Udoh Jeffrey, Lu Joan, Xu Qiang
Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
Sensors (Basel). 2024 Dec 23;24(24):8219. doi: 10.3390/s24248219.
Climate change caused by greenhouse gas (GHG) emissions is an escalating global issue, with the transportation sector being a significant contributor, accounting for approximately a quarter of all energy-related GHG emissions. In the transportation sector, vehicle emissions testing is a key part of ensuring compliance with environmental regulations. The Vehicle Certification Agency (VCA) of the UK plays a pivotal role in certifying vehicles for compliance with emissions and safety standards. One of the primary methods employed by the VCA to measure vehicle emissions for light-duty vehicles is the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). The WLTP is a global standard for testing vehicle emissions and fuel consumption, and sensors are crucial in ensuring accurate, real-time data collection in laboratories. Using the data collected by the VCA, regression machine learning models were trained to predict CO emissions in light-duty vehicles. Among six regression models tested, the Decision Tree Regression model achieved the highest accuracy, with a Mean Absolute Error (MAE) of 2.20 and a Mean Absolute Percentage Error (MAPE) of 1.69%. It was then deployed as a web application that provides users with accurate CO emission estimates for vehicles, enabling informed decisions to reduce GHG emissions. This research demonstrates the efficacy of machine learning and AI-driven approaches in fostering sustainability within the transportation sector.
温室气体（GHG）排放导致的气候变化是一个日益严重的全球问题，交通运输部门是主要贡献者之一，约占所有与能源相关的温室气体排放的四分之一。在交通运输部门，车辆排放测试是确保符合环境法规的关键部分。英国车辆认证机构（VCA）在认证车辆符合排放和安全标准方面发挥着关键作用。VCA用于测量轻型车辆排放的主要方法之一是全球统一轻型车辆测试程序（WLTP）。WLTP是测试车辆排放和燃油消耗的全球标准，传感器对于确保实验室中准确、实时的数据收集至关重要。利用VCA收集的数据，训练回归机器学习模型来预测轻型车辆的一氧化碳排放。在测试的六个回归模型中，决策树回归模型的准确率最高，平均绝对误差（MAE）为2.20，平均绝对百分比误差（MAPE）为1.69%。然后将其部署为一个网络应用程序，为用户提供车辆准确的一氧化碳排放估计，使他们能够做出明智的决策以减少温室气体排放。这项研究证明了机器学习和人工智能驱动方法在促进交通运输部门可持续发展方面的有效性。
