Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
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DOI：
10.1609/aaai.v32i1.11836
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
Huaxiu Yao;Fei Wu;Jintao Ke;Xianfeng Tang;Yitian Jia;Siyu Lu;Pinghua Gong;Jieping Ye;Z. Li-
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文献类型：
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作者：
Huaxiu Yao;Fei Wu;Jintao Ke;Xianfeng Tang;Yitian Jia;Siyu Lu;Pinghua Gong;Jieping Ye;Z. Li-
出租车需求预测是实现智慧城市智能交通系统的重要组成部分。一个准确的预测模型可以帮助城市预先分配资源以满足出行需求，减少街道上浪费能源和加剧交通拥堵的空出租车。随着Uber、滴滴出行等打车服务（在中国）的日益普及，我们能够持续收集大规模的打车需求数据。如何利用这些大数据来改进需求预测是一个有趣而关键的现实问题。传统的需求预测方法大多依赖于时间序列预测技术，无法对复杂的非线性时空关系进行建模。深度学习的最新进展表明，通过从大规模数据中学习复杂特征和相关性，在图像分类等传统挑战性任务上表现优异。这一突破激发了研究人员探索交通预测问题的深度学习技术。然而，现有的交通预测方法仅单独考虑空间关系（如使用CNN）或时间关系（如使用LSTM）。我们提出了一个深度多视图时空网络（DMVST-Net）框架来模拟空间和时间关系。具体来说，我们提出的模型包括三个视图：时间视图（通过LSTM建模未来需求值与近时间点之间的相关性）、空间视图（通过局部CNN建模局部空间相关性）和语义视图（建模具有相似时间模式的区域之间的相关性）。对大规模真实出租车需求数据的实验表明，我们的方法比最先进的方法更有效。
Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing popularity of taxi requesting services such as Uber and Didi Chuxing (in China), we are able to collect large-scale taxi demand data continuously. How to utilize such big data to improve the demand prediction is an interesting and critical real-world problem. Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. Recent advances in deep learning have shown superior performance on traditionally challenging tasks such as image classification by learning the complex features and correlations from large-scale data. This breakthrough has inspired researchers to explore deep learning techniques on traffic prediction problems. However, existing methods on traffic prediction have only considered spatial relation (e.g., using CNN) or temporal relation (e.g., using LSTM) independently. We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations. Specifically, our proposed model consists of three views: temporal view (modeling correlations between future demand values with near time points via LSTM), spatial view (modeling local spatial correlation via local CNN), and semantic view (modeling correlations among regions sharing similar temporal patterns). Experiments on large-scale real taxi demand data demonstrate effectiveness of our approach over state-of-the-art methods.