A Predictive Model for Estimating Lifecycle Cost of Gravel Roads Based on Weather Conditions

Abstract

Gravel roads play a critical role in Tanzania's transportation network, especially in rural and peri-urban areas, where they serve as the primary means of mobility for people, goods, and essential services. However, their unbound nature and lower structural integrity make them highly vulnerable to the impacts of weather conditions. This study developed a lifecycle cost predictive model that assesses the effects of weather conditions on gravel road performance in TARURA–Kinondoni District. The research employed comprehensive field condition assessments, structured interviews with key stakeholders, a systematic review of maintenance records spanning 2019-2024, and detailed meteorological data analysis from the Tanzania Meteorological Authority. Data collection included road condition surveys of 25 gravel road segments, interviews with 90 technical personnel, and analysis of climate data to establish weather pattern trends. A Relative Importance Index (RII) was used to prioritise the most influential climate-related factors, while multiple linear regression was applied to build a model capable of estimating lifecycle costs under varying climate conditions. Statistical validation included correlation analysis, residual diagnostics, and cross-validation using independent datasets. The results demonstrate that flash flooding (RII = 0.951), prolonged wet seasons (RII = 0.940), and increased rainfall intensity (RII = 0.938) are the most critical factors driving gravel road degradation. The developed model achieved strong predictive power with R² = 0.791 and adjusted R² = 0.774, explaining approximately 79.1% of the variance in gravel road lifecycle costs. Model validation using independent road segments showed prediction accuracy within 12% of actual costs in 85% of test cases. The regression equation provides a valuable forecasting tool that enhances TARURA's capacity for proactive planning and efficient resource allocation. This study contributes to the development of climate-resilient infrastructure by offering a scientific foundation for decision-making in road maintenance, investment prioritisation, and policy development.