Reseach Article
Challenges of Integrating Spatiotemporal Data with AI/ML Models for Road Traffic Congestion Prediction
| Journal of Advanced Artificial Intelligence |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 4 |
| Year of Publication: 2025 |
| Authors: Mohammad Alam |
10.5120/jaai202417
|
Mohammad Alam . Challenges of Integrating Spatiotemporal Data with AI/ML Models for Road Traffic Congestion Prediction. Journal of Advanced Artificial Intelligence. 1, 4 ( Jan 2025), 8-14. DOI=10.5120/jaai202417
Abstract
Integrating spatiotemporal data with artificial intelligence (AI) and machine learning (ML) models offers significant potential for enhancing road traffic congestion predictions. However, this integration poses several challenges. This paper examines these challenges, focusing on data quality, model complexity, and real-time processing demands. Spatiotemporal data, which encompasses both spatial and temporal dimensions, is essential for accurate traffic forecasting but often suffers from incompleteness, noise, and inconsistency. Additionally, the high computational requirements of advanced AI/ML models and the need for real-time data processing further complicate the integration process. This paper reviews recent advancements to address these challenges, including improved data collection techniques, novel AI/ML approaches, and solutions for enhancing data privacy and security. By highlighting these challenges and exploring potential solutions, this paper contributes to the ongoing development of more effective traffic congestion prediction systems.
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