Abstract
This study has examined the performance of University transport bus shuttle based on utilization using a Single-server queue system which occur if arrival and service rate is Poisson distributed (single queue) (M/M/1) queue. In the methodology, Single-server queue system was modelled based on Poisson Process with the introduction of Laplace Transform. It is concluded that the performance of University transport bus shuttle is 96.6 percent which indicates a very good performance such that the supply of shuttle bus in FUTA is capable of meeting the demand.
Highlights
The issues arising from transportation has continually subjected to various debates in the urban societies
Among the noticeable transportation problems in the University are traffic congestion; longer commuting; public transport inadequacy; difficulties for tricycles to have access to routes being plied by shuttle buses, challenge of freight distribution from one end of the University to another end, and other challenges which all impacts the performance of the University transport shuttle
Traffic Survey 3.1.1 Stopping time of shuttle bus Stopping time refers to the total time duration the shuttle bus spends at the bus stop
Summary
The issues arising from transportation has continually subjected to various debates in the urban societies. A similar study was conducted by Adanikin, Olutaiwo, and Obafemi (2017) on the performance study of University of Ado Ekiti (UNAD) transit shuttle buses. They adopted traffic volume, speed, density and revenue as main parameters of performance of transport shuttles, and find that the morning peak period (8.00am to 9.00am) has 234 vehicles/hr, evening peak period (2.00pm to 3.00pm) has 284 vehicles/hr, while the off-peak period (11.00am to 12.00pm) has 156 vehicles/hr. The average stopping time was 6.55 minutes, average interval between arrivals of motorists was 16.40 seconds, the average queue length was 14.23 people, and the average waiting time at the bus-stop 4.17 minutes These values were obtained using the queuing theory and shows much commuters time is lost on transit queues.
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