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.