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Wu D, Lee JJ, Li Y, Li J, Tian S, Yang Z. A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model. ACCIDENT; ANALYSIS AND PREVENTION 2024; 207:107738. [PMID: 39121575 DOI: 10.1016/j.aap.2024.107738] [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: 05/18/2024] [Revised: 07/08/2024] [Accepted: 08/03/2024] [Indexed: 08/12/2024]
Abstract
For identifying the optimal model for real-time conflict prediction, there is a necessity for proposing a quantitative analysis approach that adaptively selects the optimal prediction model from a large pool of task-suited models, while simultaneously considering the computational efficiency and prediction precision. Based on this line, this study developed an innovative approach termed surrogate model-based optimal prediction model selection (SM-OPMS). This approach aims to accelerate the optimal model selection while incorporating prediction precision considerations, under the precondition of comprehensively evaluating task-suited models. An analytical framework was proposed, further illustrated through a detailed case study. In the case study, real vehicle trajectory data from HighD were processed and applied, which can be aggregated to extract both traffic state variables and corresponding conflict data during a specific time interval. As for the conflict detection, Time-to-Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) indicators were utilized to identify risky conditions. Based on the proposed approach, the selection for the optimal prediction model was conducted, and the variable importance in conflict prediction within the optimal models derived from the SM-OPMS was also investigated. Finally, a comparative analysis with the enumeration-based optimal prediction model selection (E-OPMS) approach was conducted to validate the superiority of the proposed approach. Results indicate that SM-OPMS outperforms E-OPMS in optimal model selection, notably enhancing computational efficiency by up to 94.03%, while maintaining prediction precision within a maximum reduction of only 7.91%. The significance of the SM-OPMS approach is revealed by its comprehensive selection of the optimal prediction models for specific traffic scenarios, taking into account both prediction efficiency and precision simultaneously. The proposed approach is expected to contribute to the development of real-time conflict prediction in the future.
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Affiliation(s)
- Dan Wu
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China.
| | - Jaeyoung Jay Lee
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China.
| | - Ye Li
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China.
| | - Jipu Li
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China.
| | - Shan Tian
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China.
| | - Zhanhao Yang
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China.
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2
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Hossain A, Sun X, Das S, Jafari M, Rahman A. Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means. ACCIDENT; ANALYSIS AND PREVENTION 2024; 199:107503. [PMID: 38368777 DOI: 10.1016/j.aap.2024.107503] [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: 11/09/2023] [Revised: 01/24/2024] [Accepted: 02/12/2024] [Indexed: 02/20/2024]
Abstract
In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve 'unintended pedestrians', drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not 'typical pedestrians', a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014-2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian's FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35-44 years, and spring season. The interaction of 'pedestrian impairment' and 'weekend' was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the 'unintended pedestrians' about the crash scenarios on the interstate and reduce these unexpected incidents.
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Affiliation(s)
- Ahmed Hossain
- Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70503, USA.
| | - Xiaoduan Sun
- Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70503, USA.
| | - Subasish Das
- College of Science of Engineering, Texas State University, 601 University Drive, San Marcos, TX 78666-4684, USA.
| | - Monire Jafari
- Master of Science in Mathematics, Texas State University, 601 University Drive, San Marcos, TX 78666, USA
| | - Ashifur Rahman
- Louisiana Transportation Research Center, Baton Rouge, LA 70808, USA.
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3
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Islam SM, Washington S, Kim J, Haque MM. A hierarchical multinomial logit model to examine the effects of signal strategies on right-turn crash injury severity at signalised intersections. ACCIDENT; ANALYSIS AND PREVENTION 2023; 188:107091. [PMID: 37150130 DOI: 10.1016/j.aap.2023.107091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/10/2023] [Accepted: 04/20/2023] [Indexed: 05/09/2023]
Abstract
The severity of right-turn crashes (or left-turn crashes for the roads in the US) at signalised intersections tends to be high because of the relatively high conflicting speeds and angle of impact. However, right-turn crash injury severity at signalised intersections was not sufficiently studied. In particular, the effects of signal control strategies on crash injury severity are not known. This study developed crash injury severity models for right-turn crashes at signalised intersections with a novel approach of linking crashes with signal strategies which enabled assessing the effects of signal strategies on crash injury severity. The study provided a comprehensive understanding of the impacts of signal strategies, intersection geometry and traffic factors on crash injury severity of right-turn crashes at signalised intersections. Crash injury severity models were estimated with crash data from 221 signalised intersections in Queensland from 2012 to 2018. To address the hierarchical structure of crash data, two-level hierarchical Multinomial Logit models were applied, hypothesising that the first level includes individual crash characteristics while the second level includes intersection characteristics. The applied hierarchical model accounts for the correlation among crashes within intersections. Results showed that crashes during Lagging right-turn and Diamond overlap turns are likely to be more severe than other signal strategies at intersections, with the Lagging right-turn signal being the most hazardous. The results also illustrate that the probability of severe injuries increases with the number of conflicting lanes, whereas the corresponding probability decreases with the occupancy of the conflicting lane.
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Affiliation(s)
- Sheikh Manirul Islam
- School of Civil Engineering, Faculty of Engineering, Architecture, and Information Tech., The University of Queensland, St Lucia 4072 Australia.
| | - Simon Washington
- Managing Director, Advanced Mobility Analytics Group Pty Ltd, Australia.
| | - Jiwon Kim
- School of Civil Engineering, Faculty of Engineering, Architecture, and Information Tech., The University of Queensland, St Lucia 4072 Australia.
| | - Md Mazharul Haque
- Queensland University of Technology, Faculty of Engineering, School of Civil and Environmental Engineering, Brisbane 4001 Australia.
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4
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Islam SM, Washington S, Kim J, Haque MM. A Hierarchical Multinomial Logit model to examine the effects of signal strategies on right-turn crash risks by crash movement configuration. ACCIDENT; ANALYSIS AND PREVENTION 2023; 184:106993. [PMID: 36796218 DOI: 10.1016/j.aap.2023.106993] [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: 08/26/2022] [Revised: 11/11/2022] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
Crash risk models relying on total crash counts are limited in their ability to extract meaningful insights regarding the context of crashes and to identify effective remedial measures. In addition to the typical classification of collisions noted in the literature (e.g., angle, head-on and rear-end), crashes can also be categorised according to vehicle movement configurations (Definitions for Coding Accidents or DCA codes in Australia). This classification presents an opportunity to extract useful insights into road traffic collision causes and contributing factors that are highly contextual. With this aim, this study develops crash-type models by DCA crash movement, with a focus on right-turn crashes (equivalent to left-turn crashes for right-hand traffic) at signalised intersections using a novel approach for linking crashes with signal control strategies. The modelling approach with contextual data enables quantification of the effect of signal control strategies on right-turn crashes, offering potentially unique and novel insights into right-turn crash causes and contributing factors. Crash-type models are estimated with the crash data of 218 signalised intersections in Queensland from 2012 to 2018. Multilevel (Hierarchical) Multinomial Logit Models with random intercepts are employed to capture the hierarchical influence of factors on crashes and unobserved heterogeneities. These models capture upper-level influences on crashes from intersection characteristics and lower-level influences from individual crash characteristics. The models specified in this way account for the correlation among crashes within intersections and influences on crashes across spatial scales. The model results reveal that the probabilities of the opposite approach crash type are significantly higher than the same direction and adjacent approach crash types for all right-turn signal control strategies at intersections except the split approach, for which the opposite is true. The results also suggest that the number of right-turning lanes and occupancy in conflicting lanes are positively associated with the likelihood of crashes for the same direction crash type.
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Affiliation(s)
- Sheikh Manirul Islam
- School of Civil Engineering, Faculty of Engineering, Architecture, and Information Tech., The University of Queensland, St Lucia 4072, Australia.
| | | | - Jiwon Kim
- School of Civil Engineering, Faculty of Engineering, Architecture, and Information Technology, The University of Queensland, St Lucia 4072, Australia.
| | - Md Mazharul Haque
- School of Civil and Environmental Engineering, Faculty of Engineering, Queensland University of Technology, Brisbane 4001, Australia.
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5
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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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Siddiqui AW, Arshad Raza S, Ather Elahi M, Shahid Minhas K, Muhammad Butt F. Temporal impacts of road safety interventions: A structural-shifts-based method for road accident mortality analysis. ACCIDENT; ANALYSIS AND PREVENTION 2022; 174:106767. [PMID: 35792475 DOI: 10.1016/j.aap.2022.106767] [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: 02/08/2022] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 06/15/2023]
Abstract
Extensive prior research has statistically analyzed the impact of infrastructural, policy, and environmental factors on road accidents, injuries, and mortalities. Most of these studies assumed long-term temporal stability in road safety data. These studies were later criticized for ignoring structural shifts in data over time caused by varying systemic influences such as socioeconomic and environmental factors, as well as major changes to road safety rules and networks. In this work, we proposed a novel four-phase methodology that identifies structural shifts or breaks in the road safety data and subsequently evaluates the role of various factors (including road safety interventions) in causing these breaks. The method is generalized, allowing different modeling bases and assumptions on the underlying data distribution. To demonstrate the merits of this methodology, we used it to investigate road accident mortality patterns in the Eastern Province of Saudi Arabia and its subregions for the period 2010-2020, when a series of road safety interventions were introduced. The case study analysis revealed the varying impact of these interventions at both the provincial and governorate levels. These results can be used to evaluate the efficacy of road safety interventions. The lessons learned can help to develop more robust road safety management programs.
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Affiliation(s)
- Atiq W Siddiqui
- College of Business Administration, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam 31451, Saudi Arabia; College of Business Administration, Imam Abdulrahman Bin Faisal University, Saudi Arabia.
| | - Syed Arshad Raza
- College of Business Administration, Imam Abdulrahman Bin Faisal University, Saudi Arabia.
| | - Muhammad Ather Elahi
- College of Business Administration, Imam Abdulrahman Bin Faisal University, Saudi Arabia.
| | | | - Farhan Muhammad Butt
- Development Services, Lee County, Government Board of County Commissioners, Fort Myers, FL, USA
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Manirul Islam S, Washington S, Kim J, Haque M. A comprehensive analysis on the effects of signal strategies, intersection geometry, and traffic operation factors on right-turn crashes at signalised intersections: An application of hierarchical crash frequency model. ACCIDENT; ANALYSIS AND PREVENTION 2022; 171:106663. [PMID: 35439685 DOI: 10.1016/j.aap.2022.106663] [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: 11/10/2021] [Revised: 03/31/2022] [Accepted: 04/01/2022] [Indexed: 06/14/2023]
Abstract
Right-turn movements (equivalent to left turn movements for countries that drive on the right) at intersections are among the most complex driving maneuvers and require a high level of attention for turning across (potentially) oncoming traffic by accepting a safe gap. Not surprisingly, right-turn-involved crashes are one of the most frequent collision types at intersections (e.g., 42% of all signalised intersection crashes in Queensland, Australia). Unfortunately, the causes and contributing factors to right-turn crashes are not well understood, particularly the effect of right-turn signal strategies on the crash risk. In the safety literature, signal strategies are coarsely considered in two generic categories-protected right-turns and permitted right-turns. In reality, right-turn signal strategies could be of various types (usually 5) based on the level of intersection complexity and potential traffic conflicts. The effects of these signal strategies, along with the geometric and traffic factors, have not been well studied. To fill this gap, this study investigates the effects of right-turn signal strategies, intersection geometry and traffic operations factors on right-turn crashes at signalised intersections. To achieve this aim, crash frequency models were estimated using crash data from 221 signalised intersections in Queensland from the years spanning 2012 to 2018. Hierarchical Poisson Regression Models (random intercept models) were employed to capture the hierarchical structure of influences on crashes, with upper-level capturing intersection characteristics and lower-level capturing approach characteristics. The hierarchical model structure, disaggregate exposure variables, and signal strategies examined in this study give rise to an entirely unique study in the literature.
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Affiliation(s)
- Sheikh Manirul Islam
- School of Civil Engineering, Faculty of Engineering, Architecture, and Information Technology, The University of Queensland, St Lucia 4072, Australia.
| | | | - Jiwon Kim
- School of Civil Engineering, Faculty of Engineering, Architecture, and Information Technology, The University of Queensland, St Lucia 4072, Australia.
| | - Mazharul Haque
- School of Civil Engineering and Built Environment, Faculty of Engineering, Queensland University of Technology, Brisbane 4001, Australia.
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8
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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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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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10
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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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Traffic Injury Risk Based on Mobility Patterns by Gender, Age, Mode of Transport and Type of Road. SUSTAINABILITY 2021. [DOI: 10.3390/su131810112] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The role of gender and age in the risk of Road Traffic Injury (RTI) has not been fully explored and there are still significant gaps with regard to how environmental factors, such as road type, affect this relationship, including mobility as a measure of exposure. The aim of this research is to investigate the influence of the environmental factor road type taking into account different mobility patterns. For this purpose, a cross-sectional study was carried out combining two large databases on mobility and traffic accidents in Andalusia (Spain). The risk of RTI and their severity were estimated by gender and age, transport mode and road type, including travel time as a measure of exposure. Significant differences were found according to road type. The analysis of the rate ratio (Ratemen/Ratewomen), regardless of age, shows that men always have a higher risk of serious and fatal injuries in all modes of transport and road types. Analysis of victim rates by gender and age groups allows us to identify the most vulnerable groups. The results highlight the need to include not only gender and age but also road type as a significant environmental factor in RTI risk analysis for the development of effective mobility and road safety strategies.
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Jeon H, Kim J, Moon Y, Park J. Factors affecting injury severity and the number of vehicles involved in a freeway traffic accident: investigating their heterogeneous effects by facility type using a latent class approach. Int J Inj Contr Saf Promot 2021; 28:521-530. [PMID: 34477045 DOI: 10.1080/17457300.2021.1972320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The number of vehicles involved in a traffic accident can be representative of the severity of the accident and provide profound insight into the diverse factors affecting severity, which cannot be identified through the victim fatality rate. This paper presents an analysis and comparison between the effects of factors affecting injury severity and the number of involved vehicles. In this study, a latent class model was used to investigate the unobserved heterogeneity of the accident factors. Freeway facility types are latent factors that affect the heterogeneity of the effects of accident factors. The class mainly including accidents at the freeway mainline sections included more injury/fatal accidents and multiple-vehicle accidents and more significant accident factor estimation results than the other class including accidents at the tollgates or ramps. Among these factors, night-time, faults made by the driver, and heavy vehicle accidents were found to increase the accident severity. Investigating accident factors affecting both the injury severity and number of involved vehicles is important as the number of people who are injured or dead is likely to increase when multiple vehicles are involved in the accident.
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Affiliation(s)
- Hyeonmyeong Jeon
- ITS Performance Evaluation Center, Korea Institute of Civil Engineering and Building Technology, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Jinhee Kim
- Department of Urban Planning and Engineering, Yonsei University, Seoul, Republic of Korea
| | - Yeseul Moon
- Korea Agency for Infrastructure Technology Advancement, Seoul, Republic of Korea
| | - Juneyoung Park
- Department of Transportation and Logistics Engineering, Hanyang University, Ansan, Gyeonggi-do, Republic of Korea
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13
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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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Kadeha C, Haule H, Ali MS, Alluri P, Ponnaluri R. Modeling Wrong-way Driving (WWD) crash severity on arterials in Florida. ACCIDENT; ANALYSIS AND PREVENTION 2021; 151:105963. [PMID: 33385958 DOI: 10.1016/j.aap.2020.105963] [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: 05/18/2020] [Revised: 11/20/2020] [Accepted: 12/20/2020] [Indexed: 06/12/2023]
Abstract
Wrong-way Driving (WWD) is the movement of a vehicle in a direction opposite to the one designated for travel. WWD studies and mitigation strategies have exclusively been focused on limited-access facilities. However, it has been established that WWD crashes on arterial corridors are also severe and relatively more common. As such, this study focused on determining factors influencing the severity of WWD crashes on arterials. The analysis was based on five years of WWD crashes (2012-2016) that occurred on state-maintained arterial corridors in Florida. Police reports of 2,879 crashes flagged as "wrong-way" were downloaded and individually reviewed. The manual review of the police reports revealed that of the 2,879 flagged WWD crashes, only 1,890 (i.e., 65.6 %) occurred as a result of a vehicle traveling the wrong way. The Bayesian partial proportional odds (PPO) model was used to establish the relationship between the severity of these WWD crashes and different driver attributes, temporal factors, and roadway characteristics. The following variables were significant at the 90 % Bayesian Credible Interval (BCI): day of the week, lighting condition, presence of work zone, crash location, age and gender of the wrong-way driver, airbag deployment, alcohol use, posted speed limit, speed ratio (i.e., driver's speed over the posted speed limit), and the manner of collision. Based on the model results, specific countermeasures on Education, Engineering, Enforcement, and Emergency response are discussed. Potential Transportation Systems Management and Operations (TSM&O) strategies for WWD detection systems on arterials to minimize WWD frequency and severity are also proposed.
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Affiliation(s)
- Cecilia Kadeha
- Department of Civil & Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3720, Miami, FL 33174, USA.
| | - Henrick Haule
- Department of Civil & Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3720, Miami, FL 33174, USA.
| | - Md Sultan Ali
- Department of Civil & Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3720, Miami, FL 33174, USA.
| | - Priyanka Alluri
- Department of Civil & Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3628, Miami, FL 33174, USA.
| | - Raj Ponnaluri
- Connected Vehicles, Arterial Management, & Managed Lanes Engineer, Florida Department of Transportation, 605 Suwannee St, MS 36, Tallahassee, FL 32399, USA.
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15
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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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16
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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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17
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Kitali AE, Mokhtarimousavi S, Kadeha C, Alluri P. Severity analysis of crashes on express lane facilities using support vector machine model trained by firefly algorithm. TRAFFIC INJURY PREVENTION 2020; 22:79-84. [PMID: 33206561 DOI: 10.1080/15389588.2020.1840563] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 10/18/2020] [Accepted: 10/19/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVE Express lanes (ELs) provide an alternative way for improving the capacity of the existing freeway network without considerably expanding the roadway footprint. Although much research has been done to explore factors contributing to crashes on these facilities, there is not much discussion on factors influencing their injury severity. This study explored factors influencing the injury severity of crashes on EL facilities. METHOD A Support Vector Machine (SVM) model trained by the Firefly Algorithm was used to identify factors influencing the injury severity of crashes on EL facilities. The analysis was based on three years of crash data (2012-2014) from four EL facilities in California, totaling 61 miles. RESULTS The results indicated that the following factors increased the probability of an injury or a fatality: concrete barriers, high average annual daily traffic, rolling or mountainous terrain, weekend, adverse road surface condition, and nighttime condition. Moreover, wide right and left shoulder widths decreased the probability of having an injury or a fatality. CONCLUSIONS The results provide insights into the influence of different geometric characteristics and crash-related factors on the severity of crashes on EL facilities. The study findings may assist agencies to better understand the impacts of factors contributing to injury and fatal crashes on EL facilities and implement strategies to reduce the severity of these crashes.
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Affiliation(s)
- Angela E Kitali
- Department of Civil & Environmental Engineering, Florida International University, Miami, Florida
| | | | - Cecilia Kadeha
- Department of Civil & Environmental Engineering, Florida International University, Miami, Florida
| | - Priyanka Alluri
- Department of Civil & Environmental Engineering, Florida International University, Miami, Florida
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18
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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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Champahom T, Jomnonkwao S, Watthanaklang D, Karoonsoontawong A, Chatpattananan V, Ratanavaraha V. Applying hierarchical logistic models to compare urban and rural roadway modeling of severity of rear-end vehicular crashes. ACCIDENT; ANALYSIS AND PREVENTION 2020; 141:105537. [PMID: 32298806 DOI: 10.1016/j.aap.2020.105537] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 03/30/2020] [Accepted: 03/31/2020] [Indexed: 05/26/2023]
Abstract
A rear-end crash is a widely studied type of road accident. The road area at the crash scene is a factor that significantly affects the crash severity from rear-end collisions. These road areas may be classified as urban or rural and evince obvious differences such as speed limits, number of intersections, vehicle types, etc. However, no study comparing rear-end crashes occurring in urban and rural areas has yet been conducted. Therefore, the present investigation focused on the comparison of diverse factors affecting the likelihood of rear-end crash severities in the two types of roadways. Additionally, hierarchical logistic models grounded in a spatial basis concept were applied by determining varying parameter estimations with regard to road segments. Additionally, the study compared coefficients with multilevel correlation model and those without multilevel correlation. Four models were established as a result. The data used for the study pertained to rear-end crashes occurring on Thai highways between 2011 and 2015. The results of the data analysis revealed that the model parameters for both urban and rural areas are in the same direction with the larger number of significant parameter values present in the rural rear-end crash model. The significant variables in both the urban and rural road segment models are the seat belt use, and the time of the incident. To conclude, the present study is useful because it provides another perspective of rear-end crashes to encourage policy makers to apply decisions that favor rules that assure safety.
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Affiliation(s)
- Thanapong Champahom
- 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.
| | - Duangdao Watthanaklang
- Department of Construction Technology, Faculty of Industrial Technology, Nakhon Ratchasima Rajabhat University, 340 Suranarai Road, Naimuang Sub-District, Muang District, Nakhon Ratchasima, 30000, Thailand.
| | - Ampol Karoonsoontawong
- Department of Civil Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha Utid Rd., Bangmod, Thung Khru, Bangkok, 10140, Thailand.
| | - Vuttichai Chatpattananan
- Department of Civil Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand.
| | - Vatanavongs Ratanavaraha
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand.
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20
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Benlagha N, Charfeddine L. Risk factors of road accident severity and the development of a new system for prevention: New insights from China. ACCIDENT; ANALYSIS AND PREVENTION 2020; 136:105411. [PMID: 31911400 DOI: 10.1016/j.aap.2019.105411] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 10/11/2019] [Accepted: 12/21/2019] [Indexed: 06/10/2023]
Abstract
Road accident fatalities and accident severity costs have become top priorities and concerns for Chinese policymakers. Understanding the principal factors that explain accident severity is considered to be the first step towards the adequate design of an accident prevention strategy. In this paper, we examine the contribution of various types of factors (vehicle, driver and others) in explaining accident severity in China. Unlike previous studies, the analysis gives a particular focus on fatal accidents. Using a large sample of 405,177 observations for 4-wheeled vehicles in the year 2017 and various statistical and econometrics approaches (e.g., OLS, quantile regression and extreme value theory), the results show that the factors explaining the severity of accidents differs significantly between normal and extreme severity accidents, e.g. across quantiles. Interestingly, we find that the gender factor is only significant for fatal accidents. In particular, the analysis shows that male drivers have an increased likelihood of extreme risk taking. On the basis of these empirical findings, a new ratemaking approach that aims to improve road safety and prevention is discussed and proposed.
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Affiliation(s)
- Noureddine Benlagha
- Department of Finance and Economics, College of Business and Economics, Qatar University. P.O.X 2713, Doha, Qatar.
| | - Lanouar Charfeddine
- Department of Finance and Economics, College of Business and Economics, Qatar University. P.O.X 2713, Doha, Qatar.
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21
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Xing Y, Chen S, Zhu S, Zhang Y, Lu J. Exploring Risk Factors Contributing to the Severity of Hazardous Material Transportation Accidents in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E1344. [PMID: 32093095 PMCID: PMC7068398 DOI: 10.3390/ijerph17041344] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 02/03/2020] [Accepted: 02/13/2020] [Indexed: 11/17/2022]
Abstract
With the increasing demand of hazardous material (Hazmat), traffic accidents occurred frequently during Hazmat transportation, which had caused widespread concern in communities. Therefore, a good understanding of Hazmat transportation accident characteristics and contributing factors is of practical importance. In this study, 1721 Hazmat accidents that have occurred during road transportation for the period 2014-2017 in China were examined, and a random-parameters ordered probit model was established to explore the influence of contributing factors on the severity of accidents by accounting for unobserved heterogeneity in the data. Both the injuries and the number of people evacuated were considered as the indicator of accident severity and investigated, respectively. Results show that higher injury severity is likely to be associated with type of Hazmat (compressed gas, explosive, and poison), misoperation, driver fatigue, speeding, tunnel, slope, county road, dry road surface, winter, dark, more than two vehicles, rear end crash, and explosion. As for the correlation between risk factors and the severity of evacuation, type of Hazmat (compressed gas, explosive, and poison), quantity of Hazmat (10-39 t), misoperation, county road, dry road surface, weekdays, dusk, explosion significantly contribute to increasing the severity of evacuation of Hazmat accidents.
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Affiliation(s)
- Yingying Xing
- College of Transportation Engineering, Tongji University, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Shanghai 201804, China;
| | - Shengdi Chen
- School of Transport & Communications, Shanghai Maritime University, 1550 Haigang Street, Shanghai 201306, China;
| | - Shengxue Zhu
- Jiangsu key Laboratory of Traffic and Transportation Security, Huaiyin Institute of Technology, Huaian 223003, China;
| | - Yi Zhang
- Department of Transportation Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China;
| | - Jian Lu
- College of Transportation Engineering, Tongji University, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Shanghai 201804, China;
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Yuan Q, Xu X, Xu M, Zhao J, Li Y. The role of striking and struck vehicles in side crashes between vehicles: Bayesian bivariate probit analysis in China. ACCIDENT; ANALYSIS AND PREVENTION 2020; 134:105324. [PMID: 31648116 DOI: 10.1016/j.aap.2019.105324] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 09/25/2019] [Accepted: 10/07/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVE Side crashes between vehicles which usually lead to high casualties and property loss, rank first among total crashes in China. This paper aims to identify the factors associated with injury severity of side crashes at intersections and to provide suggestions for developing countermeasures to mitigate the levels of injuries. METHOD In order to investigate the role of striking and struck vehicles in side crashes simultaneously, bivariate probit model was proposed and Bayesian approach was employed to evaluate the model, compared to the corresponding univariate probit model. DATA Crash data from Beijing, China for the period 2009-2012 were used to carry out the statistical analysis. Based on the investigation with vehicles and data analysis on events, 130 intersection side crash cases were selected to form a specific dataset. Then, the influence of human, vehicles, roadway and environmental variables on crash severity was examined by means of bivariate probit regression within Bayesian framework. RESULTS The effects of the factors on striking vehicle drivers and struck vehicle drivers were considered separately and simultaneously to find more targeted conclusions. The statistical analysis revealed vehicle type, lane number, no non-motorized lane and speeding have the corresponding influence on the injury severity of striking vehicles, while time of day and vehicle type of struck vehicles increased the likelihood of being injured. CONCLUSIONS From the results it can be concluded that there indeed exists correlation between striking and struck vehicles in side crashes, although the correlation is not so strong. Importantly, Bayesian bivariate probit model can address the role of striking and struck vehicles in side crashes simultaneously and can accommodate the correlation clearly, which extends the range of univariate probit analysis. The general and empirical countermeasures are presented to improve the safety at intersections.
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Affiliation(s)
- Quan Yuan
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China; Center for Intelligent Connected Vehicles and Transportation, Tsinghua University, Beijing, China
| | - Xuecai Xu
- School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, China.
| | - Mingchang Xu
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China
| | - Junwei Zhao
- School of Automobile, Chang'an University, Xi'an, China
| | - Yibing Li
- State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China
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23
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Jamal A, Rahman MT, Al-Ahmadi HM, Mansoor U. The Dilemma of Road Safety in the Eastern Province of Saudi Arabia: Consequences and Prevention Strategies. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:E157. [PMID: 31878293 PMCID: PMC6982029 DOI: 10.3390/ijerph17010157] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 12/12/2019] [Accepted: 12/20/2019] [Indexed: 11/28/2022]
Abstract
Road traffic crashes (RTCs) are one of the most critical public health problems worldwide. The WHO Global Status Report on Road Safety suggests that the annual fatality rate (per 100,000 people) due to RTCs in the Kingdom of Saudi Arabia (KSA) has increased from 17.4 to 27.4 over the last decade, which is an alarming situation. This paper presents an overview of RTCs in the Eastern Province, KSA, from 2009 to 2016. Key descriptive statistics for spatial and temporal distribution of crashes are presented. Statistics from the present study suggest that the year 2012 witnessed the highest number of crashes, and that the region Al-Ahsa had a significantly higher proportion of total crashes. It was concluded that the fatality rate for the province was 25.6, and the mean accident to injury ratio was 8:4. These numbers are substantially higher compared to developed countries and the neighboring Gulf states. Spatial distribution of crashes indicated that a large proportion of severe crashes occurred outside the city centers along urban highways. Logistic regression models were developed to predict crash severity. Model estimation analysis revealed that crash severity can be attributed to several significant factors including driver attributes (such as sleep, distraction, overspeeding), crash characteristics (such as sudden deviation from the lane, or collisions with other moving vehicles, road fences, pedestrians, or motorcyclists), and rainy weather conditions. After critical analysis of existing safety and infrastructure situations, various suitable crash prevention and mitigation strategies, for example, traffic enforcement, traffic calming measures, safety education programs, and coordination of key stakeholders, have been proposed.
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Affiliation(s)
- Arshad Jamal
- Department of Civil Engineering, King Fahd University of Petroleum & Minerals, KFUPM Box 655, Dhahran 31261, Saudi Arabia; (A.J.); (H.M.A.-A.); (U.M.)
| | - Muhammad Tauhidur Rahman
- Department of City and Regional Planning, King Fahd University of Petroleum & Minerals, KFUPM Box 5053, Dhahran 31261, Saudi Arabia
| | - Hassan M. Al-Ahmadi
- Department of Civil Engineering, King Fahd University of Petroleum & Minerals, KFUPM Box 655, Dhahran 31261, Saudi Arabia; (A.J.); (H.M.A.-A.); (U.M.)
| | - Umer Mansoor
- Department of Civil Engineering, King Fahd University of Petroleum & Minerals, KFUPM Box 655, Dhahran 31261, Saudi Arabia; (A.J.); (H.M.A.-A.); (U.M.)
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24
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A generalized ordered logit analysis of risk factors associated with driver injury severity. J Public Health (Oxf) 2019. [DOI: 10.1007/s10389-019-01135-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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25
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Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9173449] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Agribusiness industries are among the most hazardous workplaces for non-fatal occupational injuries. The term “post-incident state” is used to describe the health status of an injured person when a non-fatal occupational injury has occurred, in the post-incident period when the worker returns to work, either immediately with zero days away from work (medical state) or after a disability period (disability state). An analysis of nearly 14,000 occupational incidents in agribusiness operations allowed for the classification of the post-incident state as medical or disability (77% and 23% of the cases, respectively). Due to substantial impacts of occupational incidents on labor-market outcomes, identifying factors that influence the severity of such incidents plays a significant role in improving workplace safety, protecting workers, and reducing costs of the post-incident state of an injury. In addition, the average costs of a disability state are significantly higher than those of a medical state. Therefore, this study aimed to identify the contributory factors to such post-incident states with logistic regression using information from workers’ compensation claims recorded between 2008 and 2016 in the Midwest region of the United States. The logistic regression equation was derived to calculate the odds of disability post-incident state. Results indicated that factors influencing the post-incident state included the injured body parts, injury nature, and worker’s age, experience, and occupation, as well as the industry, and were statistically significant predictors of post-incident states. Specific incidents predicting disability outcomes included being caught in/between/under, fall/slip/trip injury, and strain/injury by. The methodology and estimation results provide insightful understanding of the factors influencing medical/disability injuries, in addition to beneficial references for developing effective countermeasures for prevention of occupational incidents.
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26
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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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Dabbour E, Haider M, Diaa E. Using random-parameter and fixed-parameter ordered models to explore temporal stability in factors affecting drivers' injury severity in single-vehicle collisions. JOURNAL OF TRAFFIC AND TRANSPORTATION ENGINEERING (ENGLISH EDITION) 2019. [DOI: 10.1016/j.jtte.2018.04.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Zhou M, Chin HC. Factors affecting the injury severity of out-of-control single-vehicle crashes in Singapore. ACCIDENT; ANALYSIS AND PREVENTION 2019; 124:104-112. [PMID: 30639682 DOI: 10.1016/j.aap.2019.01.009] [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: 06/18/2018] [Revised: 11/08/2018] [Accepted: 01/06/2019] [Indexed: 06/09/2023]
Abstract
Single-vehicle (SV) crashes are of major concerns because of their high fatality rates. To understand the proneness of high injury severity for vehicle operators brought about by SV crashes without the confounding influence of other road users, this study focuses on those SV crashes without colliding with pedestrians, which are defined as out-of-control SV crashes given the general consequence of involved vehicles. Moreover, to compare the influence of contributory factors (including driver-vehicle/rider-vehicle, roadway, and environmental characteristics) by vehicle types, the injury severity for riders of motorized two-wheelers and drivers of other motorized vehicles are investigated separately using two disaggregated ordered probit models. The results show that for both riders and drivers, variables such as age (65 and above), drink driving, error type of failing to have proper control, driving maneuvers of left and right turns as well as driving after midnight are associated with more severe injuries whereas factors such as wet, oily or sandy surfaces are related to less severe injury. Four other variables, i.e., foreign vehicle registration, probation or expired license, high speed-limit roads, and type of median lane, have different influences on riders and drivers on injury severity. Additionally, factors such as road traffic type and nationality are only found to significantly influence only riders and drivers respectively. The results shed light on both the similar and different causes of high injury severity for riders and drivers involved in out-of-control SV crashes. Based on the findings, targeted countermeasures may be introduced from multiple perspectives such as driver education and policy development to improve non-traffic-interactive safety.
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Affiliation(s)
- Mo Zhou
- Department of Civil & Environmental Engineering, National University of Singapore, 3 Engineering Drive 2, Singapore, 117576, Singapore.
| | - Hoong Chor Chin
- Department of Civil & Environmental Engineering, National University of Singapore, 3 Engineering Drive 2, Singapore, 117576, Singapore
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29
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Kang Y, Cho N, Son S. Spatiotemporal characteristics of elderly population's traffic accidents in Seoul using space-time cube and space-time kernel density estimation. PLoS One 2018; 13:e0196845. [PMID: 29768453 PMCID: PMC5955513 DOI: 10.1371/journal.pone.0196845] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 04/20/2018] [Indexed: 11/19/2022] Open
Abstract
The purpose of this study is to analyze how the spatiotemporal characteristics of traffic accidents involving the elderly population in Seoul are changing by time period. We applied kernel density estimation and hotspot analyses to analyze the spatial characteristics of elderly people's traffic accidents, and the space-time cube, emerging hotspot, and space-time kernel density estimation analyses to analyze the spatiotemporal characteristics. In addition, we analyzed elderly people's traffic accidents by dividing cases into those in which the drivers were elderly people and those in which elderly people were victims of traffic accidents, and used the traffic accidents data in Seoul for 2013 for analysis. The main findings were as follows: (1) the hotspots for elderly people's traffic accidents differed according to whether they were drivers or victims. (2) The hourly analysis showed that the hotspots for elderly drivers' traffic accidents are in specific areas north of the Han River during the period from morning to afternoon, whereas the hotspots for elderly victims are distributed over a wide area from daytime to evening. (3) Monthly analysis showed that the hotspots are weak during winter and summer, whereas they are strong in the hiking and climbing areas in Seoul during spring and fall. Further, elderly victims' hotspots are more sporadic than elderly drivers' hotspots. (4) The analysis for the entire period of 2013 indicates that traffic accidents involving elderly people are increasing in specific areas on the north side of the Han River. We expect the results of this study to aid in reducing the number of traffic accidents involving elderly people in the future.
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Affiliation(s)
- Youngok Kang
- Department of Social Studies, College of Education, Ewha Womans University, Seoul, South Korea
| | - Nahye Cho
- Department of Social Studies, College of Education, Ewha Womans University, Seoul, South Korea
| | - Serin Son
- Department of Social Studies, College of Education, Ewha Womans University, Seoul, South Korea
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30
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Haghighi N, Liu XC, Zhang G, Porter RJ. Impact of roadway geometric features on crash severity on rural two-lane highways. ACCIDENT; ANALYSIS AND PREVENTION 2018; 111:34-42. [PMID: 29169103 DOI: 10.1016/j.aap.2017.11.014] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 11/11/2017] [Accepted: 11/11/2017] [Indexed: 06/07/2023]
Abstract
This study examines the impact of a wide range of roadway geometric features on the severity outcomes of crashes occurred on rural two-lane highways. We argue that crash data have a hierarchical structure which needs to be addressed in modeling procedure. Moreover, most of previous studies ignored the impact of geometric features on crash types when developing crash severity models. We hypothesis that geometric features are more likely to determine crash type, and crash type together with other occupant, environmental and vehicle characteristics determine crash severity outcome. This paper presents an application of multilevel models to successfully capture both hierarchical structure of crash data and indirect impact of geometric features on crash severity. Using data collected in Illinois from 2007 to 2009, multilevel ordered logit model is developed to quantify the impact of geometric features and environmental conditions on crash severity outcome. Analysis results revealed that there is a significant variation in severity outcomes of crashes occurred across segments which verifies the presence of hierarchical structure. Lower risk of severe crashes is found to be associated with the presence of 10-ft lane and/or narrow shoulders, lower roadside hazard rate, higher driveway density, longer barrier length, and shorter barrier offset. The developed multilevel model offers greater consistency with data generating mechanism and can be utilized to evaluate safety effects of geometric design improvement projects.
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Affiliation(s)
- Nima Haghighi
- Department of Civil & Environmental Engineering, University of Utah, 110 Central Campus Drive, Suite 2000, Salt Lake City, UT 84112, United States.
| | - Xiaoyue Cathy Liu
- Department of Civil & Environmental Engineering, University of Utah, 110 Central Campus Drive, Suite 2000, Salt Lake City, UT 84112, United States.
| | - Guohui Zhang
- Department of Civil & Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street, Holmes 383, Honolulu, HI 96822, United States.
| | - Richard J Porter
- Venture I, 940 Main Campus Drive, Suite 500, Raleigh, NC 2706, United States.
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31
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Anarkooli AJ, Hosseinpour M, Kardar A. Investigation of factors affecting the injury severity of single-vehicle rollover crashes: A random-effects generalized ordered probit model. ACCIDENT; ANALYSIS AND PREVENTION 2017; 106:399-410. [PMID: 28728062 DOI: 10.1016/j.aap.2017.07.008] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Revised: 07/02/2017] [Accepted: 07/05/2017] [Indexed: 06/07/2023]
Abstract
Rollover crashes are responsible for a notable number of serious injuries and fatalities; hence, they are of great concern to transportation officials and safety researchers. However, only few published studies have analyzed the factors associated with severity outcomes of rollover crashes. This research has two objectives. The first objective is to investigate the effects of various factors, of which some have been rarely reported in the existing studies, on the injury severities of single-vehicle (SV) rollover crashes based on six-year crash data collected on the Malaysian federal roads. A random-effects generalized ordered probit (REGOP) model is employed in this study to analyze injury severity patterns caused by rollover crashes. The second objective is to examine the performance of the proposed approach, REGOP, for modeling rollover injury severity outcomes. To this end, a mixed logit (MXL) model is also fitted in this study because of its popularity in injury severity modeling. Regarding the effects of the explanatory variables on the injury severity of rollover crashes, the results reveal that factors including dark without supplemental lighting, rainy weather condition, light truck vehicles (e.g., sport utility vehicles, vans), heavy vehicles (e.g., bus, truck), improper overtaking, vehicle age, traffic volume and composition, number of travel lanes, speed limit, undulating terrain, presence of central median, and unsafe roadside conditions are positively associated with more severe SV rollover crashes. On the other hand, unpaved shoulder width, area type, driver occupation, and number of access points are found as the significant variables decreasing the probability of being killed or severely injured (i.e., KSI) in rollover crashes. Land use and side friction are significant and positively associated only with slight injury category. These findings provide valuable insights into the causes and factors affecting the injury severity patterns of rollover crashes, and thus can help develop effective countermeasures to reduce the severity of rollover crashes. The model comparison results show that the REGOP model is found to outperform the MXL model in terms of goodness-of-fit measures, and also is significantly superior to other extensions of ordered probit models, including generalized ordered probit and random-effects ordered probit (REOP) models. As a result, this research introduces REGOP as a promising tool for future research focusing on crash injury severity.
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Affiliation(s)
| | - Mehdi Hosseinpour
- Department of Civil Engineering, Central Tehran Branch, Islamic Azad University (IAUCTB), Tehran, Iran.
| | - Adele Kardar
- Department of Civil Engineering, University of Golestan, Gorgan, Iran
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