Getu Segne (PhD)Sisay Asrat2024-03-122024-03-122023-06https://etd.aau.edu.et/handle/123456789/2449The conduct of red-light-running is widespread and growing in the world. The problem has been explored in different countries with less attention also in Addis Ababa. This paper examines the relationship between violation frequency and influencing factors related to red light running. The data for the current study has been taken from Addis Ababa traffic police; reported traffic rule violations including red light running behavior in Addis Ababa for four years (2018 to 2021) were analyzed using an Excel spreadsheet. Structured questionnaires were also distributed to the willing drivers with 412 rates of participants and analyzed using a chi-square test to demonstrate the rate and contributing factors to red light running. In addition, four signalized intersections namely Shola Gebeya, Legehar, Churchill, and Semen Hotel were randomly selected. Multiple linear regression analysis has been calibrated to assess the effect of the independent variables on the red-light running violations as the dependent variable by SPSS software. From the statistical analysis made on the collected traffic rule violation data, it can be seen red-light running violation rate is becoming increasing in recent years in the city and it also shows that Bole and Lideta sub-cities have higher rates of violation among the eleventh sub-cities. Gender, age, educational background, and driving experience were found as dominant influencing factors affecting red light running behavior in Addis Ababa. Most drivers were more likely to engage in RLR and would speed up at signalized intersections when they are in a hurry in the morning peak hour on working days they mostly prefer to run the red light to save time. Empirical results exposed that Car, number of lanes, red light time, cross road width and green light time, were found statistically significant at 95% of significance. Green light time, heavy vehicle, and cross road width are negatively related variable with red light running and affects negatively while car, bus, yellow light runners, Pedestrian, number of lanes, grade and red-light time are positively affecting the red light running. Finally, low-cost engineering countermeasures including optimizing the traffic light, education, and awareness campaign are recommended to reduce the number of red-light running conduct at signalized intersections. The education on red light running offenses must be accompanied by frequent Police enforcement of the traffic law to reduce the degree of violation.en-USRLR (red light running), Signalized intersection, SPSS, Multiple Linear Regression, Binary logistic regression, driver behaviorThesis