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The results of a study conducted to facilitate the development of road pavement performance models that are appropriate for Nigeria and similar developing countries and could predict the rate of deterioration over their lifespan have been presented. Comprehensive investigations were carried out on the expressway linking Lagos (the economic nerve centre of Nigeria) with Ibadan (the largest city in West Africa)-apparently one of the most heavily trafficked roads in the country. Data relating to traffic characteristics, pavement condition ratings, distress types, pavement thickness, roughness index, rainfall and temperature, were collected. Models were developed to determine Pavement Condition Score (PCS) and International Roughness Index (IRI). Stepwise Regression was used to analyse the data and quantify the impact of key input parameters on the PCS and IRI. Parameters such as depth of ruts and area of pot holes were found to be statistically significant in predicting PCS while number of patches, length of longitudinal cracks and depth of ruts were statistically significant in predicting IRI. The models can be used for planning road maintenance programs, thus minimizing the need for comprehensive data collection on pavement condition before the maintenance exercise, which is costly and time consuming.

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This study identified high-risk locations (hotspots) using geographic information systems (GIS) and spatial analysis. Five years of accident data (2013–2017) for the Lokoja-Abuja-Kaduna highway in Nigeria were used. The accident concentration analysis was conducted using the mean center analysis and Kernel density estimation method. These locations were further verified using Moran’s I statistics (spatial autocorrelation) to determine their clustering with statistical significance. Fishnet polygon and network spatial weight matrix approaches of the Getis–Ord Gi* statistic were used in the hotspot analysis. Hotspots exist for 2013, 2014, and 2017 with a significance level between 95–99%. However, hotspots for 2015 and 2016 have a low significance level and the pattern is random. The spatial autocorrelation analysis of the overall accident locations and the Moran’s I statistic showed that the distribution of the accidents on the study route is random. Thus, preventive measures for hotspot locations should be based on a yearly hotspot analysis. The average daily traffic values of 31,270 and 16,303 were obtained for the northbound and southbound directions of the Abaji–Abuja section. The results show that hotspot locations with high confidence levels are at points where there are geometric features.