The Development of Artificial Intelligence-Based Optimal Route Selection Framework for Rescue Services Process Management
DOI:
https://doi.org/10.23055/ijietap.2025.32.4.10479Abstract
An increase in urban traffic congestion has emerged as a critical bottleneck in the operational efficiency of emergency response systems, leading to substantial delays in rescue service deployment and a measurable increase in roadway mortality rates. Unplanned blockage placements by law enforcement agencies further disrupt traffic flow, elevating the vehicular density and impeding emergency response times. This study presents a data-driven framework that forecasts optimal blockage points and predicts congestion on alternative routes using a combination of operational research strategies and AI-based traffic modeling. The novelty of this work lies in leveraging AI-driven techniques to optimize blockage placement while minimizing disruptions near healthcare and public safety services. The framework employs supervised machine learning models to classify traffic flow (non-congested: 0, congested: 1) based on feature vectors linked to healthcare accessibility, achieving a 99% F1 score on both validation data and real-time traffic monitoring. Additionally, the A-star algorithm is utilized to determine the most efficient alternative routes post-blockage. To enhance practical usability, the framework is integrated into a Graphical User Interface (GUI) application capable of predicting congestion at specific time intervals throughout the day. This system serves as a decision-support tool for local agencies, aiding in strategic traffic planning and ensuring uninterrupted access to critical healthcare services. By mitigating congestion near essential service areas, the proposed approach enhances emergency response efficiency and contributes to overall public safety.
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