ABSTRACT
Transport facilities are crucial for a society's economic and social development. This study aimed to analyze traffic flow on the Benin-Uselu road using the Greenberg and Underwood models to plan, manage, and predict traffic flow. The dominance of road transportation due to neglect of other modes has led to challenges in accommodating the growing population and vehicle ownership, potentially causing negative traffic conditions. The study was conducted on the Benin - Uselu road to analyze traffic flow. Traffic counts were manually conducted at three different time intervals (8:00am to 10:00am, 12:00pm to 2:00pm, and 4:00pm to 6:30pm) on specific sections of the road, including Ekosodin – Meghon (S1), Maingate to Aikhionbare Junction (S2), Adolor to Uwasota Junction (S3), and St. Patrick to technical Junction (S4). Additionally, a speed study was performed on vehicles using the road during these 15-minute intervals. From these studies, the time mean speed 𝑢𝑡 , the flow rate 𝑞, and the flow density 𝑘 were determined. Statistical methods were employed to assess the models' ability to predict flow characteristics using the collected data. Regression analysis was used with average speed and density data to fit curves and test the developed models. The results obtained indicates a negative correlation between mean speed 𝑢𝑡 and flow density in both models, showing that flow rate and density increase over time while speed decreases. For section S1, S2, S3 and S4, the logarithmic model has a goodness of fit 𝑟 2 of 0.9504, 0.9728, 0.9122, and 0.9370 respectively and the exponential model has a goodness of fit of 0.9454, 0.9712, 0.9052, and 0.9341 for section S1, S2, S3 and S4 respectively. Regarding the relationships between density and velocity, density and v volume, and volume and velocity, the logarithmic model performs better in the first two relationships, while both models are effective in the volume-velocity relationship. The negative correlation between speed and flow density highlights the need to address traffic bottlenecks and improve overall flow. The superiority of the exponential model in predicting flow characteristics suggests its suitability for traffic forecasting and future road planning. Policymakers can use these insights to formulate sustainable transportation policies, reducing environmental impacts and improving mobility in the region. Recommendations include investing in road network expansion, optimizing traffic management, continuous monitoring, and promoting alternative transportation modes to achieve a more efficient and sustainable system on the Benin-Uselu road.
