Analysis and prediction of intersection traffic violations using automated enforcement system data

Examining enforcement coverage for speeding and red-light offenses across various populations and driver characteristics

Over the years empirical evidence has shown that traffic enforcement reduces traffic violations, crashes, and casualties. However, less attention has been paid to enforcement coverage across different populations and driver characteristics. The current study develops and explores a method for estimating police enforcement coverage, by comparing the share of drivers across several characteristics who received tickets from automatic speed and red-light cameras - as an objective estimate of offenses committed - to the share of drivers who received tickets through manual police enforcement. Using data from all speeding and red-light tickets issued to Israelis over a period of one and a half years, we found under-enforcement by police officers for female drivers, two-wheeled vehicle drivers (for speeding), and drivers with previous tickets. We found over-enforcement for younger drivers, truck drivers, and two-wheeled vehicle drivers (for red-light offenses). The findings suggest that the method developed in the research is able to identify groups of drivers who are over- or under-enforced. Police authorities can use this information to create evidence-based enforcement policies.

Taxi drivers' traffic violations detection using random forest algorithm: A case study in China

OBJECTIVE

To effectively explore the impacts of several key factors on taxi drivers' traffic violations and provide traffic management departments with scientific decisions to reduce traffic fatalities and injuries.

METHODS

43,458 electronic enforcement data about taxi drivers' traffic violations in Nanchang City, Jiangxi Province, China, from July 1, 2020, to June 30, 2021, were utilized to explore the characteristics of traffic violations. A random forest algorithm was used to predict the severity of taxi drivers' traffic violations and 11 factors affecting traffic violations, including time, road conditions, environment, and taxi companies were analyzed using the Shapley Additionality Explanation (SHAP) framework.

RESULTS

Firstly, the ensemble method Balanced Bagging Classifier (BBC) was applied to balance the dataset. The results showed that the imbalance ratio (IR) of the original imbalanced dataset reduced from 6.61% to 2.60%. Moreover, a prediction model for the severity of taxi drivers' traffic violations was established by using the Random Forest, and the results showed that accuracy, m_F1, m_G-mean, m_AUC, and m_AP obtained 0.877, 0.849, 0.599, 0.976, and 0.957, respectively. Compared with the algorithms of Decision Tree, XG Boost, Ada Boost, and Neural Network, the performance measures of the prediction model based on Random Forest were the best. Finally, the SHAP framework was used to improve the interpretability of the model and identify important factors affecting taxi drivers' traffic violations. The results showed that functional districts, location of the violation, and road grade were found to have a high impact on the probability of traffic violations; their mean SHAP values were 0.39, 0.36, and 0.26, respectively.

CONCLUSIONS

Findings of this paper may help to discover the relationship between the influencing factors and the severity of traffic violations, and provide a theoretical basis for reducing the traffic violations of taxi drivers and improving the road safety management.