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Title	A Machine Learning-Based Approach To Detect Polluting Vehicles In Smart Cities
ID_Doc	2480
Authors	Afshar B.; Fathy M.; Asgari M.; Shahverdy M.; Shahverdi P.
Year	2022
Published	Proceeding of 6th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2022
DOI	http://dx.doi.org/10.1109/SCIoT56583.2022.9953644
Abstract	After the increase of pollution caused by vehicle congestion, new approaches to reduce pollution have emerged. Although regular visits to technical inspection centers and car repair center can be helpful, a new approach is to achieve effective integration between mobile applications and vehicles. This integration can be achieved using the ELM327 interface, which provides data such as speed, fuel consumption, gas emission, and system failure using the wireless interface to the mobile phone. Nowadays, vehicles have to go to technical inspection centers for pollution testing, which is costly in terms of time and price. This paper presents a machine learning-based method that uses data extracted from vehicle sensors and can determine the amount of pollution emitted from vehicle then warns the driver. Experimental results confirm that the proposed method can efficiently detect Polluting Vehicles. © 2022 IEEE.
Author Keywords	Engine Control Unit (ECU); Intelligent Transportation System (ITS); polluting Vehicle; Vehicle Sensors

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