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Open AccessArticle

Spatial Extension of Road Traffic Sensor Data with Artificial Neural Networks

Dipartimento di Ingegneria, Università del Sannio, piazza Roma 21, 82100 Benevento, Italy
Author to whom correspondence should be addressed.
Sensors 2018, 18(8), 2640;
Received: 30 May 2018 / Revised: 5 July 2018 / Accepted: 9 August 2018 / Published: 12 August 2018
(This article belongs to the Special Issue Sensor Networks for Smart Roads)
This paper proposes a method for estimating traffic flows on some links of a road network knowing the data on other links that are monitored with sensors. In this way, it is possible to obtain more information on traffic conditions without increasing the number of monitored links. The proposed method is based on artificial neural networks (ANNs), wherein the input data are the traffic flows on some monitored road links and the output data are the traffic flows on some unmonitored links. We have implemented and tested several single-layer feed-forward ANNs that differ in the number of neurons and the method of generating datasets for training. The proposed ANNs were trained with a supervised learning approach where input and output example datasets were generated through traffic simulation techniques. The proposed method was tested on a real-scale network and gave very good results if the travel demand patterns were known and used for generating example datasets, and promising results if the demand patterns were not considered in the procedure. Numerical results have underlined that the ANNs with few neurons were more effective than the ones with many neurons in this specific problem. View Full-Text
Keywords: traffic sensors; smart roads; artificial neural networks; ITS traffic sensors; smart roads; artificial neural networks; ITS
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Gallo, M.; De Luca, G. Spatial Extension of Road Traffic Sensor Data with Artificial Neural Networks. Sensors 2018, 18, 2640.

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