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Smart city article details

Title	Deep-Learning-Based Risk Prediction With Urban Sensing Data For Consumer Driving Safety
ID_Doc	18105
Authors	Lin K.-Y.; Liu P.-Y.; Hu C.-L.; Huang S.-Z.; Chen Y.-H.; Hui L.
Year	2024
Published	GCCE 2024 - 2024 IEEE 13th Global Conference on Consumer Electronics
DOI	http://dx.doi.org/10.1109/GCCE62371.2024.10760458
Abstract	With the provision of IoT-driven and user-provided sensing data sources in smart cities, we take advantage of deep learning techniques to analyze the spatio-temporal traffic data and predict traffic risks for driving safety. Our study continues to collect the traffic data from multiple sensing sources, and meanwhile adopts both CNN and LSTM to interpret the data collection in spatial and temporal dimensions. Thus, a novel traffic risk prediction scheme based on CNN and LSTM, named TRP-CL, is proposed to generate a traffic warning map of risks and hazard situations on a grid-scaled city map. Not only a theoretic formation but also an experimental implementation of the TRP-CL scheme are made to show the practical feasibility. © 2024 IEEE.
Author Keywords	crowdsensing; intelligent transportation system (ITS); IoT; machine learning (ML); risk prediction; Traffic data

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