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Title	Spatial-Temporal Taxi Demand Prediction Using Lstm-Cnn
ID_Doc	52523
Authors	Shu P.; Sun Y.; Zhao Y.; Xu G.
Year	2020
Published	IEEE International Conference on Automation Science and Engineering, 2020-August
DOI	http://dx.doi.org/10.1109/CASE48305.2020.9217007
Abstract	Spatial-temporal taxi demand prediction is vital for efficient planning and scheduling of taxis, which could improve overall service level of public transportation in megacities. However, previous research mainly focuses on predicting the taxi demand within certain areas, and seldom considers the inter-area demands, which is essential for the macro-level taxi scheduling. Therefore, this paper proposes an effective model for spatial-temporal inter-area taxi demand prediction through integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). CNN is adopted to extract the correlation between features and temporal closeness dependence while LSTM for fusing them in time series. The model is verified using the historical data in Haikou (China) and results show it is more accurate and stable than traditional LSTM in inter-area taxi demand prediction. © 2020 IEEE.
Author Keywords	CNN; LSTM; Smart City; Smart Mobility; Taxi Demand Prediction

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