SafeZone: An AI-powered Safety-aware Locality Recommendation Platform for Indian Urban Environments

Abstract

Urban travel and movement have become an important part of everyday life, but selecting a suitable place cannot always depend only on distance, price, convenience, or user ratings. Safety is also an important factor for people travelling alone, families, commuters, and visitors who may not be familiar with a city. However, existing navigation and location-based applications generally emphasize convenience and efficiency, while safety-related information is often treated as a secondary attribute. To address this issue, this paper presents SafeZone, an AI-assisted safety-aware locality recommendation platform designed for Indian urban environments. The proposed system combines locality information, user preferences, safety ratings, and distance to generate ranked recommendations. SafeZone is implemented as a client-side web application and supports six Indian cities—Rajahmundry, Kakinada, Hyderabad, Bangalore, Delhi, and Mumbai—and six categories: hospitals, restaurants, banks, police stations, transport hubs, and tourist spots. A four-step preference-elicitation wizard captures city, category, minimum safety level, minimum rating, maximum budget, ambience, and visit purpose. These preferences are processed through a multi-criteria filter-and-rank mechanism that first applies user-defined constraints and then computes a composite score using rating, safety score, and distance. The SafeZone hybrid strategy was evaluated against keyword search, rule-based filtering, collaborative filtering, and content-based filtering using 1,000 simulated user sessions and 200 expert-annotated query-result pairs. At K = 10, SafeZone achieved a precision of 0.88, recall of 0.85, and F1-score of 0.86, exceeding the evaluated baselines. The findings indicate that combining safety constraints with weighted ranking can produce focused recommendations with low response time at the current database scale.