Forecasting Short-Term Passenger Flow : An Empirical Study on Shenzhen Metro

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

90 Scopus Citations
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Author(s)

  • Liyang Tang
  • Yang Zhao
  • Javier Cabrera
  • Jian Ma
  • Kwok Leung Tsui

Detail(s)

Original languageEnglish
Pages (from-to)3613-3622
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume20
Issue number10
Online published22 Nov 2018
Publication statusPublished - Oct 2019

Abstract

Forecasting short-term traffic flow has been a critical topic in transportation research for decades, which aims to facilitate dynamic traffic control proactively by monitoring the present traffic and foreseeing its immediate future. In this paper, we focus on forecasting short-term passenger flow at subway stations by utilizing the data collected through an automatic fare collection (AFC) system along with various external factors, where passenger flow refers to the volume of arrivals at stations during a given period of time. Along this line, we propose a data-driven three-stage framework for short-term passenger flow forecasting, consisting of traffic data profiling, feature extraction, and predictive modeling. We investigate the effect of temporal and spatial features as well as external weather influence on passenger flow forecasting. Various forecasting models, including the time series model auto-regressive integrated moving average, linear regression, and support vector regression, are employed for evaluating the performance of the proposed framework. Moreover, using a real data set collected from the Shenzhen AFC system, we conduct extensive experiments for methods validation, feature evaluation, and data resolution demonstration.

Research Area(s)

  • Feature extraction, feature extraction, Forecasting, Meteorology, Monitoring, multivariate linear regression, Predictive models, Short-term passenger flow forecasting, Support vector machines, SVR (support vector regression), time series., Transportation

Citation Format(s)

Forecasting Short-Term Passenger Flow: An Empirical Study on Shenzhen Metro. / Tang, Liyang; Zhao, Yang; Cabrera, Javier et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 20, No. 10, 10.2019, p. 3613-3622.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review