Noise level prediction model was built for road side traders at an open market area of Wurukum market in Makurdi town. One week survey was carried out to measure noise level, traffic volume, vehicles speeds and distances from the edge of road shoulder. The study revealed that roadside traders expose themselves to average noise levels of 82.33dB (A), 77.48dB (A) and 74.38dB (A) at distances of 0m, 5m and 10m respectively from the edge of roadway. At 0m, the noise level was slightly below World Health Organization (WHO) specification of 85dB (A), beyond which noise pollution is hazardous. At 10m, the noise level was above minimum safe level of 55dB (A). The model indicates high level of ambient noise which was attributed to intensive market activities from the background. The model was checked using Chi square test at 5% level of significance and coefficient of determination (R 2 =0.7216) which gave satisfactory results. A strategy of relocation and splitting the market was proposed for local government council's consideration.

This study built a Simulation Model (SM) using SimEvents toolbox in MATLAB for implementing Analytical Models (AM) of queuing process at airport check-in system. Air travel demand data for Manchester and Leeds-Bradford airports in 2014 were adopted for validation of the model. There was no statistical difference between utilisation factor (UF) and service times of AM and SM outputs. Differences in AM and SM outputs for average queue length, average waiting time on queue and average number of arrivals and throughputs were attributed to variations in discrete time events considered by SM in contrary to the AM which assumed constant values for the process. The SM exhibited stochastic behaviour which actually depicts reality hence produces more reliable results. Stochastic analysis methods are therefore recommended for queuing analysis to achieve accurate results. The SM is therefore recommended to give Airport managers prior knowledge of system performance for planning and improved level of service (LOS) at airports.

This study assessed the Pavement Condition Index (PCI) of flexible road pavements on the University of Agriculture Makurdi (UAM) Campus. The ASTM D6433 standard manual for assessing flexible road pavement condition manually was adopted. Analysis of results using descriptive statistics revealed that over 65% of the road pavement on the UAM campus were rated within the range of poor to failed state. Some segments at different routes were rated as fair and above (up to good) conditions, these routes and their corresponding percentages for fair and above conditions included; the Ring Road (RR) at 69.3%, University Entrance Gate-Clinic Junction (GCJ) route at 42.9%, Hostel Junction-Water Works (HJWW) at 33.3%, Clinic Junction-Ring Road Junction (CRRJ) at 26.7%, Staff Quarters Street (SQS) at 25%, South Core Bus Station-Entrance Gate Junction (SCG) at 9.1% and South Core Bus Station-Veterinary Auditorium (SCV) at 0%. The RR route had relatively high percentage of fair to good road pavement segments, while the SCV route had relatively high fraction of pavement with worse condition. Timely and total rehabilitation of flexible road pavement on the UAM campus were recommended since over 65% of the road network is at deplorable state, the prompt rehabilitation of the worse routes such as the SCV and SCG routes (having the least % for fair and above conditions) should be given priority. Also, the patching of potholes at this stage of deterioration is essential to prevent further damages and thereby reducing cost of rehabilitation in the future.

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