An IoT-Based Bus Bunching Prevention Framework Using Real-Time Headway Monitoring and Driver Intervention Mechanisms

Abstract

Bus bunching is one of the most persistent operational challenges in public transportation systems, causing irregular service intervals, prolonged passenger waiting times, vehicle overcrowding, and reduced service reliability. Existing solutions primarily focus on post-event analysis or complex optimization algorithms that are difficult to deploy in real-world transit environments. This study proposes an Internet of Things (IoT)-based bus bunching prevention framework that integrates real-time vehicle tracking, intelligent headway monitoring, automated driver intervention, and centralized fleet management within a unified architecture. The framework employs ESP32 microcontrollers and GPS modules installed on buses to continuously collect spatial and temporal vehicle data. A centralized web platform processes the data to calculate headways between consecutive buses and identifies potential bunching conditions when predefined thresholds are violated. Upon detection, automated alerts are transmitted to drivers through onboard notification devices and simultaneously displayed on an administrative monitoring dashboard, enabling immediate corrective actions. To evaluate the operational need for such a system, a survey was conducted among public transport users in Malaysia. Results revealed that 70.5% of respondents had experienced bus bunching, while 57.6% reported inconsistent arrival schedules as a major concern. Furthermore, 98.4% supported real-time driver notifications and 96.7% agreed that continuous fleet monitoring would improve service reliability. The findings demonstrate that integrating real-time monitoring with active intervention mechanisms provides a practical and scalable approach to preventing bus bunching and enhancing public transport performance.