Research Article
A Machine Learning Approach for Light-Duty Vehicle Idling Emission Estimation Based on Real Driving and Environmental Information
Qing Li1*, Fengxiang Qiao1and Lei Yu21Innovative Transportation Research Institute, Texas Southern University, 3100 Cleburne Street, Houston, 77004, Texas, USA
2College of Science, Engineering and Technology, Texas Southern University, Texas, USA
- *Corresponding Author:
- Qing Li
Innovative Transportation Research Institute
Texas Southern University, 3100 Cleburne Street
Houston, 77004, Texas, USA
Tel: 713-313-7532
E-mail: liq@tsu.edu
Received date: December 07, 2016; Accepted date: December 15, 2016; Published date: December 22, 2016
Citation: Li Q, Qiao F, Yu L (2016) A Machine Learning Approach for Light-Duty Vehicle Idling Emission Estimation Based on Real Driving and Environmental Information. Environ Pollut Climate Change 1:106.
Copyright: © 2016 Li Q, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
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 (NOx) 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.
