Simulation of Traffic Flow at a Major Interchange in Abuja Using Monte Carlo Methods

Urban traffic congestion at major interchanges in Abuja, Nigeria's Federal Capital Territory, represents a significant social and economic burden, increasing commute times, fuel consumption, and carbon emissions. This study employs Monte Carlo simulation to model and analyse traffic flow dynamics at the Berger Interchange in Abuja, North Central Nigeria. Traffic count data were collected manually during morning and evening peak periods over four weeks, capturing vehicle arrival rates, turning movements, and signal cycle lengths. Random number generation and empirical probability distributions derived from the collected data are used to simulate traffic behaviour under current and modified signal timing configurations. Five hundred simulation runs are executed to estimate key performance metrics including average vehicle delay, queue length, and throughput under each configuration. Results indicate that adjusting signal green times in favour of the highest-volume approaches reduces average delay by approximately 19 percent during peak hours. The simulation also identifies spillback conditions at two turning movements that current phasing fails to address adequately. Recommendations include adopting adaptive signal control technology and redesigning the eastbound channelization lane. The study demonstrates the value of simulation as a low-cost planning tool for traffic engineers and urban planners in Nigeria's rapidly urbanizing capital. The Monte Carlo methodology is particularly well-suited to capturing the inherent randomness of vehicle arrival patterns. Keywords: Monte Carlo simulation, traffic flow, Abuja, signal optimization, urban congestion