scholarly journals Identification of Road Traffic Injury Risk Prone Area Using Environmental Factors by Machine Learning Classification in Nonthaburi, Thailand

2021 ◽  
Vol 13 (7) ◽  
pp. 3907
Author(s):  
Morakot Worachairungreung ◽  
Sarawut Ninsawat ◽  
Apichon Witayangkurn ◽  
Matthew N. Dailey
Keyword(s):  
Machine Learning ◽  
Environmental Factors ◽  
Injury Risk ◽  
Road Traffic ◽  
Road Traffic Injury ◽  
Machine Learning Algorithms ◽  
Factors Affecting ◽  
Road Traffic Injuries ◽  
The Road ◽  
Traffic Injury

Road traffic injuries are a major cause of morbidity and mortality worldwide and currently rank ninth globally among the leading causes of disease burden regarding disability-adjusted life years lost. Nonthaburi and Pathum Thani are parts of the greater Bangkok metropolitan area, and the road traffic injury rate is very high in these areas. This study aimed to identify the environmental factors affecting road traffic injury risk prone areas and classify road traffic injuries from an environmental factor dataset using machine learning algorithms. Road traffic injury risk prone areas were set as the dependent variables for the analysis, with other factors that influence road traffic injury risk prone areas being set as independent variables. A total of 20 environmental factors were selected from the spatial datasets. Then, machine learning algorithms were applied using a grid search. The first experiment from 2017 in Nonthaburi and Pathum Thani was used for training the model, and then, 2018 data from Nonthaburi and Pathum Thani were used for validation. The second experiment used 2018 Nonthaburi data for the training, and 2018 Pathum Thani data were used for the validation. The important factors were grocery stores, convenience stores, electronics stores, drugstores, schools, gas stations, restaurants, supermarkets, and road geometrics, with length being the most critical factor that influenced the road traffic injury risk prone model. The first and second experiments in a random forest model provided the best model environmental factors affecting road traffic injury risk prone areas, and machine learning can classify such road traffic injuries.

scholarly journals Identification of factors affecting the road traffic injury rate

2020 ◽  
Vol 50 ◽  
pp. 735-742
Author(s):  
Gulnara Yakupova ◽  
Polina Buyvol ◽  
Vladimir Shepelev
Keyword(s):  
Road Traffic ◽  
Injury Rate ◽  
Road Traffic Injury ◽  
Factors Affecting ◽  
The Road ◽  
Traffic Injury

scholarly journals Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach

2021 ◽  
Vol 18 (19) ◽  
pp. 10540
Author(s):  
Wachiranun Sirikul ◽  
Nida Buawangpong ◽  
Ratana Sapbamrer ◽  
Penprapa Siviroj
Keyword(s):  
Machine Learning ◽  
Mortality Risk ◽  
Road Traffic ◽  
Road Traffic Injury ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Learning Models ◽  
Traffic Injury ◽  
Drunk Drivers ◽  
Machine Learning Models

Background: Alcohol-related road-traffic injury is the leading cause of premature death in middle- and lower-income countries, including Thailand. Applying machine-learning algorithms can improve the effectiveness of driver-impairment screening strategies by legal limits. Methods: Using 4794 RTI drivers from secondary cross-sectional data from the Thai Governmental Road Safety Evaluation project in 2002–2004, the machine-learning models (Gradient Boosting Classifier: GBC, Multi-Layers Perceptrons: MLP, Random Forest: RF, K-Nearest Neighbor: KNN) and a parsimonious logistic regression (Logit) were developed for predicting the mortality risk from road-traffic injury in drunk drivers. The predictors included alcohol concentration level in blood or breath, driver characteristics and environmental factors. Results: Of 4974 drivers in the derived dataset, 4365 (92%) were surviving drivers and 429 (8%) were dead drivers. The class imbalance was rebalanced by the Synthetic Minority Oversampling Technique (SMOTE) into a 1:1 ratio. All models obtained good-to-excellent discrimination performance. The AUC of GBC, RF, KNN, MLP, and Logit models were 0.95 (95% CI 0.90 to 1.00), 0.92 (95% CI 0.87 to 0.97), 0.86 (95% CI 0.83 to 0.89), 0.83 (95% CI 0.78 to 0.88), and 0.81 (95% CI 0.75 to 0.87), respectively. MLP and GBC also had a good model calibration, visualized by the calibration plot. Conclusions: Our machine-learning models can predict road-traffic mortality risk with good model discrimination and calibration. External validation using current data is recommended for future implementation.

