Deep Learning for Reducing Redundancy in Madrid’s Traffic Sensor Network

Redundancy reduction plays a critical role in optimizing sensor network performance. This research proposes a deep-learning approach to identify and eliminate redundant sensors in a traffic network. This strategy aims to create a more cost-effective, efficient and reliable traffic monitoring system, ultimately leading to improvements in the transportation infrastructure. Leveraging traffic data from the Madrid Open Data Portal (focusing on ’District 19’), we employed sensor correlation (cosine) and similarity analysis (VGG16-based model) to identify significant correlations among sensors. This allows for accurate prediction (using Long Short-Term Memory(LSTM)-based models) of values from highly correlated sensors, leading to a potential reduction in District 19’s sensor nodes by 43% (from 32 to 18) and connectivity edges by 82% (from 106 to 19). Notably, the predictive accuracy for ’highly similar’ sensors achieved an average R-squared score of 0.82, validating the reliability of LSTM model predictions. These initial results encourage a larger analysis of the methodology to better prove the potential of our deep learning approach in optimizing and streamlining smart city infrastructure. This promising approach can be extended to analyze districts with higher sensor density and be adapted for application in other cities. We aim to utilize deep learning algorithms to optimize future sensor deployment planning.