Analysis Of Causes Of Traffic Accidents At Urban Road Intersections Based On Big Data

The number of traffic accidents in almost all countries is increasing every year as society develops and urbanization accelerates,and the resulting traffic problems have become global disasters.As an essential part of the urban road network,the intersection attracts a large number of transport vehicles and pedestrians,making traffic accidents more dangerous.Because of the limited amount of road space available for traffic,as well as the disorder and diversity of individual characteristics such as motor vehicles,non-motor vehicles,pedestrians,and drivers,When they meet,there is a conflict,and as the conflict escalates,it leads to an accident,and the intersection becomes a high-accident area.The causes of traffic accidents are studied in order to prevent or avoid traffic accidents and ensure the safety of people’s daily travel.First and foremost,the traffic accident data from Nevada’s Urban Road intersection is summarized.The proportion of traffic accidents is analyzed according to the road condition and environmental condition,and the characteristics and laws of traffic accident at Urban Road intersection are summarized by analyzing the casualty level of intersection,the collision form of traffic accident,and the proportion of traffic accident according to the road condition and environmental condition.Second,to further subdivide drivers,vehicles,intersections,and environmental factors,the law and characteristics of traffic accidents at urban road intersections are statistically analyzed.This paper employs AHP to develop a cause model for traffic accidents,with traffic accidents as the primary decision-making goal.The criterion layer includes drivers,vehicles,intersections,and the environment;the scheme layer includes driver behavior,intersection channelization,and weather conditions.From2014 to 2016,big data on traffic accidents at urban road intersections in Nevada was collected.According to the findings,the most common cause is driver error,which is followed by drunk driving,fatigue driving,and vehicle failure.Finally,the data in the Nevada traffic database is mined using Weka software.The data in the database is clustered to construct the accident logic model after the irrelevant information is removed,and then the Apriori algorithm is used to search for association rules among the influencing factors of traffic accidents.It was discovered that the combination factors of driver error,wet and slick road conditions are highly correlated,while the combination factors of vehicle fault and weather are secondary,and the combination factors of weather and light condition are less correlated.Simultaneously,preventive measures are proposed based on a combination of factors that are known to cause traffic accidents.