What type of study is this?

This is a Cross-Sectional Study study (also classified as: Quantitative Study).

November 4, 2025

Assessment of the Level of Urban Traffic Congestion for Improved Mobility: Case Study of Selected Intersections in Ibadan, Nigeria

Key Points

Urban traffic congestion in Ibadan remains a significant issue, impacting mobility and city life.
The assessment indicates traffic control and infrastructure are vital for alleviating congestion in intersections.
Assessment using traffic surveys and regression techniques evaluated congestion causes and infrastructure impacts.
Improved traffic control can lead to enhanced infrastructure efficiency, fostering better urban mobility.

Abstract

The traffic problems in Ibadan can be primarily attributed to inadequate road infrastructure and a shortage of traffic control personnel. The objective of this study is to assess and analyze the current level of urban traffic congestion at specific intersections in Ibadan. A comprehensive traffic survey was conducted using a manual methodology for counting traffic volume. The volume to capacity ratio was employed to assess the levels of service at the Sango –Polytechnic Ibadan Road intersection, while the Kimber empirical regression technique was utilized to evaluate the capacity of the Iwo Road Intersection. The results indicated that the volume to capacity ratio at the Sango-Polytechnic Road Intersection ranges from 0.71, 0.72 and 0.74, resulting in consistent Levels of service classified as C. In contrast, the volume to capacity ratio at the Iwo Road Intersection ranges from 0.73, 0.77, 0.76, 0.90 and 0.96, corresponding to Levels of service classified as C, D and E. A questionnaire survey data was analyzed utilizing the Relative Important Index (RII) to ascertain the ranking of factors that contribute to and influence congestion at the selected intersections. The main factors contributing to congestion identified by the respondents are presence of a roadside market with RII of 0.888, lack of parking facilities resulting to on street parking with RII of 0.878, increased population with RII of 0.820, peak hour congestion with RII of 0.810 and poor driving behaviors with RII of 0.807. Binary logistic regression analysis shows that factors with RII ≥ 0.80 have high rating and R-square Nagelkerke of the model shows 95% of the variance in explanatory variables indicating that the overall model fit is good. The analysis demonstrated that well-designed and accessible pedestrian walkways can significantly reduce congestion caused by the factors, thereby fostering a more vibrant urban environment.

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