ANALYSIS AND EVALUATION OF THE INFLUENCES OF NON-MOTORIZED VEHICLES ON ROADWAY CAPACITY UNDER HETEROGENEOUS TRAFFIC CONDITIONS: A CASE STUDY SHASHAMANE TOWN

Abstract

n most Ethiopian cities, notably Shashamane town, traffic conditions are highly heterogeneous in nature and comprise motorized and non-motorized vehicles (NMVs). Under mixed traffic, the presence of NMVs with motorized vehicles on the same roadway affects the speed of motorized vehicles, the capacity of the roadway, and the overall performance of the road. The main objective of this study was to analyze and evaluate the influences of non motorized vehicles on roadway capacity under heterogeneous traffic conditions in Shashamane town. The analysis was conducted using traffic data collected from the three road segments with the help of videographic data collection methods and geometric data measured in the field. These segments connect Shashamane town with Addis Ababa, Halaba, and Dodola. The fundamental diagrams were developed based on the Greenshields model for five different study locations for estimating capacities. Additionally, multiple linear regression models were created using SPSS software to predict the average speed and roadway capacity. The study revealed donkey carts were the most commonly used NMVs in the area, followed by bicycles and hand carts. The capacity of the segments was found to be 1316 pcu/hr to 1562 pcu/hr at the section, with high and low percentages of NMVs, respectively. Factors such as total traffic volume, the percentage of NMVs, the percentage of heavy vehicles, and shoulder types were identified as significant variables that affect average traffic speed. Roadway capacity was found to be highly affected by the proportion of NMVs and shoulder types. The developed MLR models demonstrated a strong fit, with R-square values of 91.2% for average speed and 99.8% for roadway capacity. Specifically, at the maximum percentage of NMVs, the average traffic speed decreased by 18.32%, and capacity reduced by 14.39% in locations with unpaved shoulders. During peak periods, the road segment with a high proportion of NMVs indicated low performance, found at LOS D and E. The study reveals that NMVs significantly affect average traffic speed and roadway capacity, and the developed model effectively determines the speed and capacity of two-lane roads under their influence. Based on the study's findings, recommendations for improvement measures were proposed, including converting unpaved shoulders to paved ones and providing separate lanes for NMVs