Yana Qin
ABSTRACT
In recent years, with the rapid growth of car ownership, Chinese road traffic conditions have changed a lot. Governments, enterprises, and the public are increasingly finding that the increasing deviation between the actual fuel consumption and the results of the regulatory certification based on NEDC (New European Driving Cycle). In addition, this deviation has seriously affected the credibility of the government, energy saving and emission reduction of automobiles and environmental pollution. Thus, need to improve urban driving cycle construction methods to adapt the Chinese traffic and automobiles driving cycles.
This paper proposes an advanced data science model based on big data analysis for accurate urban driving cycle construction in Chinese cities. In addition, we conduct a lot of data analysis and statistics. Then we design a data preprocessing method for cleaning the noise data to use in driving cycle construction. Extensive experiments and analysis on real-world datasets demonstrate that the proposed methods can significantly reduce the impact of missing and abnormal data on microtrips segmentation, and thus the proposed methods can be used for driving cycle construction in China more accurately.
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Index Terms
- An Advanced Data Science Model Based on Big Data Analytics for Urban Driving Cycle Construction in China
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