Vehicles Departure Headway Modeling at Signalized Intersections: The Case of Addis Ababa, Ethiopia
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Date
2018-10
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AAU
Abstract
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.
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Keywords
Departure Headway