Vehicles Departure Headway Modeling at Signalized Intersections: The Case of Addis Ababa, Ethiopia

No Thumbnail Available



Journal Title

Journal ISSN

Volume Title




Addis Ababa, capital of Ethiopia, is one of the most congested cities in Ethiopia. Road network of the city has 33 registered signalized intersections. Signalized intersections affect the capacity and level of service of the intersecting roads through controlling the amount of traffic able to use them. So understanding traffic parameters at signalized intersection is essential. Departure headway is one of the microscopic traffic flow parameters at signalized intersections which is used in capacity estimation, level of service evaluation and signal timing. Adopting inaccurate departure headway values could lead to inefficient use of intersection facility. In this study a more accurate way of estimating departure headway is proposed by developing applicable and replicable models. Four signalized intersections were selected for this study. A unique way of data collection was used in the study. The data was collected at the selected locations using Phantom 4 drones which provide a high quality video (100 frame rate per second) and a preferable aerial view of the traffic movement. Data was extracted from the video with maximum error of 3 micro seconds. Departure headway data for five vehicle types making left turning and through movement were fitted to a probability distribution. The 3-Prameter Weibull distribution fit departure headway data of left turning small bus, left turning large bus, left turning small truck & Large bus making through movement, with different shape, scale, and threshold parameters. The Largest Extreme Value distribution was found to fit departure headway data of Cars and Small bus making through movement with different location and scale parameters. The Log-Logistic distribution fit to departure headway data of large truck and small truck making through movement with different location and scale parameter. Cars making left turning movement follow a certain Gamma distribution and large trucks making left turning movement were found to follow lognormal distribution. Regression model was developed using multiple linear regression technique to predict departure headway. From the regression analysis vehicle type, Movement type, Lateral position of the vehicle, Approach Grade, Lane width, and Green time were identified as factors affecting departure headway and found to statistically significantly predict departure headway. The Developed models are applicable for signalized intersections in Addis Ababa. Interestingly, the developed regression model can also be used as a dynamic PCE value generator for various scenarios. PCE values can be computed by dividing estimated headway values of different vehicle classes to estimated headway values of a passenger car.



Departure Headway