TY  - JOUR
PY  - 2023//
TI  - Measuring the route topography impact on real driving emissions based on neural network models
JO  - Environmental research
A1  - Xu, Hualong
A1  - Zhang, Li
A1  - Chang, Hong
A1  - Han, Jinlin
A1  - Wu, Dongmei
A1  - Gong, Xiangkun
A1  - Fu, Mingming
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - Route topography is an important test boundary of real driving emission (RDE) tests. However, the RDE test boundaries, such as atmospheric environment, driver behavior, route topography, and traffic congestion, are random, uncertain, and completely coupled. It is difficult to know to what extent route topography can determine on-road emissions, especially in a region with hilly topography. In this regard, the neural network predictor importance algorithms were proposed to measure the importance of the route topography test boundary. Based on tens of thousands of data window samples from the RDE tests in Chongqing, factor analysis was performed to reduce the data dimensionality and eliminate information overlap, and neural network models were established to predict pollutant emissions and calculate the relative importance of input variables. The results show that route topography is comparable to trip dynamics for on-road emissions but the importance of the route topography test boundary is not fully appreciated in the existing RDE regulation, making mountain cities suffer from severe vehicle emissions that are not effectively controlled.<p />  <p>Language: en</p>
LA  - en
SN  - 0013-9351
UR  - http://dx.doi.org/10.1016/j.envres.2023.116072
ID  - ref1
ER  -