Version 1
: Received: 24 January 2024 / Approved: 26 January 2024 / Online: 26 January 2024 (12:03:21 CET)
How to cite:
Maniat, M.; Eebrahimzadeh, A. Enhancing Traffic Prediction Accuracy: A Comparative Analysis of Data Quality and Model Evaluation Using Artificial Intelligence. Preprints2024, 2024011917. https://doi.org/10.20944/preprints202401.1917.v1
Maniat, M.; Eebrahimzadeh, A. Enhancing Traffic Prediction Accuracy: A Comparative Analysis of Data Quality and Model Evaluation Using Artificial Intelligence. Preprints 2024, 2024011917. https://doi.org/10.20944/preprints202401.1917.v1
Maniat, M.; Eebrahimzadeh, A. Enhancing Traffic Prediction Accuracy: A Comparative Analysis of Data Quality and Model Evaluation Using Artificial Intelligence. Preprints2024, 2024011917. https://doi.org/10.20944/preprints202401.1917.v1
APA Style
Maniat, M., & Eebrahimzadeh, A. (2024). Enhancing Traffic Prediction Accuracy: A Comparative Analysis of Data Quality and Model Evaluation Using Artificial Intelligence. Preprints. https://doi.org/10.20944/preprints202401.1917.v1
Chicago/Turabian Style
Maniat, M. and Amin Eebrahimzadeh. 2024 "Enhancing Traffic Prediction Accuracy: A Comparative Analysis of Data Quality and Model Evaluation Using Artificial Intelligence" Preprints. https://doi.org/10.20944/preprints202401.1917.v1
Abstract
This study focuses on predicting traffic speed using the simple Multilayer Perceptron (MLP) model, despite the availability of various models for traffic prediction, with artificial intelligence demonstrating superior predictive capabilities. Emphasizing that the quality of the data holds greater significance than the model itself, the research underscores the challenges posed by data containing significant errors and fluctuations. Unlike relying solely on criteria such as Mean Absolute Deviation (MAD) or coefficient of determination (r2), this study advocates for the use of MAD/range as a more robust metric for prediction accuracy, especially when dealing with data exhibiting substantial order.The study sets the speed limit and traffic value at 0.65 and 0.88, respectively. Notably, both criteria yield identical accuracy results of 13% for MAD/R in predicting speed and 14% for traffic. This parity suggests that the artificial intelligence model performs equally well for both variables, highlighting the importance of considering alternative evaluation metrics in the face of data complexities.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
