Abstract
About 90% of traffic crashes are caused by human factors, within which traffic violations are one of the most typical and common causes. In order to investigate the relationship between traffic violations and traffic crashes, this research targets signalized intersections in two Chinese cities: Yinchuan and Suqian. Thirty-one intersections are selected as the research sites, and additionally, the traffic volume, traffic violation, and traffic crash data of each intersection are collected for one year. A White’s test is conducted to test the homoscedasticity of the data and a multiple linear regression model is employed to investigate the relationship between traffic crashes and violations. The results show the following: (1) although the research sites are located in two different cities, the data is homoscedastic, which suggests that the above result may be statistically stable between different cities. (2) There is a significant multiple linear regression relationship (R2 = 0.782, adjusted R2 = 0.716) between the total number of traffic crashes and traffic violations. Among the chosen 7 independent variables, four are significantly related to the dependent variable, namely, driving commercial vehicle during internship, wrong-way entry, speeding, and traffic-light violation. (3) With the increase of annual average daily traffic (AADT), the number of total crashes goes up; however, the injury-or-fatality rate decreases, which means that intersections with smaller traffic volumes tend to have higher traffic crash severity. Based on the above conclusions, it is possible to conduct more targeted enforcement to improve the safety of intersections.
Highlights
A White’s test is conducted to test the homoscedasticity of the data and a multiple linear regression model is employed to investigate the relationship between traffic crashes and violations. e results show the following: (1) the research sites are located in two different cities, the data is homoscedastic, which suggests that the above result may be statistically stable between different cities. (2) ere is a significant multiple linear regression relationship (R2 0.782, adjusted R2 0.716) between the total number of traffic crashes and traffic violations
We found that there are a total of 4 independent variables (driving commercial vehicle during internship, wrong-way entry, speeding (0∼20% over speed limit), and against signals) that are statistically significant enough to cause considerable traffic crashes
E model shows that four kinds of traffic violations can significantly lead to traffic crashes, namely, driving commercial vehicle during internship, wrong-way entry, speeding, and traffic-light violation
Summary
In order to reduce traffic crashes, it is crucial to assess the safety of intersections, identify the risk factors, and conduct targeted enforcement. If the most common violations with their relative order of frequency leading to traffic crashes can be found and targeted enforcement can be conducted, the number of overall accidents will be reduced. With the rapid development of video recognition technology in the past ten years, electronic police equipment has been widely installed by Chinese traffic management authorities at urban road intersections. In order to ensure the justice of punishment, authorities manually check the accuracy of electronic-police data. After these procedures, this recorded data with an accuracy of 95% lays the foundation for this study. Is there a significant statistical relationship between traffic crashes and traffic violations? What kind of relationship is it?
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