Multi-Step Passenger Flow Prediction for Urban Metro System Based on Spatial-Temporal Graph Neural Network
Abstract
:1. Introduction
- A novel spatiotemporal graph neural network (STGNN) is proposed for multistep passenger flow prediction in metro stations.
- A spatial modeling module is proposed, which consists of a dynamic global attention network (DAGN) and graph convolution network (GCN). DAGN implicitly captures the dynamic influence of passenger flow variation between global node pairs, and GCN can integrate the structural information of the input graph.
- A temporal modeling module consisting mainly of series decomposition blocks and locality-aware sparse attention blocks (LSA). A series decomposition block is employed to better capture the global characteristics of time series. At the same time, the LSA block can extract multiple local contexts and reduce the computational complexity for long sequence modeling.
- Both simulation and real-world datasets are used in the experiments. For simulation data, the simulation scenario is the real-world 3D architecture model of Chengdu Metro Line 10 Shuangliu International Airport Terminal 1 station. The passenger flow data are acquired based on AnyLogic pedestrian simulation. For large-scale real-world data, the Automatic Fare Collection (AFC) data in Hangzhou, China, are acquired. The experimental results demonstrate that our proposed STGNN outperforms all those ten baselines.
2. Related Works
2.1. Attention-Based Models
2.2. Spatial-Temporal Graph Neural Networks
3. Methodology
3.1. Problem Formulation
3.2. Overview
3.3. Spatial Modeling
3.3.1. Dynamic Global Attention Network
3.3.2. Incorporation of Structural Information
3.4. Temporal Modeling
3.4.1. Temporal Encoder
3.4.2. Temporal Decoder
3.5. Locality-Aware Sparse Attention
3.6. Embedding
3.7. Loss Function
4. Experiments
4.1. Data Preparation
4.1.1. AnyLogic Simulation in Metro Station
4.1.2. Hangzhou Metro System
4.2. Setting
4.3. Baseline Methods
4.4. Performance Evaluation
4.4.1. Comparison with Baseline Methods
4.4.2. Comparison with Ground Truth
4.5. Effect of Different Network Configurations
4.6. Ablation Experiments
4.7. Computation Cost Study
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Datasets | Nodes | Time Span | Daily Range |
---|---|---|---|
Ours | 13 | 1 January–21 January | 6:00–23:00 |
Hangzhou | 81 | 1 January 2019–26 January 2019 | 6:00–23:30 |
Models | Prediction Step = 10 | Prediction Step = 20 | Prediction Step = 30 | Prediction Step = 60 | ||||
---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |
LSTM | 6.40 | 3.68 | 6.65 | 3.81 | 7.43 | 4.10 | 9.62 | 5.39 |
Reformer | 6.80 | 3.25 | 7.38 | 3.43 | 8.68 | 3.98 | 9.27 | 4.56 |
Autoformer | 6.45 | 2.85 | 6.64 | 2.98 | 7.55 | 3.58 | 9.60 | 4.77 |
TimesNet | 5.41 | 2.38 | 6.02 | 2.61 | 6.46 | 2.98 | 7.41 | 3.39 |
GCN | 9.33 | 5.04 | 10.11 | 6.02 | 10.59 | 6.18 | 11.17 | 7.55 |
T-GCN | 5.85 | 2.76 | 6.54 | 3.14 | 7.30 | 3.35 | 8.72 | 4.22 |
STG-NCDE | 7.39 | 3.94 | 8.30 | 3.89 | 9.93 | 4.66 | 10.81 | 5.89 |
