Reliable traffic load characterization remains a critical challenge in many African countries due to the lack of continuous field measurements. This study developed an integrated dynamic traffic monitoring and weigh-in-motion system on representative highways in Kenya to obtain long-term, multi-source traffic data. Traffic operations were quantified across hourly, weekly, and monthly scales, including flow variability, vehicle class composition, axle loads, overload behavior, and speed distributions. Results indicate that the spatiotemporal characteristics of traffic volume show pronounced short-term fluctuations but strong long-term stability. Despite their lower proportion, multi-axle heavy trucks dominate structural loading, with overload ratios exceeding 80% and gross weights approaching 100 t. Over 60% of vehicles operate at medium-to-low speeds (20–60 km/h), extending load duration and increasing pavement damage potential. These combined effects indicate that average indicators alone underestimate true loading demand. The proposed framework provides field-based traffic load spectra and a transferable methodology for traffic monitoring and pavement design optimization across developing regions in Africa.

Heavy and overloaded freight traffic strongly affects pavement performance, yet short-term weigh-in-motion (WIM) measurements are not easily converted into design-oriented traffic inputs. Using the Nairobi Southern Bypass in Kenya as a case study, this study develops axle load spectrum (ALS) and equivalent single axle load (ESAL) indicators from more than 1.5 million axle group records collected between June and December 2025 and proposes an XGBoost-based conditional axle load spectrum (CA-ALS) framework. The data revealed strongly right-skewed load distributions, with a limited number of heavily loaded axle groups dominating pavement damage. Compared with the static ALS by axle group type baseline, the CA-ALS reduced log loss from 2.7563 to 2.6709 in conditional spectrum prediction. In the December 2025 tandem axle benchmark, the CA-ALS increased the ESAL-based verification input by 6.0% at b = 4 and 11.1% at b = 5 relative to the stronger static reference. A legal-load-capped counterfactual analysis further showed that, for all heavy vehicles, observed overloading increased ESAL by 161.0% at b = 4 and 239.4% at b = 5. These results indicate that the CA-ALS provides condition-sensitive traffic inputs for design traffic verification, scenario-based pavement checks, and overload-sensitive evaluation based on short-term WIM observations.