Affiliations: [a] School of Transportation, Jilin University, Changchun, China
| [b] College of Computer Science and Technology, Jilin University, Changchun, China
| [c] College of Software, Jilin University, Changchun, China
Abstract: A fuzzy multivariate based line traffic prediction model and a station traffic proportion inference model are proposed in order to predict line entrance traffic and station traffic in the Shanghai subway system. The nonlinear autoregressive with external input (NARX) is adopted to predict subway line traffic. Correlative features that influence the line traffic time series are identified by time series trend analysis, including meteorological features, time features, and proportion of commuter passenger features. A time series correlation method and fuzzy c-means (FCM) is proposed to simplify the feature set by deleting the features of small coefficients between features and traffic series. We determine the statistics of all stations’ traffic proportion of a subway line in order to construct a proportion matrix, and an eigenvector based station traffic proportion inference model is proposed to predict the future station traffic proportion, which is combined with the subway line traffic prediction results to realize subway station traffic prediction. We evaluate our model on the dataset of smart card records and weather condition dataset of one month in the Shanghai subway system. Experiment results confirm our proposed model’s advantages over baseline approaches.
Keywords: Fuzzy time series, NARX, eigen recognition, subway traffic prediction, urban computing
