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 (
The traffic safety situation around the world is still not optimistic. According to the latest statistics of WHO [
Because of the complex traffic conflicts and frequent signal changes, signalized intersections have been proven to be one of the most unsafe locations in the road network. In the United States, 43% of traffic crashes and 23% of fatal traffic crashes occur in/near intersections [
In the transportation system, human factors are one of the decisive factors leading to traffic crashes. A Policy on Geometric Design of Highways and Streets (“green book”) [
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. This technology could automatically record the times, locations, and types of traffic violations. Additionally, in order to ensure the justice of punishment, authorities also 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.
In this research, we raise the following two research questions: Question 1. Is there a significant statistical relationship between traffic crashes and traffic violations? What kind of relationship is it? Question 2. Based on the above conclusion, what kind of targeted enforcement advice can be given?
The quantitative research on the relationship between unsafe acts and accidents can be traced back to the classic Heinrich’s Law [
Research on traffic violations mainly focuses on the following two points. The first concern is the impact of traffic enforcement on traffic crashes, namely, “can traffic enforcement reduce traffic crashes?” Research by Bjørnskau and Elvik et al. [
The second research hot issue on traffic violation is to investigate the quantitative relationship with traffic crashes. However, due to privacy concerns, the actual traffic violation record data is hard to obtain from traffic management authorities. Conventional studies [
Summarizing the above studies, it is not difficult to find that (a) electronic police enforcement can significantly inhibit traffic violations and (b) there is a clear positive relation between traffic violations and crashes. However, (a) much of previous research has been based on self-report data and, therefore, may be affected by social desirability bias. (b) As far as we can find, the relevant researches are concerned about the relationship between a certain type of traffic violation (such as red-light running) and the crash. In fact, the accidents that happen at an intersection in one year are bound to be related to different kinds of traffic violations. Hence, there is a need for further work using a different data source and investigating the relationship between multiple kinds of traffic violations and crashes. In particular, there is a lack of research on traffic violations at intersections, wherein around 30% of traffic crashes happen. Therefore, this paper will conduct further study on traffic violations based on empirical data.
A total of 31 four-legged signalized intersections in Yinchuan and Suqian, China, are selected as the research sites. Google Earth is utilized to explore the spatial data for assisting in the selection of signalized intersections. The selection criteria of research sites are as follows: The intersections should be located in urban areas of these two cities. The research site should be 90° intersections. Skewed intersections tend to exhibit higher crash rates than 90° intersections [ The selected intersections should have a high traffic volume in order to maximize the crash sample size and reduce randomness. The annual average daily traffic (AADT) is required to exceed 10,000 PCU. The selected intersection data should be complete. Because of the damage of detectors, especially the traffic-flow detectors, it is not easy to find intersections with complete one-year records of traffic volumes, violations, and crashes. Additionally, there are currently no GIS statistical crash data available, so it is necessary to manually extract the crash record. Figure
The installation and monitoring screen capture of the electronic police. (a) Installation. (b) Screen capture.
The 2017 annual traffic volume, violation, and crash data for the above intersections are used within this study. Among them, traffic volume and violation data are exported through the electronic police system, which can also automatically record the traffic volume every 5 minutes. Since the crash analysis reporting systems have not been utilized in these two cities, the traffic crash data were manually extracted from paper archives. The total number of crashes for a signalized intersection is calculated by both at-intersection and influenced-by-intersection crashes [
The descriptive statistics of traffic crashes and traffic violations in these two cities are shown in Table
Descriptive statistics of variables.
Description | Mean | S.D. | Max. | Min. |
---|---|---|---|---|
The number of traffic crashes | 10.15 | 6.88 | 24 | 2 |
The number of property-loss crashes | 5.80 | 5.38 | 17 | 0 |
The number of injury-or-fatal crashes | 4.38 | 1.68 | 8 | 2 |
Designated approach lane violation (1208) | 20725.06 | 9318.77 | 54853 | 8448 |
Driving commercial vehicle during internship (1234) | 92.13 | 42.81 | 159 | 27 |
Wrong-way entry (1301) | 4306.06 | 2841.62 | 12190 | 817 |
Against signs (1344) | 23894.03 | 9654.51 | 48758 | 10078 |
Against marks (1345) | 27982.35 | 10686.43 | 51806 | 11890 |
Speeding (0∼20% over speed limit) (1352) | 33696.52 | 15594.46 | 97516 | 14268 |
Against signals (1625) | 8550.90 | 3171.46 | 18284 | 2973 |
Some of the variable descriptions are followed by brackets, which is the traffic violation code, based on the “People’s Republic of China Public Safety Industry Standard: Codes for Classification of Traffic Violation Actions (GA/T 16.31-2017).”
