Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data

The identification of the characteristics of urban road traffic accidents is of great significance for reducing traffic accidents and the corresponding losses. In the context of big data, to accurately understand the characteristics of traffic accidents, the feature set of urban road traffic accidents is proposed, the XGBoost model is used to classify traffic accidents into minor accidents, general accidents, major accidents and serious accidents, and a GA-XGBoost feature recognition model is built. The GA-XGBoost feature recognition model is based on the genetic algorithm (GA) as a factor search algorithm and is verified by applying the big data of traffic accidents in a Chinese city from 2006 to 2016; in addition, the model is compared with the GA-RF, GA-GBDT and GA-LightGBM models. The results show that the GA-XGBoost model can accurately identify the features of the traffic accidents in 7 cities, including driving experience, illegal driving behavior, vehicle age, road intersection type, weather conditions, traffic flow and time interval. Compared with the GA-RF, GA-GBDT and GA-LightGBM models, the recognition features are more accurate, and the performance is better.

[1]  Metin Senbil,et al.  Influence of urban built environment on traffic accidents: The case of Eskisehir (Turkey) , 2017 .

[2]  Birgit Debrabant,et al.  Identifying traffic accident black spots with Poisson-Tweedie models. , 2018, Accident; analysis and prevention.

[3]  Henrikas Sivilevičius,et al.  The analysis of traffic accidents on Lithuanian regional gravel roads , 2013 .

[4]  A. Moradi,et al.  Effective environmental factors on geographical distribution of traffic accidents on pedestrians, downtown Tehran city , 2017, International journal of critical illness and injury science.

[5]  Yasser M. Alginahi,et al.  Analysis of a Transportation System With Correlated Network Intersections: A Case Study for a Central Urban City With High Seasonal Fluctuation Trends , 2017, IEEE Access.

[6]  Sanjay Kumar Singh,et al.  Road traffic accidents in India: issues and challenges , 2017 .

[7]  Gang Tao,et al.  A traffic accident morphology diagnostic model based on a rough set decision tree , 2016 .

[8]  Marina Milenković,et al.  Analysis of Relations Between Freeway Geometry and Traffic Characteristics on Traffic Accidents , 2017 .

[9]  Kelvin K W Yau,et al.  Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong. , 2004, Accident; analysis and prevention.

[10]  Edwin M. Izueke,et al.  The impact of traffic sign deficit on road traffic accidents in Nigeria , 2019, International journal of injury control and safety promotion.

[11]  S. Kar,et al.  Pattern of Road Traffic Accidents in Bhubaneswar, Odisha , 2016 .

[12]  Jiandong Zhao,et al.  Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data , 2019, IEEE Access.

[13]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[14]  You Song,et al.  A Deep Learning Approach to the Prediction of Short-term Traffic Accident Risk , 2017, ArXiv.

[15]  Marin Litoiu,et al.  Sipresk: A Big Data Analytic Platform for Smart Transportation , 2016 .

[16]  Zaw Myo Tun,et al.  Burden, pattern and causes of road traffic accidents in Bhutan, 2013–2014: a police record review , 2018, International journal of injury control and safety promotion.

[17]  Milan Batista,et al.  Identifying the key risk factors of traffic accident injury severity on Slovenian roads using a non-parametric classification tree , 2014 .

[18]  M. Moosazadeh,et al.  Epidemiological Patterns of Road Traffic Crashes During the Last Two Decades in Iran: A Review of the Literature from 1996 to 2014 , 2016, Archives of trauma research.

[19]  Huang Ke.  Characteristics of Driving Behavior and Traffic Accident in Automated Enforcement , 2011 .

[20]  Uchendu O. Onwurah,et al.  Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria. , 2018, Accident; analysis and prevention.

[21]  Sutanto Soehodho,et al.  Public transportation development and traffic accident prevention in Indonesia , 2017 .

[22]  Aamir Saeed Malik,et al.  Selection of Measurement Method for Detection of Driver Visual Cognitive Distraction: A Review , 2017, IEEE Access.

[23]  I. Kawachi,et al.  The short-term impact of economic uncertainty on motor vehicle collisions. , 2018, Preventive Medicine.

[24]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[25]  K. Lipovac,et al.  A model for traffic accidents prediction based on driver personality traits assessment , 2017 .

[26]  Liu Yi,et al.  Model Study for Intelligent Transportation System with Big Data , 2017 .

[27]  Ebenezer Owusu-Sekyere,et al.  Motorcyclist characteristics and traffic behaviour in urban Northern Ghana: Implications for road traffic accidents , 2017 .