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 traf...Show MoreMetadata
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.
Date of Conference: 29 October 2024 - 01 November 2024
Date Added to IEEE Xplore: 28 November 2024
ISBN Information: