Risk factors affecting the severity of traffic accidents at Shanghai river-crossing tunnel

Lü, Jian; Xing, Yingying; Wang, Chen; Cai, Xiaobo

doi:10.1080/15389588.2015.1051222

Cited by 38 publications

(23 citation statements)

References 33 publications

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“…This finding supports previous literature reports, where the main factor affecting traffic injury severity was determined to be the designed road geometry [60,61] and one of the factors that was significantly related to severity was road wetness [62,63]. In addition, it has been found that the traffic accident severity correlates with Dg and that male drivers show increased severity [64].…”

Section: Discussionsupporting

confidence: 90%

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

2019

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There have been numerous studies on traffic accidents and their severity, particularly in relation to weather conditions and road geometry. In these studies, traditional statistical methods have been employed, such as linear regression, logistic regression, and negative binomial regression modeling, which are the most common linear and non-linear regression analysis methods. In this research, machine learning architecture was applied to this problem using the random forest, artificial neural network, and decision tree techniques to ascertain the strengths and weaknesses of these methods. Three data sets were used: road geometry data, precipitation data, and traffic accident data over nine years corresponding to the Naebu Expressway, which is located in Seoul, Korea. For the model evaluation, three measures were employed: the out-of-bag estimate of error rate (OOB), mean square error (MSE), and root mean square error (RMSE). The low mean OOB, MSE, and RMSE observed in the results obtained using the proposed random forest model demonstrate its accuracy.

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Section: Discussionsupporting

confidence: 90%

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

2019

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“…There was a significant difference in terms of fatality of RTIs between elderly and non-elderly people. Several studies, all around the world, have confirmed the subject [ 23 , 70 - 73 ]. The main reason is the physical conditions of elderly people as their physical power is atrophied and mitigated and makes them more vulnerable when they face RTIs.…”

Section: Discussionmentioning

confidence: 94%

Epidemiology of Road Traffic Injuries among Elderly People; A Systematic Review and Meta-Analysis

Azami-Aghdash¹,

Aghaei²,

Sadeghi-Bazarghani³

2018

BEAT

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Objective:To systematically review the epidemiological patterns and interventions for prevention of road traffic injuries (RTIs) among elderly. Methods:Searching keywords including: accident, trauma, road injury, road traffic injuries, aging, old, elder, strategy, intervention, road traffic crash prevention and traffic accident in databases including, Google scholar, SID, IranMedex, PubMed and Scopus. English and non-Persian articles, articles presented in congresses, articles that considered elderly people to have age under than 60 years were excluded. The reporting quality of articles was assessed by two experts using Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) check list.Results:RTIs compromised 23.6% of total injuries among elderly. The most frequent injuries were about car accidents (51.4%). Pedestrian injuries composed 48.1% of the RTIs. Head and neck (32.1%) were most injured body parts. There was a significant difference between elderly and non-elderly people in terms of RTIs associated mortality (Odd=2.57 [1.2-5.4 CI 95%]). Overall 25 main domains of intervention and 73 subordinate domains were extracted in five categories (human, road and environment, tools and cars, medical, legal and political issues). Conclusion:According to the notable prevalence and fatality of RTIs, lack of sufficient studies and valid evidence of the present study can provide an appropriate evidence for better interventions for RTIs prevention among elderly.

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“…Yeung and Wong [12] studied the characteristics of 608 traffic accidents occurred in expressway tunnels in Singapore during 2009-2011, and accorded to the location of traffic accidents, the location of tunnels is classified. Lu et al [13] described the temporal and space distribution characteristics of traffic accidents occurred in river-crossing tunnels of Shanghai. Ma et al [14] and Zhang et al [15] explored the major distribution characteristics and rules of TTAs based on the statistical analyses of TTAs of the Shaoguan section of the Beijing-Zhuhai expressway, the causes of traffic accidents were determined, and the practical countermeasures were proposed.…”

Section: Introductionmentioning

confidence: 99%

Spatialtemporal Characteristics of Tunnel Traffic Accidents in China from 2001 to Present

Sun

Wang

Zhang

et al. 2019

Advances in Civil Engineering

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There are often greater risks in the process of tunnel construction, and because of the unpredictable tunnel features, the risks during tunnel operation are also great. Due to the substantial growth of TTAs during the recent years, the safety issues of the tunnel traffic accidents (TTAs) have become a widely discussed topic in China. This study examined 2703 TTAs that occurred in the expressway tunnels of China, from 2001 to the present. The datasets from these accidents were analyzed in detail to determine the temporal and spatial characteristics of the traffic accidents as well as the source of the traffic accident distributions in the expressway tunnels. According to the analysis results, 58% of TTAs took place in the entrance and exit zones, and the rear end accident is the most common accident. In addition, the special festivals are liable to traffic accidents, especially during the Chinese Spring Festival. In order to reduce the accidents, special traffic service needs to be taken in key periods, the expressway tunnel operation departments should improve the management rules, and all the drivers should be educated to be more careful when they enter the tunnel.

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Risk factors affecting the severity of traffic accidents at Shanghai river-crossing tunnel

Abstract: This article aims to provide useful information for engineers to develop interventions and countermeasures to improve tunnel safety in China.

Cited by 38 publications

References 33 publications

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

Epidemiology of Road Traffic Injuries among Elderly People; A Systematic Review and Meta-Analysis

Spatialtemporal Characteristics of Tunnel Traffic Accidents in China from 2001 to Present

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