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Title	Filling Missing Values In Spatial-Temporal Data Collected From Traffic Sensors
ID_Doc	26484
Authors	Oliveira F.; Rocha A.P.
Year	2020
Published	2020 IEEE International Smart Cities Conference, ISC2 2020
DOI	http://dx.doi.org/10.1109/ISC251055.2020.9239016
Abstract	Intelligent transportation systems (ITS) are critical to any smart city strategy. They are used to optimize the flow of urban traffic which in turn leads to a reduction in time spent traveling. In order for ITS to work properly, sensors that collect real-time traffic flow information from streets and highways are required so the ITS can know the current state of the traffic. However, such sensors are prone to failures and network faults. This poses a serious hindrance when performing data analysis and knowledge extraction on sensor data due to the fact that such data is composed of noisy and missing values. In this work, we benchmark several deep learning based methods for filling missing values in a dataset collected from 2013 to 2015 in the city of Oporto, Portugal. The dataset is composed of readings of 26 sensors that measure traffic information in 5 minute intervals. Around 12% of all values are missing. © 2020 IEEE.
Author Keywords	Intelligent Transportation Systems; Machine Learning; Missing data; Neural networks

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