Traffic congestion remains a pressing challenge in many urban areas, particularly within growing cities such as Calabar Metropolis. The increase in vehicle density on limited road networks often leads to delays, fuel wastage, and reduced commuter productivity. Traditional shortest path algorithms provide efficient distance-based routing but fail to adequately capture the impact of congestion on travel time and cost. This limitation highlights the need for a more dynamic and adaptive solution that integrates traffic conditions into route computation. This research aimed at developing a vehicular traffic optimization system using a modified Dijkstra shortest path algorithm. The system was designed to compute optimal paths within Calabar’s major road networks, dynamically adjusting edge weights to reflect congestion scenarios. Implementation was carried out using we development technologies, with a structured storage mechanism serving as the system’s database. The interface was equipped with a “Costometer” sidebar that presented route costs and travel distances, alongside clear visualization of roads such as Watt Roundabout, Manye Avenue, White House Street, Palm Street, and their adjoining routes. In its workings, the user selects the starting point, and target location, the system compute cost in minutes. While in a heavy traffic, the system recomputes cost from the current position of the user and provided another route helping the user to escape staying long in traffic. The system also provide information to the user if it is not possible to leave the traffic based on traffic condition. Experimental testing showed that the modified algorithm effectively rerouted traffic away from congested roads while maintaining efficiency in non-congested cases. The Costometer provided real-time route cost updates, while the storage system ensured efficient retrieval of traffic data. The results demonstrate that the proposed system offers a practical and reliable tool for congestion-aware navigation and traffic optimization in Calabar Metropolis. 

Keywords: Traffic Congestion, Optimization, Modified Shortest Path Algorithm, Costometer, Navigation.