Road crashes are generally characterized by occurrence without prior notice as well as low or moderate to severe losses in terms of lives and properties. In particular, rural communities in developing countries count huge losses to crash exigencies due to lack of basic infrastructure. It is important that such accident trends which possess special and distinct features from those observed for Urban settlement be carefully studied and modeled for effective mitigation strategies. This paper proposes models that quantitatively assess the effects of various causal factors of crashes in rural roads of developing communities. Taking data from Nsukka municipality, South East Nigeria, it provides two reliable, statistically significant techniques of predicting accident rates on such roads. The results show that although important causal variables like “illiteracy, weather condition, alcohol and drugs” affect road accident, “reckless driving, over speeding, annual average daily traffic, poor road and mechanical faults” were variables that demand more assessment and control in road accident management in the studied location. Further, surface comparison of the developed models based on overall statistical indices suggests that multiple linear accident models are more accurate, though the quadratic accident model is statistically significant. A point by point calculation of deviations shows that the quadratic accident model, apart from its ability to take care of non-linearity effects of independent road accident variables, is actually more accurate than the linear model, except where the quadratic model suffered more numerical instability from the combination of numerical parameters.
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