Effect of Traffic Load Variability on Pavement Performance and Design Life Reliability

This study presents a quantitative assessment of critical traffic and environmental factors affecting pavement performance on the Kilombero road network using data from 10 observation points. Descriptive statistics, correlation analysis, and Multi-Criteria Decision Analysis (MCDA) were employed to systematically evaluate key determinants of pavement deterioration based on correlation strength, frequency of impact, and severity of effect. Axle Load Distribution emerged as the most critical factor (total effect = 30), indicating its dominant influence on structural damage through concentrated stresses. Traffic Load Variability (27) and Traffic Volume and Composition (24) were also highly influential, reflecting the effects of temporal weight fluctuations and heavy vehicle proportions on pavement fatigue. Factors such as Lane Distribution and Load Repetition (21), Road Geometry & Alignment (18), and Deceleration Patterns (10) exhibited moderate impacts, primarily affecting localised pavement stress and deterioration. Seasonal and Daily Fluctuations (8) and Ecosystem & Climate Impacts (6) were identified as less critical but still relevant under specific operational conditions. Normalisation and scoring frameworks enabled fair comparison of diverse variables, while correlation and coefficient of variation analyses highlighted periods of heightened risk. The results provide a systematic basis for prioritising maintenance interventions, optimising road design, and managing traffic loads, supporting data-driven strategies to extend pavement service life and ensure sustainable infrastructure performance.