Analysis of injury traffic accidents with machine learning methods: Adana case

Özden, Cevher; Acı, Çiğdem İnan

doi:10.5505/pajes.2016.87847

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study by Acı and Özden [18] examined the factors affecting the traffic accident outcomes in Adana, Turkey and classified the same using machine learning techniques. The research data was obtained from the weather conditions between the years 2005-2015 provided by the Regional Directorate of Meteorology, along with the 25015 data obtained from the reports kept by the Adana Regional Traffic Branch.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining and Machine Learning Approaches in Data Science: Predictive Modeling of Traffic Accident Causes

Ersöz

2022

International Journal of 3D Printing Technologies and Digital Industry

View full text Add to dashboard Cite

Due to the increasing number of deaths and injuries in traffic accidents today, it has become necessary to examine the potential contributing risk factors. The increase in the number of vehicles today leads to an increase in traffic accidents and loss of life and property. Analytical models are presented to investigate the socio-economic, demographic and temporal effects of the factors affecting the level of injury resulting from traffic accidents. By examining the data of various traffic accidents and developing a model, the factors and hazards affecting traffic accidents can be determined by data mining and machine learning approaches. The aim of this study is to determine which classification techniques are important for analyzing traffic accidents and to find out the factor that affects traffic accidents among the variables used in the research. The "Random Forest" algorithm, which gives the best model result among the techniques used in the research, was found. Weather conditions were found to be the most important factor among the factors that lead to traffic accidents, followed by the age and education of the driver. This study is a traceable application in terms of revealing the differences between data mining and machine learning and following the processes.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Data Mining and Machine Learning Approaches in Data Science: Predictive Modeling of Traffic Accident Causes

Ersöz

2022

International Journal of 3D Printing Technologies and Digital Industry

View full text Add to dashboard Cite

show abstract

Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques

et al. 2022

View full text Add to dashboard Cite

Kingdom of Among the G20 countries, Saudi Arabia (KSA) is facing alarming traffic safety issues compared to other G-20 countries. Mitigating the burden of traffic accidents has been identified as a primary focus as part of vision 20230 goals. Driver distraction is the primary cause of increased severity traffic accidents in KSA. In this study, three different machine learning-based severity prediction models were developed and implemented for accident data from the Qassim Province, KSA. Traffic accident data for January 2017 to December 2019 assessment period were obtained from the Ministry of Transport and Logistics Services. Three classifiers, two of which are ensemble machine learning methods, namely random forest, XGBoost, and logistic regression, were used for crash injury severity classification. A resampling technique was used to deal with the problem of bias due to data imbalance issue. SHapley Additive exPlanations (SHAP) analysis interpreted and ranked the factors contributing to crash injury. Two forms of modeling were adopted: multi and binary classification. Among the three models, XGBoost achieved the highest classification accuracy (71%), precision (70%), recall (71%), F1-scores (70%), and area curve (AUC) (0.87) of receiver operating characteristic (ROC) curve when used for multi-category classifications. While adopting the target as a binary classification, XGBoost again outperformed the other classifiers with an accuracy of 94% and an AUC of 0.98. The SHAP results from both global and local interpretations illustrated that the accidents classified under property damage only were primarily categorized by their consequences and the number of vehicles involved. The type of road and lighting conditions were among the other influential factors affecting injury s severity outcome. The death class was classified with respect to temporal parameters, including month and day of the week, as well as road type. Assessing the factors associated with the severe injuries caused by road traffic accidents will assist policymakers in developing safety mitigation strategies in the Qassim Region and other regions of Saudi Arabia.

show abstract

Analysis of injury traffic accidents with machine learning methods: Adana case

Cited by 2 publications

References 6 publications

Data Mining and Machine Learning Approaches in Data Science: Predictive Modeling of Traffic Accident Causes

Data Mining and Machine Learning Approaches in Data Science: Predictive Modeling of Traffic Accident Causes

Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques

Contact Info

Product

Resources

About