Urban mobility has become one of the central challenges in modern transportation planning. Congestion, road closures, and inefficient routing increase travel time, fuel consumption, and emissions. To address these issues, computational models that can determine shortest and most efficient routes are essential. This study employs shortest route planning algorithms, specifically Dijkstra’s algorithm and the A* (A-star) algorithm, to suggest optimal routes for vehicular movement. Dijkstra’s algorithm guarantees the shortest path by exhaustively exploring all nodes, while A* enhances efficiency by incorporating heuristic estimations of the distance to the destination. The study compares both algorithms on a simulated road network and evaluates performance in terms of path optimality, computational efficiency, and applicability in dynamic environments. Results demonstrate that while Dijkstra’s algorithm is reliable for static networks, A* performs better in large and complex real-world road systems due to its heuristic-driven efficiency. These findings highlight the potential of shortest path algorithms to improve transportation systems and pave the way for intelligent traffic management applications. ©2025 ijrei.com. All rights reserved
Singh et al. (Wed,) studied this question.