An Energy-Efficient Smart Bus Transport Management System with Blind-Spot Collision Detection Ability
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Abstract (arXiv)
Public bus transport systems in developing countries often suffer from a lack of real-time location updates and for users, making commuting inconvenient and unreliable for passengers. Furthermore, stopping at undesired locations rather than designated bus stops creates safety risks and contributes to roadblocks, often causing traffic congestion. Additionally, issues such as blind spots, along with a lack of following traffic laws, increase the chances of accidents. In this work, we address these challenges by proposing a smart public bus system along with intelligent bus stops that enhance safety, efficiency, and sustainability. Our approach includes a deep learning-based blind-spot warning system to help drivers avoid accidents with automated bus-stop detection to accurately identify bus stops, improving transit efficiency. We also introduce IoT-based solar-powered smart bus stops that show real-time passenger counts, along with an RFID-based card system to track where passengers board and exit. A smart door system ensures safer and more organised boarding, while real-time bus tracking keeps passengers informed. To connect all these features, we use an HTTP-based server for seamless communication between the interconnected network systems. Our proposed system demonstrated approximately 99% efficiency in real-time blind spot detection while stopping precisely at the bus stops. Furthermore, the server showed real-time location updates both to the users and at the bus stops, enhancing commuting efficiency. The proposed energy-efficient bus stop demonstrated 12.71kWh energy saving, promoting sustainable architecture. Full implementation and source code are available at: https://github.com/sadman-adib/MoveMe-IoT
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 28 references that are in the map. First posted on arXiv 3 Jan 2026.
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7 of 28 references.
References · 7
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
- 2020Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlationcited by 420
- 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
- 2021Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labelscited by 17
- 2020Real-time object detection method based on improved YOLOv4-tinycited by 9
- 2023The MCC approaches the geometric mean of precision and recall as true negatives approach infinitycited by 2
- 2022Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecturecited by 1
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