Smart Societies, Infrastructure, Technologies and Applications. First International Conference, SCITA 2017, Jeddah, Saudi Arabia, November 27–29, 2017, Proceedings

Research Article

Disaster Management in Smart Cities by Forecasting Traffic Plan Using Deep Learning and GPUs

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  • @INPROCEEDINGS{10.1007/978-3-319-94180-6_15,
        author={Muhammad Aqib and Rashid Mehmood and Aiiad Albeshri and Ahmed Alzahrani},
        title={Disaster Management in Smart Cities by Forecasting Traffic Plan Using Deep Learning and GPUs},
        proceedings={Smart Societies, Infrastructure, Technologies and Applications. First International Conference, SCITA 2017, Jeddah, Saudi Arabia, November 27--29, 2017, Proceedings},
        proceedings_a={SCITA},
        year={2018},
        month={7},
        keywords={Smart cities Disaster management Deep learning GPUs Convolution neural networks},
        doi={10.1007/978-3-319-94180-6_15}
    }
    
  • Muhammad Aqib
    Rashid Mehmood
    Aiiad Albeshri
    Ahmed Alzahrani
    Year: 2018
    Disaster Management in Smart Cities by Forecasting Traffic Plan Using Deep Learning and GPUs
    SCITA
    Springer
    DOI: 10.1007/978-3-319-94180-6_15
Muhammad Aqib1,*, Rashid Mehmood1,*, Aiiad Albeshri1,*, Ahmed Alzahrani1,*
  • 1: King Abdulaziz University
*Contact email: aqib.qazi@gmail.com, RMehmood@kau.edu.sa, aaalbeshri@kau.edu.sa, asalzahrani@kau.edu.sa

Abstract

The importance of disaster management is evident by the increasing number of natural and manmade disasters such as Irma and Manchester attacks. The estimated cost of the recent Irma hurricane is believed to be more than 80 billion USD; more importantly, more than 40 lives have been lost and thousands were misplaced. Disaster management plays a key role in reducing the human and economic losses. In our earlier work, we have developed a disaster management system that uses VANET, cloud computing, and simulations to devise city evacuation strategies. In this paper, we extend our earlier work by using deep learning to predict urban traffic behavior. Moreover, we use GPUs to deal with compute intensive nature of deep learning algorithms. To the best of our knowledge, we are the first to apply deep learning approach in disaster management. We use real-world open road traffic within a city available through the UK Department for Transport. Our results demonstrate the effectiveness of deep learning approach in disaster management and correct prediction of traffic behavior in emergency situations.