A Smartphone-Based IoT System to Monitor Driving Behaviors by Using a Machine Learning Classifier

Chi Chung Cheung; Chun Ming Cheung; Chi Wang Tong

doi:10.1109/IICAIET59451.2023.10291395

A Smartphone-Based IoT System to Monitor Driving Behaviors by Using a Machine Learning Classifier

Chi Chung Cheung
, Chun Ming Cheung
, Chi Wang Tong

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

Traffic accidents cause great casualties and property losses all over the world. Thus, traffic safety is one of the very important topics in smart cities. One of the major causes of traffic accidents is bad driving behaviors. This paper proposes a smartphone-based IoT (Internet of Things) system to monitor driving behaviors by using a machine learning classifier. In this system, a smartphone is put into a car near the driver. A cloud server gets data from accelerators and GPS (Global Positioning System) sensors in the smartphone through a 4G/5G network. In the cloud server, an SVM (Support Vector Machine) classifier and a GPS speed-detecting algorithm are installed to identify abnormal driving behaviors and the instantaneous speed of the car. A user performance report is generated at the end of a journey. The report shows abnormal driving behaviors during the journey and information when the car is over the speed limit. We carried out some experiments and we found that the system can identify different abnormal driving behaviors and driving speed properly. Moreover, the system generated user performance reports properly.

Original language	English
Title of host publication	5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	66-71
Number of pages	6
ISBN (Electronic)	9798350304152
DOIs	https://doi.org/10.1109/IICAIET59451.2023.10291395
Publication status	Published - Sept 2023
Event	5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023 - Kota Kinabalu, Malaysia Duration: 12 Sept 2023 → 14 Sept 2023

Publication series

Name	5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

Conference

Conference	5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023
Country/Territory	Malaysia
City	Kota Kinabalu
Period	12/09/23 → 14/09/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

Keywords

Artificial Intelligence
Internet Of Things

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Information Systems and Management
Electrical and Electronic Engineering
Artificial Intelligence
Computer Science Applications
Instrumentation

More information

10.1109/IICAIET59451.2023.10291395

Cite this

Cheung, C. C., Cheung, C. M., & Tong, C. W. (2023). A Smartphone-Based IoT System to Monitor Driving Behaviors by Using a Machine Learning Classifier. In 5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023 (pp. 66-71). Article 10291395 (5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IICAIET59451.2023.10291395

Cheung, Chi Chung ; Cheung, Chun Ming ; Tong, Chi Wang. / A Smartphone-Based IoT System to Monitor Driving Behaviors by Using a Machine Learning Classifier. 5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 66-71 (5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023).

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abstract = "Traffic accidents cause great casualties and property losses all over the world. Thus, traffic safety is one of the very important topics in smart cities. One of the major causes of traffic accidents is bad driving behaviors. This paper proposes a smartphone-based IoT (Internet of Things) system to monitor driving behaviors by using a machine learning classifier. In this system, a smartphone is put into a car near the driver. A cloud server gets data from accelerators and GPS (Global Positioning System) sensors in the smartphone through a 4G/5G network. In the cloud server, an SVM (Support Vector Machine) classifier and a GPS speed-detecting algorithm are installed to identify abnormal driving behaviors and the instantaneous speed of the car. A user performance report is generated at the end of a journey. The report shows abnormal driving behaviors during the journey and information when the car is over the speed limit. We carried out some experiments and we found that the system can identify different abnormal driving behaviors and driving speed properly. Moreover, the system generated user performance reports properly.",

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Cheung, CC, Cheung, CM & Tong, CW 2023, A Smartphone-Based IoT System to Monitor Driving Behaviors by Using a Machine Learning Classifier. in 5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023., 10291395, 5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023, Institute of Electrical and Electronics Engineers Inc., pp. 66-71, 5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023, Kota Kinabalu, Malaysia, 12/09/23. https://doi.org/10.1109/IICAIET59451.2023.10291395

A Smartphone-Based IoT System to Monitor Driving Behaviors by Using a Machine Learning Classifier. / Cheung, Chi Chung; Cheung, Chun Ming; Tong, Chi Wang.
5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 66-71 10291395 (5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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PY - 2023/9

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N2 - Traffic accidents cause great casualties and property losses all over the world. Thus, traffic safety is one of the very important topics in smart cities. One of the major causes of traffic accidents is bad driving behaviors. This paper proposes a smartphone-based IoT (Internet of Things) system to monitor driving behaviors by using a machine learning classifier. In this system, a smartphone is put into a car near the driver. A cloud server gets data from accelerators and GPS (Global Positioning System) sensors in the smartphone through a 4G/5G network. In the cloud server, an SVM (Support Vector Machine) classifier and a GPS speed-detecting algorithm are installed to identify abnormal driving behaviors and the instantaneous speed of the car. A user performance report is generated at the end of a journey. The report shows abnormal driving behaviors during the journey and information when the car is over the speed limit. We carried out some experiments and we found that the system can identify different abnormal driving behaviors and driving speed properly. Moreover, the system generated user performance reports properly.

AB - Traffic accidents cause great casualties and property losses all over the world. Thus, traffic safety is one of the very important topics in smart cities. One of the major causes of traffic accidents is bad driving behaviors. This paper proposes a smartphone-based IoT (Internet of Things) system to monitor driving behaviors by using a machine learning classifier. In this system, a smartphone is put into a car near the driver. A cloud server gets data from accelerators and GPS (Global Positioning System) sensors in the smartphone through a 4G/5G network. In the cloud server, an SVM (Support Vector Machine) classifier and a GPS speed-detecting algorithm are installed to identify abnormal driving behaviors and the instantaneous speed of the car. A user performance report is generated at the end of a journey. The report shows abnormal driving behaviors during the journey and information when the car is over the speed limit. We carried out some experiments and we found that the system can identify different abnormal driving behaviors and driving speed properly. Moreover, the system generated user performance reports properly.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 12 September 2023 through 14 September 2023

ER -

Cheung CC, Cheung CM, Tong CW. A Smartphone-Based IoT System to Monitor Driving Behaviors by Using a Machine Learning Classifier. In 5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 66-71. 10291395. (5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023). doi: 10.1109/IICAIET59451.2023.10291395

A Smartphone-Based IoT System to Monitor Driving Behaviors by Using a Machine Learning Classifier

Abstract

Publication series

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UN SDGs

Keywords

ASJC Scopus subject areas

More information

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