Elisha Akech Ochungo

Efficient axle-load control is essential for managing overloaded freight vehicles and limiting delays at highway weigh stations. Static weighbridges provide accurate confirmatory measurements but require vehicles to stop, whereas weigh-in-motion (WIM) systems screen vehicles in motion and have potential to increase throughput. This study develops a conditional machine-learning benchmark for evaluating the operational efficiency of axle-load control at the Athi River (Mlolongo) weighbridge station in Kenya. Operational records collected by the Kenya National Highways Authority between April and July 2026 were analyzed, comprising 1,410,389 vehicle passages, 5,808 station-hour observations, and 117 complete daily observations. A gradient-boosted regression model was used to predict static-scale confirmatory throughput from WIM-screened demand, weekday, month, and time trend. Performance was evaluated using five-fold cross-validation against fixed-constant and single-ratio benchmarks. A station-hour model was subsequently developed using hour, weekday, month, direction, hourly demand, and time trend, with eight-fold cross-validation grouped by calendar date. The daily conditional model achieved an out-of-sample R² of 0.84 and mean absolute error (MAE) of 88 vehicles/day, compared with R² = 0.00 and MAE = 227 vehicles/day for the fixed-constant model and R² = 0.39 and MAE = 184 vehicles/day for the single-ratio model. The station-hour model achieved R² = 0.90 and MAE = 4.7 vehicles/hour, compared with R² = 0.71 and MAE = 8.3 vehicles/hour for the hour-by-direction baseline. These results show that operational context substantially improves efficiency benchmarking. The findings support the use of WIM as a high-throughput screening mechanism, with suspected overloaded vehicles subsequently referred for confirmatory static weighing.

Background: Noise is one of the externalities of transportation systems in cities. To help understand which neighborhoods have noise levels exceeding the World Health Organization’s (WHO) thresholds, scholars have proposed the use of spatial noise maps. Most cities in developed nations have regularly produced these maps. Unfortunately, in developing nations, citizens still lack this important tool to help in the identification of quieter neighborhoods. One such, is Nairobi, the capital city of Kenya hence the reason for this study. In previous studies, varying methods have been used to produce Road Traffic Noise maps, such as Inverse Distance Weighting (IDW), Ordinary Kriging (OK), among others, each of which has its pros and cons. Aims: This study aimed to develop a GIS-based spatial model of Road Traffic Noise distribution in Nairobi city in Kenya. This was motivated by the fact that the ambient environmental conditions of urban neighborhoods are an indicator of how the welfare of their residents is taken care of. Study Design: This study deployed the GIS-based IDW method. Place and Duration of Study: The Department of Civil and Construction Engineering, University of Nairobi coordinated data collection across the city, between 6th July, 2025 and 12th July, 2025. Methodology: A seven-day Road Traffic Noise data for twelve hours from 6 AM to 6 PM using a handheld Class 1 Sound Level Meter was collected. The data was then bundled into two-hour intervals, resulting in six map presentations. GIS-based spatial maps were then subsequently produced using IDW method. Results: According to the maps, the noisiest time falls between 12 PM and 2 PM, while the low-noise periods occur from 8 AM to 10 AM and from 4 PM to 6 PM. Inferentially, noisy road corridors have high speed in the periods mentioned, and quieter corridors are heavily congested over the same time period. Notorious noisy road corridors in Nairobi include Ngong Road, Waiyaki Way, Eastleigh area, and Thika Road, and the quieter neighborhoods are in Runda and Karen areas. Conclusion: This study concludes that the GIS-based noise maps are helpful tools for residents to choose areas of habitation with a tolerable soundscape and to make noise management sustainable, by deploying GIS-based real-time noise maps accessible via the internet web display.

Today, the research interest on the state of mobility and accessibility of a place is growing everywhere. Previous studies on space-time convergence have shown that, the world has become ‘flat’ due to fastness in accessibility of places by goods, information and the people. Whereas this is true, the prevailing state of space-time convergence in Africa is still an outstanding issue of concern. This paper aims to fill this gap through story telling of the information borrowed randomly from existing literature on the subject matter. The results obtained show that, Africa is yet to fully get integrated proper into the global networked society because of her huge transport and communication infrastructure gap. The paper concludes with a recommendation that, African leaders should endeavour to fix the infrastructure gap and must at the same time, purpose to allow a faster cross border movement of goods and people to help speed up space-time convergence to match the global mobility pace.