Real-Time Route Recommendation Framework for Congested Urban Networks

Efficient route guidance in congested cities requires optimizing travel time, because signal timing, localized bottlenecks, and time-varying congestion can make longer routes faster than the shortest path. This paper presents a real-time route recommendation framework for Thessaloniki that integrates road network topology with spatiotemporal traffic speeds to support both shortest-distance and fastest-time routing. The framework preprocesses urban road data to construct distance- and time-weighted graphs, enabling dynamic route selection under peak and off-peak conditions. Using real-world mobility and traffic datasets, the proposed approach consistently recommends congestion-aware routes that can be physically longer yet significantly faster during heavy traffic. Experimental results across multiple scenarios demonstrate travel-time reductions of up to 88% during peak periods, while maintaining route optimality with respect to the selected objective. A comparative evaluation of A∗and Dijkstra's algorithm shows that A∗achieves lower execution times, making it more suitable for real-time navigation in dense urban networks.