs), and less to the detailed categories and precise determining of twilight. In this paper, solar altitude angle (SAA), as a basis for differentiating and categorizing LCs, is proposed to investigate explanatory variables in much greater detail. For each LC, namely, dark, twilight, dark lit (dark with street lights) and daylight, separate random parameter models are developed to investigate the impacts of some factors on crash injury severity data of 2017 and 2018 in two lane rural roads of Texas. The model estimation results indicated that different LCs have various contributing factors, indeed, to each injury severity, further stressing the significance of investigating crashes based on SAA. The key differences include crash location, marked lane, grade direction, no passing zone, shoulder width, weekday and collision type. The important findings were that developing artificial lighting at intersections and LED raised pavement markers on two lane rural roads could lead to enhanced road safety under dark LCs. Furthermore, increasing shoulder width in straight segments of two lane rural roads is important for decreasing severe injury in daylight conditions.
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14
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Chen Y, Luo R, King M, Shi Q, He J, Hu Z. Spatiotemporal analysis of crash severity on rural highway: A case study in Anhui, China. ACCIDENT; ANALYSIS AND PREVENTION 2022; 165:106538. [PMID: 34922106 DOI: 10.1016/j.aap.2021.106538] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 11/30/2021] [Accepted: 12/07/2021] [Indexed: 06/14/2023]
Abstract
Traffic crashes are the result of the interaction between human activities and different socio-economic, geographical, and environmental factors, showing a temporal and spatial relationship. The temporal and spatial correlations must be characterized in crash severity studies, for which the geographically and temporally weighted ordered logistic regression (GTWOLR) model is an effective approach. However, existing studies using the GTWOLR model only subjectively selected a type of kernel function and kernel bandwidth, which cannot determine the best expression of the spatiotemporal relationship between crashes. This paper explores the optimal kernel function and kernel bandwidth considering the aforementioned problem to obtain the best GTWOLR model to analyze the crash data based on the crash data of rural highways in Anhui Province, China, from 2014 to 2017. First, the GTWOLR models with Gaussian or Bi-square kernel function and fixed (the spatiotemporal distance remains constant of local sample) or adaptive (the quantity of the local sample is constant) bandwidth are compared. Second, the log-likelihood and Akaike information criterion are used to compare the GTWOLR model with the ordered logistic regression (OLR) model. Finally, the spatial and temporal characteristics of the contributing factors in the best GTWOLR model are analyzed, and corresponding countermeasures for improving traffic safety on rural highways are proposed. Model comparison results reveal that although the difference was insignificant, the Bi-square kernel function with fixed bandwidth (BF)- GTWOLR model has a better goodness of fit than the GTWOLR models with other types of kernel function and bandwidth and the OLR model. The BF-GTWOLR model estimation results showed that eight factors, including pedestrian-vehicle crash, middle-aged driver, hit-and-run, truck, motorcycle, curve, slope and mountainous, passed the non-stationary test, indicating their varying effects on the crash severity across space and over time. As a crash severity modeling approach that effectively quantifies the spatiotemporal relationships in crashes, the BF-GTWOLR model, which adapts to crash data, may have implications for future research. In addition, the findings of this paper can help traffic management departments to propose progressive and targeted policies or countermeasures, so as to reduce the severity of rural highway crashes.
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Affiliation(s)
- Yikai Chen
- School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China.
| | - Renjia Luo
- School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China; Anhui Provincial Traffic Survey and Design Institute Co., Hefei, Anhui, China.
| | - Mark King
- Centre for Accident Research and Road Safety-Queensland, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.
| | - Qin Shi
- School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China
| | - Jie He
- School of Transportation, Southeast University, Nanjing, Jiangsu, China
| | - Zongpin Hu
- School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China
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15
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Wang L, Li R, Wang C, Liu Z. Driver injury severity analysis of crashes in a western China's rural mountainous county: Taking crash compatibility difference into consideration. JOURNAL OF TRAFFIC AND TRANSPORTATION ENGINEERING (ENGLISH EDITION) 2021. [DOI: 10.1016/j.jtte.2020.12.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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16
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Hosseinpour M, Haleem K. Examining crash injury severity and barrier-hit outcomes from cable barriers and strong-post guardrails on Alabama's interstate highways. JOURNAL OF SAFETY RESEARCH 2021; 78:155-169. [PMID: 34399911 DOI: 10.1016/j.jsr.2021.06.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/24/2021] [Accepted: 06/17/2021] [Indexed: 06/13/2023]
Abstract
INTRODUCTION This study investigates the impact of several risk factors (i.e., roadway, driver, vehicle, environmental, and barrier-specific characteristics) on the injury severity resulting from barrier-related crashes and also on barrier-hit outcomes (i.e., vehicle containment, vehicle redirection, and barrier penetration). A total of 1,685 barrier-related crashes, which occurred on three major interstate highways (I-65, I-85, and I-20) in the state of Alabama, were collected for a seven-year period (2010-2016), and all relevant information from the police reports was reviewed. Features that were rarely explored before (e.g., median width, barrier length, barrier offset or lateral position, left shoulder width, blockout type, and number of cables) were also collected and examined. Two types of longitudinal barriers were analyzed: high-tension cable barriers installed on medians and strong-post guardrails installed on medians and/or roadsides. METHOD Two separate mixed logit (MXL) models were used to analyze crash injury severity in median and roadside barrier-related crashes. Two additional MXL models were separately adopted for median and roadside barrier-related crashes to estimate the probability of three barrier-hit outcomes (vehicle containment, vehicle redirection, and barrier penetration). RESULTS The results of crash injury severity MXL models showed that, for both median and roadside barrier crashes, barrier penetration, female drivers, and driver fatigue were associated with a higher probability of injury or fatal crashes. The results of barrier-hit MXL models showed that longer barrier length, Brifen cable barrier system, and barrier lateral position were significant predictors of median barrier-hit outcomes, whereas dark lighting condition, driving under the influence (DUI), presence of curved freeway sections, and right shoulder width significantly contributed to roadside barrier-hit outcomes. CONCLUSIONS The MXL model succeeded in identifying several contributing factors of crash severity and barrier-hit outcomes along Alabama's interstate highways. Practical applications: One study application is to design longer barrier run length (greater than 1230 feet or 0.2 miles) to reduce the barrier penetration likelihood.
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Affiliation(s)
- Mehdi Hosseinpour
- School of Engineering & Applied Sciences, Western Kentucky University, 1906 College Heights Blvd, EBS 2122, Bowling Green, KY 42101, United States.
| | - Kirolos Haleem
- School of Engineering & Applied Sciences, Western Kentucky University, 1906 College Heights Blvd, EBS 2122, Bowling Green, KY 42101, United States
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Zhang C, He J, Yan X, Liu Z, Chen Y, Zhang H. Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types. JOURNAL OF SAFETY RESEARCH 2021; 78:80-95. [PMID: 34399934 DOI: 10.1016/j.jsr.2021.05.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 12/08/2020] [Accepted: 05/27/2021] [Indexed: 06/13/2023]
Abstract
INTRODUCTION Freeway accidents are a leading cause of death in China, which also triggers substantial economic loss and an emotional burden to society. However, the internal mechanism of how microscopic kinetic parameters of vehicles influenced by road characteristics determine the occurrence of different types of accidents has not been explicitly studied. This research aimed to explore the "link role" of tire microscopic kinetic parameters in road characteristic variables and traffic accidents to aid in facilitating the traffic design and management, and thus to prevent traffic accident. METHOD A mountain freeway in Zhejiang Province, China was used as the research object and the data used in this paper were obtained through a real-time vehicle experiment. Multiple estimation models, including the standard ordered logit (SOL) model, fixed parameters logit (FPL) model, and random parameters logit (RPL) model were established. RESULTS The findings show that road characteristics will affect the longitudinal kinetic characteristics of the vehicle and, consequently, map the level of risk of rear-end accidents. Driving compensation effects were also identified in this paper (i.e., the drivers tend to be more cautious in complicated driving circumstances). Another finding relating to the mountain freeway is that different tunnel characteristics (e.g., tunnel entrance and tunnel exit) have different effects on different types of traffic accidents. Practical Applications: The framework proposed in this article can provide new insight for researchers to enlarge the research subjects of both explanatory and outcome variables in accident analysis. Future research could be implemented to consider more driving conditions.
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Affiliation(s)
- Changjian Zhang
- School of Transportation, Southeast University, Nanjing 210018, China; School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China.
| | - Jie He
- School of Transportation, Southeast University, Nanjing 210018, China; School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China.
| | - Xintong Yan
- School of Transportation, Southeast University, Nanjing 210018, China; School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China.
| | - Ziyang Liu
- School of Transportation, Southeast University, Nanjing 210018, China; School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China.
| | - Yikai Chen
- School of Transportation, Southeast University, Nanjing 210018, China; School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China.
| | - Hao Zhang
- School of Transportation, Southeast University, Nanjing 210018, China; School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China.
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18
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Zhou Z, Meng F, Song C, Sze NN, Guo Z, Ouyang N. Investigating the uniqueness of crash injury severity in freeway tunnels: A comparative study in Guizhou, China. JOURNAL OF SAFETY RESEARCH 2021; 77:105-113. [PMID: 34092300 DOI: 10.1016/j.jsr.2021.02.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/24/2020] [Accepted: 02/11/2021] [Indexed: 06/12/2023]
Abstract
INTRODUCTION With the rapid development of transportation infrastructures in precipitous areas, the mileage of freeway tunnels in China has been mounting during the past decade. Provided the semi-constrained space and the monotonous driving environment of freeway tunnels, safety concerns still remain. This study aims to investigate the uniqueness of the relationships between crash severity in freeway tunnels and various contributory factors. METHOD The information of 10,081 crashes in the entire freeway network of Guizhou Province, China in 2018 is adopted, from which a subset of 591 crashes in tunnels is extracted. To address spatial variations across various road segments, a two-level binary logistic approach is applied to model crash severity in freeway tunnels. A similar model is also established for crash severity on general freeways as a benchmark. RESULTS The uniqueness of crash severity in tunnels mainly includes three aspects: (a) the road-segment-level effects are quantifiable with the environmental factors for crash severity in tunnels, but only exist in the random effects for general freeways; (b) tunnel has a significantly higher propensity to cause severe injury in a crash than other locations of a freeway; and (c) different influential factors and levels of contributions are found to crash severity in tunnels compared with on general freeways. Factors including speed limit, tunnel length, truck involvement, rear-end crash, rainy and foggy weather and sequential crash have positive contributions to crash severity in freeway tunnels. Practical applications: Policy implications for traffic control and management are advised to improve traffic safety level in freeway tunnels.
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Affiliation(s)
- Zichu Zhou
- Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China
| | - Fanyu Meng
- Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China; Department of Statistics and Data Science, Soutern University of Science and Technology, Shenzhen, China.
| | - Cancan Song
- Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China
| | - N N Sze
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zhongyin Guo
- Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China
| | - Nan Ouyang
- Guizhou Transportation Planning Survey & Design Co., Ltd, Guiyang, China
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Sivasankaran SK, Rangam H, Balasubramanian V. Investigation of factors contributing to injury severity in single vehicle motorcycle crashes in India. Int J Inj Contr Saf Promot 2021; 28:243-254. [PMID: 33820490 DOI: 10.1080/17457300.2021.1908367] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Single-vehicle crashes are of major concern in both developed as well as in Low Middle Income Countries due to the severity of injuries, particularly fatal accidents. In India, a significant proportion of crashes are single-vehicle crashes. The vehicles which are involved in accidents due to causes such as self skidding, hitting stationary objects, trees that are simply contributed by the drivers themselves are referred to as out-of-control single-vehicle crashes. The main objective of this study is to evaluate the risk factors associated with single-vehicle motorcycle collisions in Tamilnadu, India and identifies the unique characteristics and injury outcomes associated with these collisions. Crash dataset for the present study was prepared from the police-reported crashes for the past nine years that occurred within the state of Tamilnadu between 2009 and 2017. The identified contributory factors which influence injury severity include driver characteristics, crash-related factors, traffic-related factors, vehicle and environment-related factors. In this study, injury severity is classified into three categories, i.e. fatal, serious, and minor injuries. Since the outcome of the injury severity could be measured on an ordinal scale, a discrete ordered outcome model, an ordered logit model is applied. To summarise the results, thirteen of the studied factors are found to have a significant influence on the injury severity of drivers. Results show that the likelihood of fatal injuries increases in crashes where motorcyclists hit stationary fixed objects, hit trees, ran-off road, inclement weather conditions, urban areas. It is also found that winter season, north districts of Tamilnadu, single and two-lane roads, highways, village roads and, other district roads, daylight conditions, drivers who are younger and working-age group, overtaking from left, taking u-turn are associated with less likelihood of fatal crashes. To increase the overall safety of the roads, targeted countermeasures may be designed in light of injury severity of the drivers with respect to single-vehicle crashes also. This study provides useful insights for reducing injury severity in single-vehicle motorcycle crashes.
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20
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Islam M, Mannering F. The role of gender and temporal instability in driver-injury severities in crashes caused by speeds too fast for conditions. ACCIDENT; ANALYSIS AND PREVENTION 2021; 153:106039. [PMID: 33611081 DOI: 10.1016/j.aap.2021.106039] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/14/2021] [Accepted: 02/08/2021] [Indexed: 06/12/2023]
Abstract
The effect of inappropriate speed adjustment to adverse conditions on crash-injury severities, and how this effect might vary across male and female drivers, and over time, is not well understood. To study this, single-vehicle crashes occurring in rainy weather, where speed too fast for conditions is a driver action identified as a contributing factor to the crash, were considered. The differences between the resulting crash-injury severities of male and female drivers (and how these differences change over time) is then studied utilizing three years of Florida crash data and estimating random parameters multinomial logit models of driver injury severity while considering potential heterogeneity in the means and variances of parameter estimates. Model estimation results show that there were significant differences in the driver-injury severities of male and female drivers, and that the effect of factors that determine injury severities varied significantly over time (statistically significant temporal instability). This suggests that male and female drivers generally perceive and react to rainy weather conditions in fundamentally different ways, and that their responses, as reflected by the effect that explanatory variables have on injury severity probabilities, change over time. However, there were two explanatory variables that had relatively stable effects on injury-severity probabilities over time and across genders: an indicator variable for crashes involving non-collision factors (including overturn/rollover crashes) and an indicator variable for restraint usage. Policies that target these two variables could produce long-term reductions in crash-injury severities under adverse conditions.
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Affiliation(s)
- Mouyid Islam
- Research Faculty, Center for Urban Transportation Research, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620, United States.
| | - Fred Mannering
- Department of Civil and Environmental Engineering, University of South Florida, 4202 E. Fowler Avenue, ENG 207, Tampa, FL 33620, United States.
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21
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Yan X, He J, Zhang C, Liu Z, Qiao B, Zhang H. Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models. ACCIDENT; ANALYSIS AND PREVENTION 2021; 153:106034. [PMID: 33647597 DOI: 10.1016/j.aap.2021.106034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 01/07/2021] [Accepted: 02/07/2021] [Indexed: 06/12/2023]
Abstract
Single-vehicle crashes are more fatality-concentrated and have posed increasing challenges in traffic safety, which is of great research necessity. Tremendous previous studies have conducted relevant analysis with econometric modeling approaches, whereas the ability of non-parametric methods to predict crash severity is still smattering of knowledge. Consequently, the main objective of this paper is to conduct single-vehicle crash severity prediction with different tree-based and non-parameter models. An alternate aim is to identify the intrinsic mechanism of how contributing factors determine single-vehicle crash severity. By virtue of Grid-Search method, this paper conducted fine-tuning of different models to obtain the best performances based on five crash severity sub-datasets. For model evaluation, the accuracy indicators were calculated in training, validation and test sets, respectively. Besides, feature importance extraction was undertaken based on the results of model comparison. The finding indicated that these models didn't exhibit a huge performance difference for crash severity prediction in the same severity level; however, the performances of the models did vary among different datasets, with an average training accuracy of 99.27 %, 96.4 %, 86.98 %, 86.84 %, 71.76 % in fatal injury, severe injury, visible injury, complaint of pain, PDO crash datasets, respectively. Additionally, it was found that in each severity dataset, the indicator urban freeways is a determinant factor that leads to the occurrence of crashes while rural freeways is more related to more severe crashes (i.e., fatal and severe crashes). This paper can provide valuable information for model selection and tuning in accident severity prediction. Future research could consider the influences that temporal instability of contributing features has on the model performances.
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Affiliation(s)
- Xintong Yan
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing, 210096, PR China.
| | - Jie He
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing, 210096, PR China.
| | - Changjian Zhang
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing, 210096, PR China.
| | - Ziyang Liu
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing, 210096, PR China.
| | - Boshuai Qiao
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing, 210096, PR China.
| | - Hao Zhang
- School of Transportation, Southeast University, 2 Si pai lou, Nanjing, 210096, PR China.
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Se C, Champahom T, Jomnonkwao S, Banyong C, Sukontasukkul P, Ratanavaraha V. Hierarchical binary logit model to compare driver injury severity in single-vehicle crash based on age-groups. Int J Inj Contr Saf Promot 2020; 28:113-126. [PMID: 33302804 DOI: 10.1080/17457300.2020.1858113] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Most of the previous single-vehicle crash analysis studies ignored the effect of road-segments level at higher plan that could probably be unobserved heterogeneity and vary among crash-level factor from one road-segment to next and possibly could lead to a potential biased estimated result. This study developed a hierarchical binary logit model which have the ability to account for both unobserved heterogeneity and correlation within road-segment, to investigate and compare the impact of significant factors influencing fatal single-vehicle crash between young, mid-age and old driver model. A seven-years from 2011 to 2017 crash data, Department of Highway (DOH), Thailand were used in this study. The Intra-Class-Correlation values indicate the importance of road-segment level that 10.1%, 12.2% and 12.8% of the total variation were accounted by random effect from road-segment heterogeneity for young, mid-age and old driver model, respectively. The estimated result of this study shows that influence of alcohol and fatigue increase risk of fatal crash among young and old driver, seatbelt-usage reduce risk of being fatal among mid-age and old driver, roadside safety feature (guardrail) significantly reduce fatality risk among young and mid-age driver, and night time driving without light increase probability of fatal crash for mid-age driver. This study recommends the need to enforce the law on driver under influence of alcohol and seatbelt usage, educational campaign on driving, and installation of guardrail on curve road.
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Affiliation(s)
- Chamroeun Se
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Thanapong Champahom
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Sajjakaj Jomnonkwao
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Chinnakrit Banyong
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Piti Sukontasukkul
- Department of Civil Engineering, Construction and Building Materials Research Center, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand
| | - Vatanavongs Ratanavaraha
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand
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Li G, Lai W, Qu X. Association between Crash Attributes and Drivers' Crash Involvement: A Study Based on Police-Reported Crash Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17239020. [PMID: 33287359 PMCID: PMC7730043 DOI: 10.3390/ijerph17239020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/15/2020] [Accepted: 11/26/2020] [Indexed: 11/21/2022]
Abstract
Understanding the association between crash attributes and drivers’ crash involvement in different types of crashes can help figure out the causation of crashes. The aim of this study was to examine the involvement in different types of crashes for drivers from different age groups, by using the police-reported crash data from 2014 to 2016 in Shenzhen, China. A synthetic minority oversampling technique (SMOTE) together with edited nearest neighbors (ENN) were used to solve the data imbalance problem caused by the lack of crash records of older drivers. Logistic regression was utilized to estimate the probability of a certain type of crashes, and odds ratios that were calculated based on the logistic regression results were used to quantify the association between crash attributes and drivers’ crash involvement in different types of crashes. Results showed that drivers’ involvement patterns in different crash types were affected by different factors, and the involvement patterns differed among the examined age groups. Knowledge generated from the present study could help improve the development of countermeasures for driving safety enhancement.
