Articlegreen2020

Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit

Jinlei Zhang · Zhiyong Cui · Yinan Guo · Yadi Zhu

275 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Traffic Prediction and Management Techniques Railway Systems and Energy Efficiency Human Mobility and Location-Based Analysis

Abstract

Short-term passenger flow forecasting is an essential component in urban rail transit operation. Emerging deep learning models provide good insight into improving prediction precision. Therefore, we propose a deep learning architecture combining the residual network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) (called “ResLSTM”) to forecast short-term passenger flow in urban rail transit on a network scale. First, improved methodologies of the ResNet, GCN, and attention LSTM models are presented. Then, the model architecture is proposed, wherein ResNet is used to capture deep abstract spatial correlations between subway stations, GCN is applied to extract network topology information, and attention LSTM is used to extract temporal correlations. The model architecture includes four branches for inflow, outflow, graph-network topology, as well as weather conditions and air quality. To the best of our knowledge, this is the first time that air-quality indicators have been taken into account, and their influences on prediction precision quantified. Finally, ResLSTM is applied to the Beijing subway using three time granularities (10, 15, and 30 min) to conduct short-term passenger flow forecasting. A comparison of the prediction performance of ResLSTM with those of many state-of-the-art models illustrates the advantages and robustness of ResLSTM. Moreover, a comparison of the prediction precisions obtained for time granularities of 10, 15, and 30 min indicates that prediction precision increases with increasing time granularity. This study can provide subway operators with insight into short-term passenger flow forecasting by leveraging deep learning models.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 275 citations exist upstream.

References

2019T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction 2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2017LSTM network: a deep learning approach for short‐term traffic forecast 2016Using LSTM and GRU neural network methods for traffic flow prediction 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

Cited by

2021Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.