A Low-Cost Real-Time Bus Stop Notification System for Rural Transportation in India

Public transportation in rural India is often the only means for many to access work, education, markets, and healthcare. However, service reliability is low: buses may arrive only once per hour, schedules are inconsistent, and passengers lack real-time information. This leads to lost time, reduced earnings, and increased dependence on expensive or unsafe private transport. We address these challenges with a low-cost, real-time bus stop notification system designed for rural India. The system integrates on-board GPS units, solar-powered displays at bus stops, and a cloud backend. To ensure data delivery despite unreliable mobile networks, we combine LoRa wireless, MQTT, and SMS as a fallback. Bus arrival times are predicted using a Random Forest model trained on actual bus movement data. Multilingual LED displays (Tamil, Hindi, English) address digital literacy constraints. The system buffers data during network outages, achieving 95% data retention. GPS noise is reduced using a Kalman filter, lowering position errors by 39.7%. Field tests on rural routes in Tamil Nadu show a mean arrival-time prediction error of 2 minutes, a 44% improvement over published schedules, and an 80% increase in prediction stability. A camera-based backup, using YOLOv8n, detects buses with 96.85% accuracy in areas with poor GPS coverage. The pilot, deployed on 8 routes and 12 stops serving approximately 2,500 daily passengers, reduced average passenger wait times by 15 minutes (47% improvement) and increased on- time arrivals by 20%. System uptime reached 98%. The solution reduces costs by over 90% compared to commercial alternatives, with hardware costs below 1,500 per bus and 10,200 per stop. Solar units operate for up to three days without sunlight, ensuring resilience during power outages. User feedback indicates 85% satisfaction with the displays, with 73% preferring information in Tamil. The system is cost-effective, reliable, robust to network disruptions, and accessible, providing a scalable model for rural transport information systems.

Intelligent Transportation Systems, rural mobility, bus tracking, Internet of Things, GPS, machine learning, arrival time prediction

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