Cloud Computing-Based Passenger Flow Sensing and Analysis Methods for Metro Stations Using AFC Data

Analysis of passenger flow based on metro passenger travel data can provide an important reference basis for metro network planning and layout optimization. This paper proposes a metro big data analysis method based on cloud computing, the metro big data processing and analysis is divided into five stages: data reading, data storage, data cleaning, data analysis and data visualization. The data analysis is mainly based on the scale of passenger flow at the station, passenger travel time, etc. as an example to illustrate. And Ali cloud computing service big data platform as a tool, testing the computing efficiency of cloud computing technology and traditional database, proving that cloud computing has the advantages of fast processing speed and does not take up local resources. Finally, one week’s worth of swipe data from a city’s metro AFC is used as a case study, after preprocessing and cleaning the data, Ali Cloud Computing technology is used to process the big data to analyze the characteristics of metro passenger flow, and further test and compare the characteristics of Ali Cloud Computing technology and traditional SQL database technology, comparing the actual situation with the conclusions obtained from the data analysis, which verifies that the proposed big data analysis method has good general applicability. It has good implications for future research on metro big data analytics.