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Affiliation(s)
- Guofa Li
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
| | - Weijian Lai
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
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Chen Y, Luo R, Yang H, King M, Shi Q. Applying latent class analysis to investigate rural highway single-vehicle fatal crashes in China. ACCIDENT; ANALYSIS AND PREVENTION 2020; 148:105840. [PMID: 33166878 DOI: 10.1016/j.aap.2020.105840] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/26/2020] [Accepted: 10/09/2020] [Indexed: 06/11/2023]
Abstract
Rural highways are an important component of highway networks in developing countries. The high fatality rates of single-vehicle crashes in these highways recently attracted increasing attention. Given that most studies on the factors that affect the severity of single-vehicle crashes in rural highways were conducted in developing countries, the present study investigated this issue in a Chinese setting by analyzing the single-vehicle crash data of rural highways in Anhui Province, China from 2014 to 2017. First, in consideration of the unobserved heterogeneity of crash data, a method that combines latent class analysis (LCA) and binary logistic regression (BLR), which is called LC-BLR, was applied to identify the significant factors that affect the severity of single-vehicle crashes in rural highways. Second, the goodness-of-fit and prediction accuracy of the LC-BLR model and the BLR model were compared. Results revealed that the performance of the former was more satisfactory than that of the latter. Finally, countermeasures were proposed based on the analysis of the main factors that affect each sub-class crash in the LC-BLR model. The LC-BLR model results indicated that collision typewas significant in all three sub-class models considered in the analysis, but the effects on crash severity varied. Several variables (e.g., driving license state, time of week, driver age) demonstrated a significant effect in a specific sub-class model, thereby indicating that these factors were only effective in mitigating the crash severity of one sub-class. The findings of this study can facilitate the development of cost-effective policies or countermeasures for reducing the severity of single-vehicle crashes in rural highways.
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Affiliation(s)
- Yikai Chen
- School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China.
| | - Renjia Luo
- School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China
| | - Huimin Yang
- School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China
| | - Mark King
- Centre for Accident Research and Road Safety-Queensland, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.
| | - Qin Shi
- School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China.
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Huo X, Leng J, Hou Q, Zheng L, Zhao L. Assessing the explanatory and predictive performance of a random parameters count model with heterogeneity in means and variances. ACCIDENT; ANALYSIS AND PREVENTION 2020; 147:105759. [PMID: 32971380 DOI: 10.1016/j.aap.2020.105759] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 07/04/2020] [Accepted: 09/02/2020] [Indexed: 06/11/2023]
Abstract
Random parameters model has been demonstrated to be an effective method to account for unobserved heterogeneity that commonly exists in highway crash data. However, the predefined single distribution for each random parameter may limit how the unobserved heterogeneity is captured. A more flexible approach is to develop a random parameters model with heterogeneity in means and variances by allowing the mean and variance of potential each random parameter to be an estimable function of explanatory variables. This burgeoning technique for modelling unobserved heterogeneity has been increasingly applied to various safety evaluation scenarios recently. However, the predictive performance of this emerging method, which determines the practicability of the model for a specific circumstance, has never been investigated as far as our knowledge. In addition, the explanatory power by including heterogeneous means and variances of random parameters need to be further investigated to confirm the potential merits of this method in crash data analysis. In this paper, a random parameters negative binomial with heterogeneity in means and variances (RPNBHMV) model, a standard random parameters negative binomial (RPNB) model and a traditional fixed parameters negative binomial (NB) model were estimated using the same dataset. The explanatory and predictive performance of the three models were thoroughly evaluated and compared. Results showed that: 1) the RPNB model fitted the data significantly better than the NB model, and the RPNBHMV model further improved the statistical fit of the RPNB model but the improvement was slight; 2) more insights into interactions of safety factors were inferred from the RPNBHMV model, which demonstrates the explanatory benefit of this model; 3) the RPNBHMV and RPNB models had both advantages (e.g., produced overall better prediction accuracy) and disadvantages (e.g., provided reduced prediction accuracy across the range of explanatory variables) when applied to in-sample observations (i.e., observations used to estimate the model); 4) the RPNBHMV and RPNB models might be less precise than the NB model when applied to out-of-sample observations. These findings indicate that the RPNBHMV model offers more insights and may be used for explanatory safety analysis for sites where reliable data can be collected. However, the simple NB model is more reliable - at least with the dataset used in this study - than its random parameters model counterparts for other sites where the data are unavailable or unreliable, which is a common safety evaluation scenario in practice.
