Deep Learning for Metro Short-Term Origin-Destination Passenger Flow Forecasting Considering Section Capacity Utilization Ratio

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Qingpeng Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)7943-7960
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number8
Online published28 Apr 2023
Publication statusPublished - Aug 2023

Abstract

Origin-destination (OD) short-term passenger flow forecasting (OD STPFF) in urban rail transit (URT) is essential for developing timely network measures. The capacity utilization ratios of critical sections are key factors in developing these measures. The OD pairs passing through critical sections require a higher prediction accuracy than others; however, most studies have raised equal concerns on the prediction accuracy of each OD pair, namely, prediction at the network level. To address this problem, we raise heterogeneous time-variant concerns on OD pairs and employ an operation-oriented deep-learning architecture called the spatiotemporal convolutional neural network (STCNN) for realizing short-term OD passenger flow prediction. The architecture contains OD pair importance calculation, lagged spatiotemporal relationship construction, lagged spatiotemporal learning, real-time information learning, and sequential-temporal learning blocks. To this end, critical OD pairs are ascertained first, and the topological lagged spatiotemporal relationship among critical OD pairs are constructed and then normalized into grid-shaped data. The third block utilizes a convolutional neural network (CNN) to learn the grid-shaped lagged spatiotemporal feature and real-time information in parallel. A temporal convolutional neural network (TCN) is utilized for learning the sequential-temporal feature at last. Further, we design a time-varying weighted masked loss function to jointly reinforce the concerns on critical OD pairs during model training. Finally, we test the proposed STCNN and its components on a field dataset from Chengdu Metro. Although the proposed STCNN performs only slightly better at the network level than the other models, it outperforms state-of-the-art methods with significant superiority on critical OD pairs. © 2023 IEEE.

Research Area(s)

  • Spatiotemporal phenomena, Convolutional neural networks, Real-time systems, Forecasting, Feature extraction, Predictive models, Correlation, Origin-destination prediction, deep learning, spatiotemporal feature, temporal convolutional neural network, NEURAL-NETWORKS, PREDICTION, ARCHITECTURE, DEMAND

Citation Format(s)

Deep Learning for Metro Short-Term Origin-Destination Passenger Flow Forecasting Considering Section Capacity Utilization Ratio. / Zhang, Yan; Sun, Keyang; Wen, Di et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 24, No. 8, 08.2023, p. 7943-7960.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review