Biomaterial Database

Investigating influence factors of traffic violations at signalized intersections using data gathered from traffic enforcement camera. 2020

Chuanyun Fu, and Hua Liu

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China.

To effectively reduce traffic violations that often cause severe crashes at signalized intersections, exploring their contributing factors seems hugely urgent and essential. This study attempted to investigate the influence factors of wrong-way driving (WWD), red-light-running (RLR), violating traffic markings (VTM), and driving in the inaccurate oriented lane (DIOL) at signalized intersections by using data collected from traffic enforcement camera in Hohhot, China. To this end, an ordinary multinomial logit model was developed. By considering the unobserved heterogeneity between observations, a random effects multinomial logit model was proposed as well. After that, the marginal effects of explanatory variables were computed. The outcomes showed that non-local vehicles were more likely to commit WWD and VTM than local vehicles. WWD and RLR frequently occurred in the daytime and evening (6:00-23:59), and on most days within a week. RLR and DIOL mainly happened in June and July. The left-turn lane ratio significantly increased RLR and DIOL. The cloudy, partly cloudy, and rainy days obviously increased WWD and VTM. The temperature from 21 to 30 degrees centigrade was apparently associated with the higher likelihoods of RLR and DIOL. According to the findings of this study, some intervention measures, targeting different vehicle types and considering temporal factors, road, and weather conditions, were recommended to reduce WWD, RLR, VTM, and DIOL at signalized intersections.

UI	MeSH Term	Description	Entries
D002681	China	A country spanning from central Asia to the Pacific Ocean.	Inner Mongolia,Manchuria,People's Republic of China,Sinkiang,Mainland China
D004779	Environment Design	The structuring of the environment to permit or promote specific patterns of behavior.	Design, Environment,Healthy Places,Designs, Environment,Environment Designs,Healthy Place
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000063	Accidents, Traffic	Accidents on streets, roads, and highways involving drivers, passengers, pedestrians, or vehicles. Traffic accidents refer to AUTOMOBILES (passenger cars, buses, and trucks), BICYCLING, and MOTORCYCLES but not OFF-ROAD MOTOR VEHICLES; RAILROADS nor snowmobiles.	Traffic Collisions,Traffic Crashes,Traffic Accidents,Accident, Traffic,Collision, Traffic,Collisions, Traffic,Crashes, Traffic,Traffic Accident,Traffic Collision
D001334	Automobile Driving	The effect of environmental or physiological factors on the driver and driving ability. Included are driving fatigue, and the effect of drugs, disease, and physical disabilities on driving.	Automobile Drivings,Driving, Automobile,Drivings, Automobile
D012449	Safety	Freedom from exposure to danger and protection from the occurrence or risk of injury or loss. It suggests optimal precautions in the workplace, on the street, in the home, etc., and includes personal safety as well as the safety of property.	Safeties
D013997	Time Factors	Elements of limited time intervals, contributing to particular results or situations.	Time Series,Factor, Time,Time Factor
D014887	Weather	The state of the ATMOSPHERE over minutes to months.	Fog,Fogs
D016015	Logistic Models	Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.	Logistic Regression,Logit Models,Models, Logistic,Logistic Model,Logistic Regressions,Logit Model,Model, Logistic,Model, Logit,Models, Logit,Regression, Logistic,Regressions, Logistic