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Affiliation(s)
- Xiaoyan Huo
- School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China; School of Automotive Engineering, Harbin Institute of Technology, Weihai, China
| | - Junqian Leng
- School of Automotive Engineering, Harbin Institute of Technology, Weihai, China
| | - Qinzhong Hou
- School of Automotive Engineering, Harbin Institute of Technology, Weihai, China.
| | - Lai Zheng
- School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China
| | - Lintao Zhao
- School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China
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26
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Yu H, Yuan R, Li Z, Zhang G, Ma DT. Identifying heterogeneous factors for driver injury severity variations in snow-related rural single-vehicle crashes. ACCIDENT; ANALYSIS AND PREVENTION 2020; 144:105587. [PMID: 32540621 DOI: 10.1016/j.aap.2020.105587] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 05/03/2020] [Accepted: 05/07/2020] [Indexed: 06/11/2023]
Abstract
Snowy weather is consistently considered as a hazardous factor due to its potential leading to severe fatal crashes. A seven-year crash dataset including rural highway single vehicle crashes from 2010 to 2016 in Washington State is applied in the present study. Pseudo elasticity analysis is conducted to investigate significant impact factors and the temporal stability of model specifications is tested via a likelihood ratio test. The proposed model based on the seven-year dataset is able to capture the individual-specific heterogeneity across crash records for four significant factors, i.e., surface ice, male, and airbag combine deployment for minor injury, and male for serious injury and fatality. Their estimated parameters were found to be normal distribution instead of fixed value over the observations. Other significant impact factors with fixed effects are: inroad object, animal, overturn, surface wet, surface snow, unusual horizontal design, medium and high speed limits, driver age, impaired condition, no belt usage, vehicle type, airbag deployment. Especially, when compared to significant factors for crashes under other weather conditions, male indicator and impaired condition show significant higher effects in snow-related crashes. The results of temporal stability test show that the model specification is generally not temporally stable for driver injury severity model based on the years of crash data that were used, especially for longer period (more than 3-year dataset). Models that allow the explanatory variables to track temporal heterogeneity, are of great interest and can be explored in future research.
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Affiliation(s)
- Hao Yu
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
| | - Runze Yuan
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
| | - Zhenning Li
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
| | - Guohui Zhang
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
| | - David Tianwei Ma
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
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Meng F, Xu P, Song C, Gao K, Zhou Z, Yang L. Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17155623. [PMID: 32759863 PMCID: PMC7570167 DOI: 10.3390/ijerph17155623] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 07/30/2020] [Accepted: 07/31/2020] [Indexed: 11/24/2022]
Abstract
A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.
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Affiliation(s)
- Fanyu Meng
- Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen 518000, China;
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518000, China
| | - Pengpeng Xu
- Department of Civil Engineering, The University of Hong Kong, Hong Kong 999077, China
- Correspondence: (P.X.); (L.Y.)
| | - Cancan Song
- Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China; (C.S.); (Z.Z.)
| | - Kun Gao
- Department of Architecture and Civil Engineering, Chalmers University of Technology, 41296 Göteborg, Sweden;
| | - Zichu Zhou
- Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China; (C.S.); (Z.Z.)
| | - Lili Yang
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518000, China
- Correspondence: (P.X.); (L.Y.)
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Risk of Injury and Mortality among Driver Victims Involved in Single-Vehicle Crashes in Taiwan: Comparisons between Vehicle Types. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17134687. [PMID: 32610689 PMCID: PMC7370069 DOI: 10.3390/ijerph17134687] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 06/20/2020] [Accepted: 06/25/2020] [Indexed: 11/23/2022]
Abstract
Vehicle-type specific injury severity has rarely been investigated mainly because of a lack of such information in hospital-based studies that normally exclude those who are severely injured and die on the scene. No study has been conducted either on driver characteristics in single vehicle crashes in Taiwan according to vehicle type. This was the first population-based study aiming to describe demographic characteristics in association with vehicle-specific rates of injury and fatality among driver victims involved in single-vehicle crashes in Taiwan. We presented sex and age-specific number and proportion of driver victims according to vehicle type. We calculated sex and age-specific rates of injury and fatality. Injury and fatality rates were also graphically presented. Bicycle and motorcycle rider victims generally had higher injury rates but lower fatality rates. However, older (45+) bicycle rider victims had greater fatality risk. By contrast, truck and car driver victims were generally associated with lower injury rates but with higher fatality rates. Elderly (65+ years) truck driver victims suffered from higher rates of injury and fatality. Male victims were found to have a higher fatality rate than female victims regardless of vehicle type. The vehicle-type-specific analyses of injury and fatality are considered useful in identifying single-vehicle crash victims at greater risks of injury and fatality.
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Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods. SUSTAINABILITY 2020. [DOI: 10.3390/su12041324] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.
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30
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Li Z, Wu Q, Ci Y, Chen C, Chen X, Zhang G. Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes. ACCIDENT; ANALYSIS AND PREVENTION 2019; 129:230-240. [PMID: 31176143 DOI: 10.1016/j.aap.2019.04.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 02/14/2019] [Accepted: 04/01/2019] [Indexed: 06/09/2023]
Abstract
The single-vehicle crash has been recognized as a critical crash type due to its high fatality rate. In this study, a two-year crash dataset including all single-vehicle crashes in New Mexico is adopted to analyze the impact of contributing factors on driver injury severity. In order to capture the across-class heterogeneous effects, a latent class approach is designed to classify the whole dataset by maximizing the homogeneous effects within each cluster. The mixed logit model is subsequently developed on each cluster to account for the within-class unobserved heterogeneity and to further analyze the dataset. According to the estimation results, several variables including overturn, fixed object, and snowing, are found to be normally distributed in the observations in the overall sample, indicating there exist some heterogeneous effects in the dataset. Some fixed parameters, including rural, wet, overtaking, seatbelt used, 65 years old or older, etc., are also found to significantly influence driver injury severity. This study provides an insightful understanding of the impacts of these variables on driver injury severity in single-vehicle crashes, and a beneficial reference for developing effective countermeasures and strategies for mitigating driver injury severity.
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Affiliation(s)
- Zhenning Li
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street, Honolulu, HI 96822, USA
| | - Qiong Wu
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street, Honolulu, HI 96822, USA
| | - Yusheng Ci
- Department of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang 150090, China
| | - Cong Chen
- Center for Urban Transportation Research, University of South Florida, 4202 East Fowler Avenue, CUT100, Tampa, FL 33620, USA
| | - Xiaofeng Chen
- School of Automation, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Guohui Zhang
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street, Honolulu, HI 96822, USA.
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A Comprehensive Analysis of Multi-Vehicle Crashes on Expressways: A Double Hurdle Approach. SUSTAINABILITY 2019. [DOI: 10.3390/su11102782] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
To maintain safe expressways, it is necessary to investigate the causes of severe traffic accidents and establish a strategy. This study aims to analyze crashes and identify the influence of crash-risk factors on multi-vehicle (MV) crashes. Crashes involving three types of vehicles namely passenger cars, buses, and freight trucks were analyzed using a seven-year data spanning 2011 to 2017 which consists of crashes that occurred on expressways in South Korea. We applied a double hurdle approach in which a model consists of two estimators: The first estimation, which is a binary logit model selects MV crashes from the dataset; and the second estimation which is a truncated regression model estimates the number of vehicles involved in the MV crash. We found that driver traffic violations such as the improper distance between vehicles, reversing and passing increases the probability of MV crashes occurring. MV crashes in tunnels and mainlines were found to be positively correlated with the number of vehicles involved in the crash, whereas fewer vehicles were involved in MV crashes at ramps and toll-booths. Further, we found that the hurdle model with an exponential form of conditional mean of the latent variable provides better estimation parameters.
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