Journal Article

Analysis of Route-Way Dynamics in Urban Traffic Congestion of Enugu, Nigeria

Idoko, Augustine Enechojo, Chukwurah, Gladys Ogochukwu, Okeke, Francis Ogochukwu, Guo, Shuang, Nnaemeka-Okeke, Rosemary, Okeke, Chioma Angela, Isimah, Matthew Ogorchukwu, Okonta, Ebere Donatus, Awe, Foluso Charles

2025

Overview

Urban traffic congestion poses significant challenges to sustainable development in rapidly growing cities. This study examines the spatiotemporal dynamics of traffic congestion in Enugu, Nigeria, a representative mid-sized sub-Saharan city, through a comprehensive analysis of volumetric traffic flows along three major distributors: Abakpa, Nike, and Trans-Ekulu Road. The research employed direct observation and vehicle counts, conducting a week-long traffic census during peak morning (7:30–9:30 AM) and evening (4:00–8:00 PM) periods. Data was analyzed using peak hour factor (PHF), mean plots, and chi-square tests. The results reveal a daily mean of 2334 vehicles/h. Abakpa/Nike Road demonstrated the highest traffic volumes (mean = 809.2 vehicles/h) and most concentrated peak flows (PHF = 0.79), while Trans-Ekulu Road exhibited lower, more uniformly distributed volumes (mean = 719.4 vehicles/h, PHF = 0.93). Evening peaks (6:00–8:00 PM) consistently surpassed morning volumes, with Abakpa/Nike Road reaching 974 vehicles/hour during the evening rush compared to 620 vehicles/hour in the mornings. Chi-square analysis (χ2 = 55.5, df = 8) confirmed statistically significant differences in flow distribution among the routes. The complete absence of Monday traffic due to regional “sit-at-home” orders created a distinctive weekly pattern, with Tuesdays experiencing disproportionate congestion as the de facto first workday. Non-linear relationships between volume increases and congestion severity were observed, where modest volume changes produced amplified system-wide effects. Spatial analysis revealed that evening congestion disparities between distributors (14.9%) significantly exceeded morning differences (8.9%), indicating uneven network utilization. These findings illuminate how socio-political factors, activity patterns, and complex network dynamics shape urban mobility in rapidly developing contexts. This study offers empirical evidence supporting targeted interventions, including Tuesday-specific traffic management, evening-focused congestion mitigation strategies, and corridor-specific infrastructure improvements to enhance mobility in this representative mid-sized sub-Saharan city.

• Publisher: MDPI AG
• Subject: Big Data / Cities / Economic activity / Energy consumption / Enugu city / Infrastructure / Mobility / peak hour factor / Roads & highways / Traffic congestion / Traffic flow / Transportation planning / Urban areas / urban mobility / Vehicles / volumetric analysis