ASSESSMENT OF TRAFFIC FLOW ON ENUGU HIGHWAYS USING SPEED DENSITY REGRESSION COEFFICIENT (Nigerian Journal of Technology, 2017)
In an attempt to estimate the operating speeds and volume of traffic on highway lanes as a function of predicted demands, speed-density models were estimated using data from highway sites. Speed, flow and volume are the most important elements of the traffic flow. In this study, the speed-density regression models are compared using five highways in relation to their correlation coefficient based on the daily traffic flow data obtained from the roads. The traffic flow data were collected by hourly traffic count on each road. The coefficient of correlation (R) proved to have the best fit with a higher confidence and less variation for a two-lane highway than a one-lane highway. The space-mean speed (u) and density (k) relationship for the two-lane highways are; u,  and u whereas the space-mean speed (u) and density (k) relationship for the one-lane highways are; u =  respectively. This research provides practical application for speed estimation, construction, maintenance and optimization of the highways using the speed-density models which will enhance traffic monitoring, traffic control management, traffic forecasting and model calibration. http://dx.doi.org/10.4314/njt.v36i3.13