Applying machine learning to construct braking emission model for real-world road driving

作者：Ning Wei, Zhengyu Men, Chunzhe Ren, Zhenyu Jia, Yanjie Zhang, Jiaxin Jin, Junyu Chang, Zongyan Lv, Dongping Guo, Zhiwen Yang, Jiliang Guo, Lin Wu, Jianfei Peng, Ting Wang, Zhuofei Du, Qijun Zhang, Hongjun Mao · 发表于：Environment International · 年份：2022 · DOI：10.1016/j.envint.2022.107386 · 被引用次数：20 · 研究领域：Vehicle emissions and performance、Air Quality and Health Impacts、Transportation Planning and Optimization

Brake emissions from vehicles are increasing as the number of vehicles increases. However, current research on brake emissions, particularly the intensity and characteristics of emissions under real road conditions, is significantly inadequate compared to exhaust emissions. To this end, a dataset of 600 (200 unique real-world braking events simulated using three types of brake pads) real-world braking events (called brake pad segments) was constructed and a mapping function between the average brake emission intensity of PM2.5 from the segments and the segment features was established by five algorithms (multiple linear regression (MLR) and four machine learning algorithms). Based on the five algorithms, the importance of the different features of the fragments was discussed and brake energy intensity (BEI) and metal content (MC) of the brake pad emissions were identified as the most significant factors affecting brake emissions and used as the final modeling features. Among the five algorithms, categorical boosting (CatBoost) had the best prediction performance, with a mean R2 and RMSE of 0.83 and 0.039 respectively for the tenfold cross-validation. In addition, the CatBoost-based model was further compared with the MOVES model to demonstrate its applicability. The CatBoost-based model has better prediction performance than the MOVES model. The MOVES model overpredicts brake fragment emissions for urban roads and underpredicts brake fragment emissions for motorways. Furtherm...