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Title	Urban Mobility Prediction Based On Lstm And Discrete Position Relationship Model
ID_Doc	60078
Authors	Tao M.; Sun G.; Wang T.
Year	2020
Published	Proceedings - 2020 16th International Conference on Mobility, Sensing and Networking, MSN 2020
DOI	http://dx.doi.org/10.1109/MSN50589.2020.00081
Abstract	In the context of edge and fog computing, urban mobility prediction acting as an important role in urban planning, traffic prediction, and resource reservation has making a great contribution for the construction of smart cities, and has been considered to be a challenging research and industrial topic for many years. Generally, those popular prediction methods abstract trajectories into independent points in the manner of gridding, clustering and others. However, these data processing methods make the position representation vector lose the connection relationship between geographic locations which is very important for the mobility prediction. To address this issue, this paper proposes a discrete position relationship model to represent the connection between geographic locations, on the basis, a Long Short Term Memory (LSTM) prediction model is established to predict the next position of the mobile target. Experiments and numerical analyses show that these investigations can take full advantage of the relationship between relative positions and reduce the prediction relative error. © 2020 IEEE.
Author Keywords	Discrete position relationship model; LSTM; Position prediction; Urban mobility

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