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AI-driven smart neighborhood services: A real-time geospatial booking platform for emergency and on-demand service delivery

artificial-intelligence
computer-education
computer-vision
data-mining-and-machine-learning
spatial-and-geographic-information-systems

Abstract

The problem of connecting users with verified local service providers quickly has been a significant issue in the era of urban digital transformation, particularly in emergencies. Conventional techniques of service discovery, e.g., Directory or ad-hoc mobile applications, cannot provide real-time responsiveness, intelligent character prioritization, and accurate location. The AI-Driven Smart Neighborhood Services is an affordable, highly evolved real-time booking and online service and represents a complex geospatial intelligence, intelligent scheduling, and Progressive Web App (PWA) technology that can make it easy to discover and book professionals near their home or work areas. The technology incorporates the AI-enhanced Haversine formula and the K-Nearest Neighbor (KNN) algorithm to facilitate dynamic proximity matching, optimize booking scheduling using a Priority Queue, and employ a Bayesian average rating to ensure fair and impartial ranking of providers. The system is constructed on an expandable architecture (Next.js, Firebase Firestore, Google Maps API), which enables searching for emergency availability and provides a convenient user experience across all devices. Thorough testing by functionality modules reveals great performance efficiency, precision, and responsiveness, as well as almost real-time processing of emergency requests. It is scalable, and its future growth will expand to include predictive analytics, offline access via SMS, multilingual capabilities, and integration with local safety authorities. The work provides a strong foundation for implementing geospatially aware, intelligent service platforms in a smart city ecosystem, where a gap exists between trusted local services and real-time user demands.
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