ASTGCN | 5.14 | 2.19 | 5.81 | 2.61 | 6.92 | 2.69 | 7.71 | 3.42 |
ASTGNN | 4.89 | 2.01 | 5.55 | 2.46 | 6.71 | 2.47 | 7.58 | 3.29 |
STGNN (ours) | 4.58 | 1.91 | 5.07 | 2.23 | 5.67 | 2.39 | 7.17 | 3.08 |
Models | Prediction Step = 10 | Prediction Step = 20 | ||||||
---|---|---|---|---|---|---|---|---|
Inflow | Outflow | Inflow | Outflow | |||||
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |
LSTM | 29.13 | 19.15 | 38.93 | 23.74 | 32.92 | 21.29 | 41.42 | 25.17 |
Reformer | 22.47 | 13.27 | 30.83 | 18.01 | 24.43 | 14.01 | 31.29 | 18.47 |
Autoformer | 24.59 | 15.41 | 32.65 | 20.68 | 32.44 | 19.95 | 39.44 | 24.30 |
TimesNet | 23.94 | 14.03 | 32.10 | 17.95 | 25.36 | 14.91 | 33.81 | 18.91 |
GCN | 67.82 | 38.51 | 74.44 | 41.10 | 68.60 | 39.80 | 75.22 | 42.91 |
T-GCN | 27.96 | 17.08 | 35.94 | 22.79 | 30.53 | 18.45 | 38.62 | 24.04 |
STG-NCDE | 24.20 | 13.57 | 31.61 | 18.35 | 30.93 | 16.86 | 42.21 | 23.37 |
ASTGCN | 23.01 | 14.36 | 32.99 | 19.86 | 25.03 | 14.92 | 34.33 | 21.88 |
ASTGNN | 20.61 | 12.30 | 28.63 | 17.12 | 22.36 | 13.44 | 29.54 | 17.86 |
STGNN (ours) | 18.90 | 11.58 | 27.02 | 16.90 | 20.10 | 12.01 | 27.51 | 17.05 |
Models | Prediction Step = 30 | Prediction Step = 60 | ||||||
---|---|---|---|---|---|---|---|---|
Inflow | Outflow | Inflow | Outflow | |||||
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |
LSTM | 34.41 | 22.38 | 43.47 | 26.94 | 37.20 | 24.25 | 46.42 | 29.39 |
Reformer | 25.41 | 14.40 | 31.83 | 18.83 | 27.26 | 15.86 | 34.42 | 20.55 |
Autoformer | 38.75 | 22.79 | 41.56 | 25.59 | 54.38 | 29.14 | 57.25 | 32.14 |
TimesNet | 26.47 | 15.18 | 34.95 | 19.34 | 28.62 | 16.73 | 36.33 | 20.66 |
GCN | 69.20 | 40.50 | 76.46 | 43.34 | 73.83 | 44.84 | 80.11 | 46.76 |
T-GCN | 32.96 | 20.08 | 40.79 | 25.99 | 35.33 | 23.45 | 44.32 | 28.34 |
STG-NCDE | 38.95 | 20.45 | 51.39 | 27.40 | 41.81 | 22.03 | 54.53 | 30.10 |
ASTGCN | 30.34 | 17.99 | 36.80 | 22.10 | 32.87 | 19.96 | 39.33 | 23.28 |
ASTGNN | 23.97 | 13.92 | 30.43 | 18.32 | 25.59 | 14.95 | 32.92 | 19.96 |
STGNN (ours) | 21.44 | 12.65 | 27.81 | 17.12 | 23.40 | 13.78 | 28.93 | 18.35 |
Models | Computation Time for Training Epoch (s) | |
---|---|---|
Prediction Step = 30 | Prediction Step = 60 | |
STG-NODE | 119 | 209 |
ASTGCN | 11.49 | 27.81 |
ASTGNN | 18.33 | 33.31 |
STGNN | 8.59 | 13.28 |
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Chang, Y.; Zong, M.; Dang, Y.; Wang, K. Multi-Step Passenger Flow Prediction for Urban Metro System Based on Spatial-Temporal Graph Neural Network. Appl. Sci. 2024, 14, 8121. https://doi.org/10.3390/app14188121
Chang Y, Zong M, Dang Y, Wang K. Multi-Step Passenger Flow Prediction for Urban Metro System Based on Spatial-Temporal Graph Neural Network. Applied Sciences. 2024; 14(18):8121. https://doi.org/10.3390/app14188121
Chicago/Turabian StyleChang, Yuchen, Mengya Zong, Yutian Dang, and Kaiping Wang. 2024. "Multi-Step Passenger Flow Prediction for Urban Metro System Based on Spatial-Temporal Graph Neural Network" Applied Sciences 14, no. 18: 8121. https://doi.org/10.3390/app14188121