# Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit

arXiv: https://arxiv.org/abs/1912.12563 · HTML: https://mlsanity.com/paper/1912.12563/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2019.

## Built on by (most cited first, 25 of 25)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [MLPST: MLP is All You Need for Spatio-Temporal Prediction](https://mlsanity.com/paper/2309.13363/) (2023)
- [LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction](https://mlsanity.com/paper/2304.14343/) (2023)
- [GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2311.04245/) (2023)
- [Improving short-term bike sharing demand forecast through an irregular convolutional neural network](https://mlsanity.com/paper/2202.04376/) (2022)
- [Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction](https://mlsanity.com/paper/2204.02650/) (2022)
- [Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow](https://mlsanity.com/paper/2107.13226/) (2021)
- [Short-term passenger flow prediction for multi-traffic modes: A Transformer and residual network based multi-task learning method](https://mlsanity.com/paper/2203.00422/) (2022)
- [XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility](https://mlsanity.com/paper/2310.09847/) (2023)
- [Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable Data](https://mlsanity.com/paper/2108.03900/) (2021)
- [Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction](https://mlsanity.com/paper/2207.11034/) (2022)
- [Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]](https://mlsanity.com/paper/2308.12899/) (2023)
- [Leveraging Intra-Period and Inter-Period Features for Enhanced Passenger Flow Prediction of Subway Stations](https://mlsanity.com/paper/2410.14727/) (2024)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Models](https://mlsanity.com/paper/2501.03583/) (2025)
- [Vision-LLMs for Spatiotemporal Traffic Forecasting](https://mlsanity.com/paper/2510.11282/) (2025)
- [RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models](https://mlsanity.com/paper/2607.06504/) (2026)
- [Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach](https://mlsanity.com/paper/2108.09292/) (2021)
- [STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems](https://mlsanity.com/paper/2202.06727/) (2022)
- [An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems](https://mlsanity.com/paper/2202.10937/) (2022)
- [Spatial-Temporal Attention Fusion Network for short-term passenger flow prediction on holidays in urban rail transit systems](https://mlsanity.com/paper/2203.00007/) (2022)
- [Meta-learning Based Short-Term Passenger Flow Prediction for Newly-Operated Urban Rail Transit Stations](https://mlsanity.com/paper/2210.07098/) (2022)
- [Airport Passenger Flow Forecasting via Deformable Temporal-Spectral Transformer Approach](https://mlsanity.com/paper/2512.11845/) (2025)
- [A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting](https://mlsanity.com/paper/2609.13878/) (2026)

## Abstract (arXiv)

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.