Implementation of Dijkstra Algorithm in Vehicle Routing to Improve Traffic Issues in Urban Areas

Many studies have been conducted on Dijkstra Algorithm, and one of the implementations of those studies includes vehicle path selection or routing. A traditional Dijkstra Algorithm can compute simple path routing problems; however, it is not suitable for complex situations. This paper proposes an improved method of Dijkstra Algorithm that analyzes influencing factors, such as: traffic congestion, travel time reliability, weight of each equivalent path and then produces the best path according to each situation. This new method is a beneficial improvement in efficiency for shortest path search algorithms and reduces time complexity, and so, this paper applies the Dijkstra Algorithm to analyze travel routes between Bung Karno Stadium and Merlynn Park. A node diagram is created based on cost tables and transformed into cost metrics. The goal is to find the shortest route while considering traffic data and analyzing differences with road distances. The cost metrics are inserted into the Dijkstra Algorithm, resulting in minimum costs for each vertex from the source node. Two optimal travel paths are determined based on different datasets, highlighting the impact of traffic data. Further evaluations consider travel times and costs with and without traffic. Incorporating predicted traffic times into the algorithm improves travel cost predictions. This research concludes that the Dijkstra Algorithm is effective in solving the shortest route problem and can be applied to urban traffic networks with the inclusion of traffic predictions, addressing drawbacks and enhancing route planning effectiveness.