MODEL DEVELOPMENT OF INFLUENCE OF SLOW MOVING VEHICLES ON ROAD CAPACITY AND LEVEL OF SERVICE - A CASE STUDY IN ADDIS ABABA CITY

Abstract

his research study investigates the influence of slow-moving vehicles on road capacity and level of service. The reason for slow-moving vehicles is due to the traffic composition, obstacles such as bus stops, street vendors, etc. on the road, and the geometric of the road. The main objective is to model and analyze the influence of Slow-moving vehicles on road capacity and level of service under variable roadway and traffic conditions in Addis Ababa City for Arada, Kolfe, and Nifasilk locations. For each of these locations during both peak and non-peak hours from 7 A.M to 7 P.M, for 15 minutes consecutive, measurements of road width, traffic volume, speed studies, and fundamental parameters will be performed to achieve the objective. A mathematical model is developed which uses field-measured variables on which multiple regression analysis is performed by using SPSS software for capacity values provided for urban roads. Relations between capacity and road width, percentage of heavy vehicles, road side activities were developed and identified that drives capacity affecting zones. This relation helps in studying variation with respect to various capacity-affecting variables. The result shows that traffic congestion is happened during peak periods. From Peak Hour Traffic Volume and composition analysis, the result shows that Nifasilk was larger than Kolfe by 9.14%, and Arada (Piasa) by 13.5% respectively. Although, from the SPSS analysis, as the percentage of (%) of Slow-moving vehicles (% of heavy vehicles) increases the capacity of the road decreases Nifasilk by 73.10%, Kolfe by 33.41%, and Arada by 79.54% respectively. By considering the variable roadway and vehicular characteristics, it is decided to model the Slow movement of the vehicles in the study area that will be useful to evaluate the influence of the Slow movement of the vehicles on road capacity band its level of service time to time. The results obtained in the research work may be useful for any administrative measures and or for any infrastructure improvements in the selected study area. Level of service (LOS) is found in all selected mid blocks based on HM criteria among each midblock. As shown by traffic data collected from the field the midblock are congested during peak periods. The level of service (LOS) of the entire stretch was found to be at the level of service E during the entire survey