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Article

Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach

Institute of Cartography and Geoinformatics, Leibniz University, Appelstrasse 9a, 30167 Hanover, Germany
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ISPRS Int. J. Geo-Inf. 2020, 9(11), 652; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110652
Received: 13 August 2020 / Revised: 13 October 2020 / Accepted: 27 October 2020 / Published: 30 October 2020
(This article belongs to the Special Issue Volunteered Geographic Information and Citizen Science)
Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles’ movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method’s ability for classifying a greater variety of traffic regulator classes. View Full-Text
Keywords: traffic regulators; traffic-signs; trajectories; crowdsensing; crowdsourcing; GPS tracks; speed-profiles; intersections; classification; sequence-to-sequence; conditional generative model traffic regulators; traffic-signs; trajectories; crowdsensing; crowdsourcing; GPS tracks; speed-profiles; intersections; classification; sequence-to-sequence; conditional generative model
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MDPI and ACS Style

Cheng, H.; Zourlidou, S.; Sester, M. Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110652

AMA Style

Cheng H, Zourlidou S, Sester M. Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS International Journal of Geo-Information. 2020; 9(11):652. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110652

Chicago/Turabian Style

Cheng, Hao, Stefania Zourlidou, and Monika Sester. 2020. "Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach" ISPRS International Journal of Geo-Information 9, no. 11: 652. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110652

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