UrbanPulse: A Dynamic Multi-Attribute A* Pathfinding and Real-Time Telemetry Framework for Intelligent Urban Bike Navigation

Rapid urban motorization and vehicular density have severely escalated safety hazards and physiological friction for non-motorized urban commuters.Commercial navigation systems such as Google Maps and Waze are architected primarily around vehicular throughput, optimizing purely for travel time while routing vulnerable road users (VRUs) onto high-stress arterial corridors, complex grade-separated junctions, and congested roadways.Conversely, existing open-source bicycle routing engines rely predominantly on static topological tagging from OpenStreetMap (OSM), rendering them incapable of adapting to live traffic spillovers, sudden intersection chokepoints, or dynamic road closures.To resolve this paradigm gap, this paper introduces UrbanPulse, a cloud-native, microservices-driven intelligent bike navigation platform.UrbanPulse pioneers a Dynamic Multi-Attribute Cost Model (DMACM) coupled with a Modified A* Pathfinding Engine operating over R-Tree indexed spatial graph representations of urban networks.The engine dynamically ingests real-time vehicular flow telemetry and traffic incidents, synthesizes multi-factor edge weights encompassing segment lengths, velocity degradation, dedicated cycle track segregation bonuses, surface roughness friction, signal latency, and geometric turn penalties derived from vector azimuth differentials.We provide formal mathematical proofs of the heuristic's admissibility and consistency, guaranteeing optimal path convergence.Benchmarked against the dense, mixed-traffic urban road network of Bengaluru, India (|V| ≈ 102,400 nodes, |E| ≈ 248,600 edges), UrbanPulse achieves sub-5ms path computation latency, yields a 38.4% reduction in cyclist exposure to arterial traffic hazards, and demonstrates an 84.2% utilization rate of segregated cycling infrastructure compared to baseline routing systems.