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

Authors

M.N. Mazwir

Universiti Kuala Lumpur, Bandar Baru Bangi, Selangor, Malaysia (Malaysia)

N.A. Ajib

Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia (Malaysia)

N. Harum

Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia (Malaysia)

Article Information

DOI: 10.47772/IJRISS.2026.100800942

Subject Category: Transportation

Volume/Issue: 10/8 | Page No: 13759-13769

Publication Timeline

Submitted: 2026-09-07

Accepted: 2026-09-12

Published: 2026-09-21

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.

Keywords

Bus Bunching; Internet of Things (IoT); Real-Time Headway Monitoring; Driver Intervention; Public Transportation; GPS; Fleet Management; Service Reliability

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References

1. Shan, X., et al. (2023). Interfering Spatiotemporal Features and Causes of Bus Bunching Using Empirical GPS Trajectory Data. Journal of Transportation Engineering, 149(4), 04023012. [Google Scholar] [Crossref]

2. Ganguly, A., & Maitra, B. (2024). Bus Bunching at Bus Stops and Its Spatiotemporal Patterns along Urban Bus Routes. Journal of Urban Planning and Development, 150(1), 04023055. [Google Scholar] [Crossref]

3. Deng, L., et al. (2020). Reduce Bus Bunching with a Real-Time Speed Control Algorithm Considering Heterogeneous Roadway Conditions and Intersection Delays. IEEE Transactions on Intelligent Transportation Systems, 22(8), 5120-5131. [Google Scholar] [Crossref]

4. Moodi, M., et al. (2023). Investigating Reliability and Stability Parameters of City Buses for Intra-city Transportation based on GPS. International Journal of Sustainable Transportation, 17(5), 485-498. [Google Scholar] [Crossref]

5. Lizana, P., et al. (2014). Bus Control Strategy Application: Case Study of Santiago Transit System. Transportation Research Record, 2418(1), 33-41. [Google Scholar] [Crossref]

6. Quek, G., et al. (2021). Analysis and Simulation of Intervention Strategies against Bus Bunching by means of an Empirical Agent-Based Model. Public Transport, 13(2), 295-320. [Google Scholar] [Crossref]

7. Yue, C., & Liangliang, Z. (2024). Research on Intelligent Bus Scheduling Based on GPS Signals. IEEE International Conference on Intelligent Transportation, 112-117. [Google Scholar] [Crossref]

8. Kathuria, A., et al. (2020). Travel-Time Variability Analysis of Bus Rapid Transit (BRT) System Using GPS Data. Journal of Public Transportation, 22(1), 45-58. [Google Scholar] [Crossref]

9. Goncu, K., & Sahin, I. (2023). GPS-based incident detection algorithm for two-lane bus rapid transit systems: case study of Istanbul Metrobus. Transportmetrica A: Transport Science, 19(3), 201-224. [Google Scholar] [Crossref]

10. Khalid, A., et al. (2016). A Bus Tracking Information System using Consumer Grade GPS: A Case Study. Journal of Telecommunication, Electronic and Computer Engineering, 8(4), 89-93. [Google Scholar] [Crossref]

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