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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Machine Learning-based Approach to Detect Polluting Vehicles in Smart Cities


    Contributors:


    Publication date :

    2022-09-14


    Size :

    925427 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Low-polluting gas fueled heavy-duty vehicles

    Nylund, N.O. / Riikonen, A. | Tema Archive | 1991


    Low-polluting gas fueled heavy-duty vehicles

    Nylund,N.O. / Riikonen,A. / Technical Research Centre of Finland,FI et al. | Automotive engineering | 1991


    Low-Polluting Gas Fueled Heavy-Duty Vehicles

    Riikonen, Arto / Nylund, Nils-Olof | SAE Technical Papers | 1991


    Status of low-polluting, energy conserving vehicles in Japan

    Kontani,K. / Mechanical Engineering Lab.,JP | Automotive engineering | 1990