Application of Queuing Theory to Traffic Congestion Analysis on the Asaba–Onitsha Bridge, Nigeria

Traffic congestion on major transportation corridors poses significant economic and social challenges, particularly in developing urban regions. This study applies queuing theory to analyze traffic congestion on the Asaba–Onitsha Bridge, a critical transportation link between Delta State and Anambra State, Nigeria. Using real-life traffic data, the study evaluates the performance of the bridge in terms of traffic intensity, queue length, and waiting time under varying service capacity conditions. A quantitative and observational research design was adopted. Primary traffic data were collected through direct field observation during morning and afternoon periods, with vehicles classified into small and big categories to reflect traffic composition. An M/M/2 queuing model was employed. Traffic performance was analyzed under three assumed service rates of 600 veh/hr, 800 veh/hr, and 1000 veh/hr to assess the impact of service capacity on congestion levels. The results reveal clear temporal variations in traffic flow, with the morning period experiencing significantly higher traffic volumes, largely due to the dominance of small vehicles, while the flow of big vehicles remains relatively stable throughout the day. At a service rate of 600 veh/hr, the system operates close to capacity during the morning peak, resulting in higher traffic intensity, noticeable queues, and increased waiting times, indicating a marginally adequate service level. Increasing the service rate to 800 veh/hr leads to substantial reductions in queue length and waiting time, producing efficient traffic flow during both peak and off-peak periods. The best performance is observed at a service rate of 1000 veh/hr, where traffic intensity remains low and near free-flow conditions are achieved even during peak demand. The study concludes that congestion on the Asaba–Onitsha Bridge is strongly influenced by service capacity and temporal traffic demand variations. Enhancing service capacity through improved traffic management or infrastructure expansion can significantly reduce congestion, minimize delays, and improve overall traffic flow on the bridge. The findings provide quantitative evidence to support capacity enhancement through improved traffic management, infrastructure development, and the integration of Intelligent Transportation Systems that provides a modern, cost-effective, and adaptive approach for optimizing existing infrastructure and minimizing traffic delays.