Cleaning and Processing on the Electric Vehicle Telematics Data
The development of the Internet of Vehicles (IoV) enables companies to collect an increasing amount of telematics data, which creates plenty of new business opportunities. How to improve the integrity and precision of electric vehicle telematics data to effectively support the operation and management of vehicles is one of the thorniest problems in the electric vehicle industry. With the purpose of accurately collecting and calculating the driving mileage of electric vehicles, a series of data cleaning and processing methodologies were conducted on the real-world electric vehicle telematics data. More specifically, descriptive statistics was conducted on the data, and the statistical results showed the quality of the data in general. Above all, the driving mileage data were segmented according to the rotate speed of the electric motor, and the anomaly threshold of the driving mileage data was obtained by the box-plot method. Then, the typical anomalies in the data were screened out by the threshold and analysed, respectively. Ultimately, the real-time and offline abnormal processing algorithms are designed to process real-time and offline data, respectively. After debugging and improvement, these two sets of abnormal processing algorithms we designed have been able to run on a company’s big data cloud platform. According to the feedback of the operation results of real-world massive data, the two sets of algorithms can effectively improve the statistical accuracy of driving mileage data of electric vehicle.
KeywordsInternet of Vehicles Telematics data Data cleaning and processing Box-plot method
This research is supported by the National Key R&D Program of China under grant No. 2018YFC0706005 and No. 2018YFC0706000.
- 3.I. Reimers, B. Shiller, Welfare Implications of Proprietary Data Collection: An Application to Telematics in Auto Insurance (Social Science Electronic Publishing, 2018)Google Scholar
- 4.J. Lauer, L. Richter, T. Ellersiek, A. Zipf, TeleAgro+: analysis framework for agricultural telematics data, in ACM SIGSPATIAL International Workshop on Computational Transportation Science (ACM, 2014)Google Scholar