analiza aiqbench
This research proposes CARRS, a unified software architecture that integrates classical pathfinding algorithms with Constraint Satisfaction Problem methodologies to optimize routes for emergency vehicles in dynamic urban traffic. By achieving sub-35-second route generation and over 90% constraint adherence, the system demonstrates how algorithmic optimization directly addresses critical latency issues in life-saving operations without relying on deep learning models. The study highlights a practical application of advanced routing logic where AI-driven optimization bridges the gap between theoretical efficiency and operational compliance for first responders.
CARRS: A Constraint-Aware, Real-Time Routing System for Emergency Vehicles in Urban Environments
arXiv:2610.26346v1 Announce Type: new Abstract: Emergency vehicles such as police vehicles, fire engines, and ambulances require a stable and efficient route optimization to reach life-critical events in the shortest times possible. However, the dynamic nature of urban traffic makes this ideal scenario much more challenging, causing critical life-threatening delays. Traditional civilian navigation systems cannot account for real-time traffic changes alongside vehicle-specific constraints, creating inadequate routing scenarios. This research presents the Constraint-Aware Real-Time Routing System (CARRS), a unified software architecture tailored for emergency coordination. The CARRS architecture is engineered to fuse together classical pathfinding algorithms such as A*, Dijkstra, and Bidirectional A*, with Constraint Satisfaction Problem methodologies. The Design Science Research Methodology is the research framework, and prototyping is the iterative process. The artifact was empirically evaluated using OpenStreetMap data for major metropolitan road networks, combined with simulated dynamic traffic scenarios. Performance metrics focused on route optimality, constraint adherence, rerouting responsiveness, and the potential for future system scalability. Findings revealed the system's capability to generate optimal routes for operationally relevant subgraphs in under 35 seconds, with incremental rerouting responsiveness below 20 seconds. Furthermore, the system achieved more than 90% adherence to vehicle-specific constraints, making the CARRS architecture viable for real-time dynamic routing requirements. This study delivers a routing platform that bridges artificial intelligence-driven optimization with software engineering operational compliance, presenting a practical transformative step toward adaptive emergency vehicle coordination in dynamic urban environments.
Abstract:Emergency vehicles such as police vehicles, fire engines, and ambulances require a stable and efficient route optimization to reach life-critical events in the shortest times possible. However, the dynamic nature of urban traffic makes this ideal scenario much more challenging, causing critical life-threatening delays. Traditional civilian navigation systems cannot account for real-time traffic changes alongside vehicle-specific constraints, creating inadequate routing scenarios. This research presents the Constraint-Aware Real-Time Routing System (CARRS), a unified software architecture tailored for emergency coordination. The CARRS architecture is engineered to fuse together classical pathfinding algorithms such as A*, Dijkstra, and Bidirectional A*, with Constraint Satisfaction Problem methodologies. The Design Science Research Methodology is the research framework, and prototyping is the iterative process. The artifact was empirically evaluated using OpenStreetMap data for major metropolitan road networks, combined with simulated dynamic traffic scenarios. Performance metrics focused on route optimality, constraint adherence, rerouting responsiveness, and the potential for future system scalability. Findings revealed the system's capability to generate optimal routes for operationally relevant subgraphs in under 35 seconds, with incremental rerouting responsiveness below 20 seconds. Furthermore, the system achieved more than 90% adherence to vehicle-specific constraints, making the CARRS architecture viable for real-time dynamic routing requirements. This study delivers a routing platform that bridges artificial intelligence-driven optimization with software engineering operational compliance, presenting a practical transformative step toward adaptive emergency vehicle coordination in dynamic urban environments.
Artificial Intelligence (cs.AI)
arXiv:2610.26346 [cs.AI]
(or arXiv:2610.26346v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.26346 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 321 cuvinte