The statistics result shows that there were 3,696,659 traffic violations and the ratio of injury-or-fatal crashes, property-loss crashes, and traffic violations was 1 : 1.32 : 27 181.31. As shown in Figure
The comparison of Heinrich’s triangle between factory accidents and traffic crashes.
Traffic crashes, in this study, are classified into two types: injury-or-fatal crashes and property-loss crashes. A total of 315 crashes were recorded during the study period, including 135 injury-or-fatal crashes and 180 property-loss crashes, as shown in Figure
Descriptive statistics of crash severities and traffic violation types. (a) Proportions of crash severities. (b) Proportions of traffic-law violation types.
Since the research sites of this study are distributed in two different cities, it is of considerable importance to explore whether the data are nested. If the data is nested, the variance of the errors in a regression model is not constant. One can also disaggregate the data of intersections in different cities into a general linear regression model, but this will violate the homoscedasticity hypothesis. The test, which is an estimator for heteroscedasticity-consistent standard errors, was proposed by White [
White’s test is used to test for heteroscedastic (“differently dispersed”) errors in regression analysis. The null hypothesis for White’s test is that the variances for the errors are equal; that is,
And the alternate hypothesis (the one we are testing) is that the variances are not equal:
STATA 12 software package is employed in this study, and the result of White’s test is shown in Table
The result of White’s test.
Source | Chi-square | Degree of freedom | |
---|---|---|---|
Heteroscedasticity | 31.00 | 30 | 0.42 |
It can be seen that the
Since the data in this paper satisfies the homoscedasticity hypothesis, we consider establishing a multiple linear regression model in which the number of traffic crashes is the dependent variable and the numbers of traffic violations for each violation type are the independent variables. In addition, we also need to test whether the relationship between crashes and violations is heterogeneous between cities, so the city is also introduced into the model as a dummy variable.
The result of White’s test.
Variable | Model 1 | Model 2 | ||||||
---|---|---|---|---|---|---|---|---|
Regression coefficient | Std. error | Regression coefficient | Std. error | |||||
Designated approach lane violation (1208), | 0.000147 | 0.000255 | 0.57 | 0.571 | 0.0000609 | 0.000519 | 0.12 | 0.908 |
Driving commercial vehicle during internship (1234), | 0.042167 | 0.017219 | 2.45 | 0.022 | 0.0461448 | 0.0309232 | 1.49 | 0.156 |
Wrong-way entry (1301), | 0.001702 | 0.000508 | 3.35 | 0.003 | 0.0020574 | 0.0008136 | 2.53 | 0.023 |
Against signs (1344), | 0.000156 | 0.000156 | 1 | 0.327 | 0.0000645 | 0.0002485 | 0.26 | 0.799 |
Against marks (1345), | −7.9 | 0.000151 | −0.52 | 0.605 | 0.0000229 | 0.0002433 | 0.09 | 0.926 |
Speeding (0∼20% over speed limit) (1352), | −0.00027 | 0.000148 | −1.82 | 0.081 | −0.0002749 | 0.0002927 | −0.94 | 0.363 |
Against signals (1625), | 0.001286 | 0.000499 | 2.58 | 0.017 | 0.0012029 | 0.0006336 | 1.9 | 0.077 |
City | 0.5633981 | 3.105556 | 0.18 | 0.858 | ||||
City· | −2.70 | 0.0003241 | −0.01 | 0.993 | ||||
City· | −0.0152558 | 0.0209794 | −0.73 | 0.478 | ||||
City· | −0.0006035 | 0.0006589 | −0.92 | 0.374 | ||||
City· | 0.0001937 | 0.0001923 | 1.01 | 0.33 | ||||
City· | −0.0001082 | 0.0001865 | −0.58 | 0.571 | ||||
City· | −0.0000623 | 0.0002778 | −0.22 | 0.826 | ||||
City· | 0.0003057 | 0.0007569 | 0.4 | 0.692 | ||||
Constant | −7.48794 | 0.53862 | −2.95 | 0.007 | −6.887317 | 4.392542 | −1.57 | 0.138 |
0.7814 | 0.8399 | |||||||
Adjusted | 0.7149 | 0.6798 |
Note.
The parameter estimation results for the model 1 and model 2 are illustrated in Table
In model 1, the value of R squared is 0.7814, and the adjusted R squared is 0.7149, which shows that this model has a good fit. 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.
Among the four types of traffic violations, driving commercial vehicle during internship (1234) has the highest traffic crash risk. Its regression coefficient is 0.0422, which indicates that traffic crashes are expected to increase by 0.0422 times for each increase of driving commercial vehicle during internship violations. In China, with the rapid development of public transportation, the drivers holding A1 driving license are in short supply; however, according to the law in China, buses must be driven by an A1-licence driver, which is the highest level of Chinese driving license system. The A1 driving license cannot be obtained directly in China but must be upgraded from a B-level driving license. At the same time, after passing the A1-license exam, there is a one-year internship period during which driving commercial vehicles such as buses are not allowed. Driving commercial vehicles requires higher driving skills; hence, driving commercial vehicles during internship illegally will lead to higher traffic crash risk.
Wrong-way entry (1301) violation is the second highest violation of traffic crash risk. In model 1, the coefficient is 0.0017, which means that, for every 10,000 such traffic violations, it is expected that there will be 17 traffic crashes. Wrong-way entry generally leads to a frontal collision that can cause serious casualties. Wrong-way entry behavior at intersections in China is usually due to the large size of intersections and the lack of guiding marks inside. In particular, if there is no median between the entrance and exit lane in one leg of the large-sized intersection, it is easier for drivers to drive into the entrance lane after turning left and eventually lead to frontal collision with the approaching/queuing vehicles.
Similarly, another dangerous traffic violation is going against signals. The regression coefficient of this variable is 0.0013, which shows that each against-signal violation will result in 0.0013 crashes. The against-signal behavior recorded by the electronic police in China is actually red-light running. Numerous other studies [
Some researches show positive correlations between speeding and traffic crashes. However, in this study, an interesting conclusion is obtained that the regression coefficient is negative. This should be related to the unreasonable speed management of Chinese urban roads. According to the code of urban road design in China, the design speed at intersections should be 0.5∼0.7 times that of the adjacent road segment, as shown in Figure
The speed limit at intersections and adjacent road segments in China.
Additionally, we find that there is no statistically significant correlation between against signs/marks and traffic crashes. The so-called against signs/marks in these two cities essentially refer to an uncivilized driving behavior called “cutting in lane,” that is, approaching the intersection from the adjacent lane, crossing the lane-separation mark, and forcing through the way between the queuing vehicles. However, since this situation is common in some Chinese cities, drivers are likely to have the concept of defensive driving [
In addition, here we use the multiple linear regression model to describe the relationship between the AADT and the number of traffic crashes, as shown in Figure
The relationship between the AADT of intersections and the number of property-loss/injury-or-fatal crashes.
When we use the crash rate instead of the crash number as the dependent variable in the model, it is interesting to find that although the crash rate of property-loss crash is still positively correlated with the AADT, however, the relationship between the AADT and the crash rate of injury-or-fatal crash rate is negative, as shown in Figure
The relationship between the AADT of intersections and the rate of property-loss/injury-or-fatal crashes.
According to our dataset, there is a significant linear relationship between traffic crashes and violations. At the same time, the city-related variables are not significant in our research. This suggests that the above result may be statistically stable between different cities.
The 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. Based on the above conclusions, the traffic management authorities can be recommended to conduct more targeted enforcement and reasonable speed management measures. Additionally, some countermeasures can also be taken. For example, at intersections with high frequency of wrong-way entry violation, guide marks can be drawn and a median can be set between the approach and exit lane to reduce the crashes caused by this traffic violation.
At the same time, we found that the total number of traffic crashes increased with higher AADT at the intersection. However, the injury-or-fatal rate of the crash decreased with the increase of the AADT. This means that intersections with smaller traffic volumes have higher traffic crash severity. Therefore, this suggests that, in order to improve the overall safety of the road network, it is necessary to invest management resources not only at the intersection of large traffic volume, but also at the intersections of small traffic volume, such as intersections at rural or suburban areas.
Since the traffic crash and violation data is not available to the public in China, acquisition of research data is difficult; hence, the sample size in this study consists of only two cities. In the future, more data will be collected from different cities to verify the relationship.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors express their sincere gratitude National Natural Science Foundation of China (61773293) and Chinese Government Scholarship (201806260148) for the support.