Next-Gen Travel Safety for Women: Integrating Advanced Technologies for Enhanced Security

In densely populated urban landscapes, women navigating taxis and auto-rickshaws encounter heightened risks of harassment, assault, and route deviations, often unaddressed by conventional navigation systems that favor efficiency over security. Drawing from India’s National Crime Records Bureau data, where over 80% of transport-related incidents target women, this paper introduces the Travelers Safety Solution—an AI-IoT hybrid platform engineered for proactive risk mitigation. The system fuses YOLOv8 for real-time object and driver detection, Tesseract OCR for license plate verification, Google Maps API for safety-prioritized routing, and map-matching algorithms for deviation prediction, complemented by SMS alerts to emergency contacts and RTO database integration. Implemented via a Python/Flask backend with MySQL persistence, the prototype demonstrates 92% OCR accuracy, 95% detection precision, and 40% improved user-perceived safety in a 50-participant study. Through detailed simulations and empirical evaluations, we analyze performance under varied conditions, including low-light scenarios and network latency, revealing a 35% reduction in deviation risks compared to baselines like Uber. This work not only empowers female commuters but also sets a scalable blueprint for inclusive smart mobility, with implications for policy and urban planning.