Investigating the factors affecting traffic violations based on electronic enforcement data:A case study in Shangyu,China

To study the influencing factors of traffic violations,this study investigated the effects of vehicle attribution,day of week,time of day,location of traffic violations,and weather on traffic violations based on the electronic enforcement data and historical weather data obtained in Shangyu,China.Ten categories of traffic violations were determined from the raw data.Then,chi-square tests were used to analyze the relationship between traffic violations and the potential risk factors.Multinomial logistic regression analyses were conducted to further estimate the effects of different risk factors on the likelihood of the occurrence of traffic violations.By analyzing the results of chi-square tests via SPSS,the five factors above were all determined as significant factors associated with traffic violations.The results of the multinomial logistic regression revealed the significant effects of the five factors on the likelihood of the occurrence of corresponding traffic violations.The conclusions are of great significance for the development of effective traffic intervention measures to reduce traffic violations and the improvement of road traffic safety.

AI-Based Helmet Violation Detection for Traffic Management System

Enhancing road safety globally is imperative,especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations.Acknowledging the critical role of helmets in rider protection,this paper presents an innovative approach to helmet violation detection using deep learning methodologies.The primary innovation involves the adaptation of the PerspectiveNet architecture,transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone,aimed at bolstering detection capabilities.Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset(IDD)for training and validation,the system demonstrates exceptional performance,achieving an impressive detection accuracy of 95.2%,surpassing existing benchmarks.Furthermore,the optimized PerspectiveNet model showcases reduced computational complexity,marking a significant stride in real-time helmet violation detection for enhanced traffic management and road safety measures.

Characteristics and risk of violation behavior of non-motorists at signalized intersections

Aiming at prevalent violations of non-motorists at urban intersections in China, this paper intends to clarify the characteristics and risks of non-motorist violations at signalized intersections through questionnaires and video recordings, which may serve as a basis for non-motorized vehicle management. It can help improve the traffic order and enhance the degree of safety at signalized intersections. To obtain the perception information, a questionaire survey on the Internet was conducted and 972 valid questionnaires were returned. It is found that academic degree contributes little to non-motorist violations, while electrical bicyclists have a relatively higher frequency of violations compared with bicyclists. The video data of 18 228 non-motorist behaviors indicate that the violation rate of all non-motorists is 26.5%; the number of conflicts reaches 1 938, among which violation conflicts account for 66.8%. The study shows that the violation rates and the violation behavior at three types of surveyed intersections are markedly different. It is also concluded that the conflict rates and the violation rates are positively correlated. Furthermore, signal violation, traveling in the wrong direction, and overspeeding to cross the intersection are the most dangerous among traffic violation behaviors.

Gender Forecast Based on the Information about People Who Violated Traffic Principle

User portrait has been a booming concept in big data industry in recent years which is a direct way to restore users’information.When it talks about user portrait,it will be connected with precise marketing and operating.However,there are more ways which can reflect the good use of user portrait.Commercial use is the most acceptable use but it also can be used in different industries widely.The goal of this paper is forecasting gender by user portrait and making it useful in transportation safety.It can extract the information from people who violated traffic principle to know the features of them then forecast the gender of these people.Finally,it will analyze the prediction based on characteristics correlation and forecasting results from models which can verify if gender can have an obvious influence on the traffic violation.Also we hope give some advice to drivers and traffic department by doing this research.

Examining unobserved factors associated with red light running in Vietnam:A latent class model analysis

Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.