@inproceedings{6c593f2dd83e410fbb42fcebdb4989e8,
title = "Spatial-Temporal Taxi Demand Prediction Using LSTM-CNN",
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.",
keywords = "CNN, LSTM, Smart City, Smart Mobility, Taxi Demand Prediction",
author = "Pengfeng Shu and Ying Sun and Yifan Zhao and Gangyan Xu",
note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 71804034, the Research Foundation of STIC under Grant JCYJ20180306171958907, and the CCF-Tencent Open Research Fund. Publisher Copyright: {\textcopyright} 2020 IEEE.; 16th IEEE International Conference on Automation Science and Engineering, CASE 2020 ; Conference date: 20-08-2020 Through 21-08-2020",
year = "2020",
month = aug,
doi = "10.1109/CASE48305.2020.9217007",
language = "English",
series = "IEEE International Conference on Automation Science and Engineering",
publisher = "IEEE Computer Society",
pages = "1226--1230",
booktitle = "2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020",
address = "United States",
}