Pedestrian Safety and the Built Environment

2015 ◽  
Vol 30 (4) ◽  
pp. 377-392 ◽  
Author(s):  
Philip Stoker ◽  
Andrea Garfinkel-Castro ◽  
Meleckidzedeck Khayesi ◽  
Wilson Odero ◽  
Martin N. Mwangi ◽  
...  
Keyword(s):  
Built Environment ◽  
Road Traffic ◽  
Pedestrian Safety ◽  
Road Traffic Injury ◽  
Built Environments ◽  
Traffic Crashes ◽  
Road Traffic Crashes ◽  
Urban And Regional Planning ◽  
The Road ◽  
Traffic Injury

Urban and regional planning has a contribution to make toward improving pedestrian safety, particularly in view of the fact that about 273,000 pedestrians were killed in road traffic crashes in 2010. The road is a built environments that should enhance safety and security for pedestrians, but this ideal is not always the case. This article presents an overview of the evidence on the risks that pedestrians face in the built environment. This article shows that design of the roadway and development of different land uses can either increase or reduce pedestrian road traffic injury. Planners need to design or modify the built environment to minimize risk for pedestrians.

Ethnicity, deprivation and road traffic injury risk: a survey of risk and road safety and implications for injury prevention

2008 ◽  
Vol 46 (4) ◽  
pp. 133-138 ◽  
Author(s):  
Nicola Christie ◽  
Richard H. Kimberlee ◽  
Ronan Lyons ◽  
Elizabeth Towner ◽  
Heather Ward
Keyword(s):  
Injury Prevention ◽  
Road Safety ◽  
Injury Risk ◽  
Road Traffic ◽  
Road Traffic Injury ◽  
Traffic Injury

scholarly journals The Vehicle Gap: Wealth Inequality and Road Injury Risk Characterization in Vietnam

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Jeffrey KiHyun Park
Keyword(s):  
Injury Risk ◽  
Road Traffic ◽  
Wealth Inequality ◽  
Road Traffic Injury ◽  
Specific Model ◽  
Risk Characterization ◽  
Traffic Injury ◽  
Policy Suggestions ◽  
Definition Of ◽  
Study Designs

Road traffic injury (RTI) is a frequently overlooked issue in the literature of global health. This perspective examines the ways in which wealth inequality exacerbates RTI risk characterization in the specific model of Vietnam. The framework of the Equality-Sustainability Hypothesis, as suggested by Cushing et. al, is used, with a specific focus on three factors: political misrepresentation, discrepancy in consumption intensity, and lack of social cohesion. Policies regarding helmet coverage, healthcare infrastructure, road quality and social psychology are critically analyzed, with sources drawn primarily from epidemiological study designs. Such analyses provide the basis for various policy suggestions towards the end of the perspective that focus specifically on wealth inequality as the primary point of intervention. Overall, this perspective suggests that the Equality-Sustainability Hypothesis holds true in the example of RTIs in Vietnam, which is specifically referred to as a “Vehicle Gap”, and that this hypothesis be made more comprehensive by liberalizing its definition of environment to also include man-made infrastructure.

scholarly journals CONCEPTS AND PRINCIPLES OF ADMINISTRATIVE AND LEGAL PREVENTION OF ROAD TRAFFIC INJURY

2020 ◽  
Vol 2 (4(106)) ◽  
pp. 74-81
Author(s):  
Т. М. Дженчако
Keyword(s):  
Local Governments ◽  
Road Traffic ◽  
Road Transport ◽  
Road Traffic Injury ◽  
Social Regulation ◽  
Public Authorities ◽  
Road Traffic Injuries ◽  
Traffic Injury ◽  
Potential Factors ◽  
The Impact

The article, based on the analysis of current legislation, available scientific, journalistic and methodological sources, including foreign experience, clarifies the essence, meaning and content of the principles of administrative and legal prevention of road traffic injuries as important regulators of road safety in the country. The characteristic of prevention of road traffic injuries which is offered to consider, first, as an important means of social regulation of road legal relations is carried out; secondly, as a system of social, economic, ideological, organizational and legal and psychological and pedagogical measures; third, as a combination of different levels of prevention activities carried out by general and special actors. The goals of road traffic injury prevention are to achieve and maintain the trend of reducing accidents, a positive change in its nature and structure. The concept of administrative and legal prevention of road traffic injuries as a methodologically complex social phenomenon, which covers a multilevel system of administrative and legal measures carried out by public authorities, local governments and individual civil society institutions through the use of delegated powers to identify the causes and conditions of administrative torts on road transport, which lead to road traffic injuries, to minimize or neutralize the impact of acts that give rise to such offenses, search for ways, means of effective influence on potential factors that determine road accidents. Emphasis is placed on the importance of principles as fundamental, guiding principles (requirements) of any important public-law activity, expressing the most significant aspects (manifestations) of implementation of measures of administrative and legal prevention of road traffic injuries and acting as official guidelines in the practice of counteracting administrative "road" torts.

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