Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks
Abstract
:1. Introduction
1.1. Related Work
1.2. Overview
2. Materials and Methods
2.1. Study Design and Data Source
2.2. ARIMA Models
2.3. Visual Model
2.3.1. Data Preparation
Algorithm 1: Algorithm that searches for an empty cell near a given position. |
2.3.2. Preliminary Visual Model: Regression CNN
2.3.3. Regression ResNet Model
2.4. Performance Indexes
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Model Details | Results | |||
---|---|---|---|---|
Model | Parameters | MAE | MAPE | SMAPE |
ARIMA | 27,200 | 40.85 | 20.00% | 9.50% |
ARIMA () | 40,800 | 40.45 | 19.81% | 9.35% |
ARIMA () | 54,400 | 40.22 | 19.75% | 9.33% |
ARIMA () | 69,000 | 40.11 | 19.78% | 9.35% |
CNN () | 7169 | 51.65 | 22.79% | 11.82% |
CNN () | 154,753 | 51.52 | 22.62% | 11.56% |
CNN () | 302,337 | 50.98 | 22.45% | 11.21% |
ResNet () | 109,601 | 38.93 | 18.75% | 9.35% |
ResNet () | 262,049 | 37.92 | 18.22% | 8.29% |
ResNet () | 482,465 | 39.65 | 19.08% | 9.16% |
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Vélez-Serrano, D.; Álvaro-Meca, A.; Sebastián-Huerta, F.; Vélez-Serrano, J. Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks. Mathematics 2021, 9, 1068. https://doi.org/10.3390/math9091068
Vélez-Serrano D, Álvaro-Meca A, Sebastián-Huerta F, Vélez-Serrano J. Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks. Mathematics. 2021; 9(9):1068. https://doi.org/10.3390/math9091068
Chicago/Turabian StyleVélez-Serrano, Daniel, Alejandro Álvaro-Meca, Fernando Sebastián-Huerta, and Jose Vélez-Serrano. 2021. "Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks" Mathematics 9, no. 9: 1068. https://doi.org/10.3390/math9091068