01622576

Component

Understanding of public bicycle stations demand contributes to the redistribution and anomaly detection. Emergence of Smart card makes it possible to record information of riders’ trip with a continuous temporal coverage compared with traditional methods. The original data investigated in this paper were collected by public bicycle automatic fare collection system in Beijing, China. The withdrawal and return movements of public bicycle stations are studied by applying Principal Component Analysis (PCA) to the original data. Based on the results of the analysis, the authors find that the set of withdrawal and return movements has low intrinsic dimension, these usage demand structure can be accurately captured using a small number of independent components. The authors also decompose the structure of withdrawal and return timeserises into three categories: periodic trends, short-lived bursts, and noise. The authors provide insight into how the various constitutents contribute to the overall structure of usage (withdrawal and return movements) rates and explore the extent to which this decomposition varies over time. All these results play a critical role in the applications of forecasting and anomaly detection.

This paper was sponsored by TRB committee ABJ35 Standing Committee on Highway Traffic Monitoring.

01618707

17-02713

English

13p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References

Bicycles; Smart cards; Trend (Statistics); Vehicle sharing

Principal component analysis

Beijing (China)

Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting

Transportation Research Board Annual Meeting 2017 Paper #17-02713

TRIS, TRB, ATRI

Dec 8 2016 11:01AM