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Overcoming Traffic Sensors Malfunctions with Deep Learning

Victoria Catalan Pastor, Elena Congeduti, Aleksander Czechowski, and Frans A. Oliehoek. Overcoming Traffic Sensors Malfunctions with Deep Learning. In Proceedings of the 34th Benelux Conference on Artificial Intelligence (BNAIC) and the 30th Belgian Dutch Conference on Machine Learning (Benelearn), November 2022.

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Abstract

Constant growth of cities and their rapid urbanization contribute significantly to an increase in traffic congestion, leading to highcosts both in terms of time and fuel consumption. Intelligent Transportation Systems (ITSs) play an important role in managing traffic in urban areas by reducing accidents and increasing road capacity. To accomplish these tasks, these systems require live traffic data provided by different sources. The most common one are induction loop sensors, located on roads. When induction loop malfunctions occur, the data streams become unavailable, making crucial ITS services inoperative until maintenance can take place. This research explores a proxy solution to such problem: using predictions of deep learning models, such as Long Short-Term Memory networks (LSTMs) and Temporal-Convolutional Networks (TCNs) as temporary replacement data streams for faulty loops, based on data from neighboring, functioning sensors. This method is presented in a real-world scenario using data from a road segment in The Netherlands. The results show that the deep learning models can effectively predict the data from malfunctioning sensors, thus allowing to overcome the issues due to the missing information. The two models are compared on this task to conclude that even though the TCN running times are shorter, LSTM reaches similar levels of accuracy and provides more robust predictions toward short-term sensor failures.

BibTeX Entry

@inproceedings{CatalanPastor22BNAICBenelearn,
    author = {Catalan Pastor, Victoria and Congeduti, Elena and Czechowski, Aleksander and Oliehoek, Frans A.},
    title =     {Overcoming Traffic Sensors Malfunctions with Deep Learning},
    booktitle = BNAICBenelearn22,
    year =      2022,
    month =     nov,
    keywords =   {refereed},
    abstract =  {Constant growth of cities and their rapid urbanization contribute significantly to an increase in traffic congestion, leading to high
costs both in terms of time and fuel consumption. Intelligent Transportation Systems (ITSs) play an important role in managing traffic in urban areas by reducing accidents and increasing road capacity. To accomplish these tasks, these systems require live traffic data provided by different sources. The most common one are induction loop sensors, located on roads. When induction loop malfunctions occur, the data streams become unavailable, making crucial ITS services inoperative until maintenance can take place. This research explores a proxy solution to such problem: using predictions of deep learning models, such as Long Short-Term Memory networks (LSTMs) and Temporal-Convolutional Networks (TCNs) as temporary replacement data streams for faulty loops, based on data from neighboring, functioning sensors. This method is presented in a real-world scenario using data from a road segment in The Netherlands. The results show that the deep learning models can effectively predict the data from malfunctioning sensors, thus allowing to overcome the issues due to the missing information. The two models are compared on this task to conclude that even though the TCN running times are shorter, LSTM reaches similar levels of accuracy and provides more robust predictions toward short-term sensor failures.
  }
}

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