環境汚染と気候変動

抽象的な

A Machine Learning Approach for Light-Duty Vehicle Idling Emission Estimation Based on Real Driving and Environmental Information

Qing Li, Fengxiang Qiao, Lei Yu

The conventional models for idling emission estimation are mainly based on ambient temperature and the status of vehicle itself, such as vehicle type/size, age and accumulated mileage and fuel type. Instant vehicle activity information is seldom taken into account. In this research, a machine learning approach is proposed to dynamically estimate vehicle emission rates while idling, based on real-world driving tests on more than 1,600 km highways in the State of Texas in the USA. One driver drove a dedicated light-duty gasoline vehicle on various types of roads, including interstate freeways, farm roads, state highways, and arterial road. During each episode of idling, rates of vehicle exhaust emissions, including carbon dioxide (CO₂), carbon monoxide (CO), hydrocarbon (HC) and nitrogen oxides (NO_x) were measured by a Portable Emission Measurement System (PEMS). Meanwhile, the real-time vehicle engine information of the test vehicle, such as revolutions per min, intake air temperature, and environmental information (e.g. ambient temperature), were collected through the On-board Diagnosis II port. Five machine learning algorithms were applied to build up idling emission models to illustrate the nature of emission patterns. Results show that Boosted and Bagged Decision Trees (BBDT) based idling emission model was identified as the best-fit ones for dynamic idling emissions with better prediction performance.