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Traffic Flow Prediction Using Deep Learning Techniques in Urban Road Networks

Yilin Han

Yilin Han

Information Science and Technology School, Xian North West UniversityXi'an, China
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Han, Yilin
  
Mar 17, 2025
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Published Online: Mar 17, 2025
Received: Oct 15, 2024
Accepted: Feb 12, 2025
DOI: https://doi.org/10.2478/amns-2025-0832
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© 2025 Yilin Han, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Framework for Traffic Flow Prediction Using Deep Learning Techniques.
Framework for Traffic Flow Prediction Using Deep Learning Techniques.

Figure 2.

Long-Term Prediction Accuracy for Different Models Across Prediction Horizons
Long-Term Prediction Accuracy for Different Models Across Prediction Horizons

Figure 3.

Error Trend Analysis for Long-Term Prediction
Error Trend Analysis for Long-Term Prediction

Scalability Analysis Results on METR-LA Dataset

Number of Sensors 50 100 200
MAE (Proposed, mph) 2.31 2.43 2.51
RMSE (Proposed, mph) 4.52 4.67 4.80
MAE (ST-GCN, mph) 2.45 2.60 2.78
RMSE (ST-GCN, mph) 4.76 4.98 5.24
MAE (LSTM, mph) 2.68 2.83 3.01
RMSE (LSTM, mph) 5.05 5.32 5.60

Computational Efficiency Analysis

Model Training Time (s/epoch) Inference Time (ms/batch) GPU Memory (GB)
Proposed Model 1.8 42 6.2
ST-GCN 2.6 53 6.8
T-GCN 2.9 56 7.1
LSTM 3.1 59 7.2

Robustness to Missing Data

Missing Data Rate 10% 20% 30%
MAE (Proposed, mph) 2.45 2.62 2.84
RMSE (Proposed, mph) 4.69 4.92 5.18
MAE (T-GCN, mph) 2.61 2.82 3.12
RMSE (T-GCN, mph) 4.88 5.16 5.54
MAE (LSTM, mph) 2.85 3.12 3.45
RMSE (LSTM, mph) 5.15 5.46 5.84
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