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Pavement failure has contributed immensely to loss of lives, disruption to normal daily activities and increase amount of money being spent on maintenance annually. One of the causes is associated with inadequate investigations on subgrade materials. This study aimed at examining the geotechnical parameters as factors of pavement failure along Lagos–Ibadan Expressway. Samples were collected at the failed and stable portions on some selected road segments and subjected to laboratory tests including Natural Moisture Content (NMC), Linear Shrinkage(LS), particle size distribution and California Bearing Ratio(CBR). The NMC along the failed sections was on the high side (ranged from 13.11% to 26.89%) compared with the stable sections (ranged from 11.11% to 16.40%). Most of the tested soils have percentage passing 0.075mm sieve more than 35% maximum required by the Federal Ministry of Works and Housing for subgrade materials. The maximum dry density(MDD) for the samples at failed and stable sections ranged from 1550 kg/m3 to 1860 kg/m3; 1650 kg/m3 to 1980 kg/m3 respectively while the Optimum Moisture Content(OMC) ranged from 8.30% to 20.30%. The soaked CBR values ranged from 2% to 17% while the unsoaked values ranged from 4% to 25%. The increase in NMC along the failed portions could be as a result of high water table along these sections. Some of the materials at failed locations had values of LS > 8% which suggests high susceptibility to shrinkage and swelling which results in differential settlement and contributed to pavement failure along these sections of the road. Keywords: Geotechnical, Pavement failures, subsurface investigations, Expressway, subgrade materials.

Road safety engineering involves identifying influencing factors causing traffic crashes through accident data, carrying out detailed accident studies at different locations and implementing relevant remedial measures. This study was carried out to establish relationship between traffic accident characteristics (frequency and severity) and traffic and road design characteristics on a two-lane highway. Statistical models applied in traffic accident modeling are Poisson regression, Negative Binomial regression (NB), and Zero-Inflated Negative Binomial regression (ZINB).; Traffic flow and road geometry related variables were the independent variables of the models. Using Ilesha-Akure-Owo highway, South-West, Nigeria accident prediction models were developed on the basis of accident data obtained from Federal Road Safety Commission (FRSC) during a 4-year monitoring period extending between 2012 and 2015. Curve radius (CR), lane width (LW), shoulder factor (SF), access road (CHAR), average annual daily traffic (AADT), parentage heavy good vehicle (HGV) and traffic sign posted (TSP) were the identified effective factors on crash occurrence probability. Finally, a comparison of the three models developed proved the efficiency of ZINB models against traditional Poisson and NB models. Keywords— Traffic accidents. Single carriageway, accident prediction model, road geometric characteristics.

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In Nigeria, literature on the integration of traffic of pavement condition and traffic characteristics in predicting road traffic accident frequency on 2-lane highways are scanty, hence this article to fill the gap. A comparison of road traffic accident frequency prediction models on IIesha-Akure-Owo road based on the data observed between 2012 and 2014 is presented. Negative Binomial (NB), Ordered Logistic (OL) and Zero Inflated Negative Binomial (ZINB) models were used to model the frequency of road traffic accident occurrence using road traffic accident data from the Federal Road Safety Commission (FRSC) and pavement conditions parameters from pavement evaluation unit of the Federal Ministry of Works, Kaduna. The explanatory variables were: annual average daily traffic (aadt), shoulder factor (sf), rut depth (rd), pavement condition index (pci), and international roughness index (iri). The explanatory variables that were statistically significant for the three models are aadt, sf and iri with the estimated coefficients having the expected signs. The number of road traffic accident on the road increases with the traffic volume and the international roughness index while it decreases with shoulder factor. The systematic variation explained by the models amounts to 87.7, 78.1 and 74.4% for NB, ZINB and OL respectively. The research findings suggest the accident prediction models that should be integrated into pavement rehabilitation.   Keywords: