本文主要的目的在于预测特定时间段特定区域的人流入量和人流出量。（如 bike drop off and pick up）。然后思考了可能影响入和出流量的因素。比如，对于不同交通工具的流量变动是相关影响的，如自行车和出租车。同时，单个区域在单个时间点流量可视为多个隐式基的一个组合效应。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network | |||||||||||
| 文献作者 | Junchen Ye; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| Demand Prediction, Spatio-Temporal Analysis, Sharing Economy, Deep Neural Network | |||||||||||
| 摘要描述 | |||||||||||
| Taxi and sharing bike bring great convenience to urban transportation. A lot of efforts have been made to improve the efficiency of taxi service or bike sharing system by predicting the next-period pick-up or drop-off demand. Different from the existing research, this paper is motivated by the following two facts: 1) From a micro view, an observed spatial demand at any time slot could be decomposed as a combination of many hidden spatial demand bases; 2) From a macro view, the multiple transportation demands are strongly correlated with each other, both spatially and temporally. Definitely, the above two views have great potential to revolutionize the existing taxi or bike demand prediction methods. Along this line, this paper provides a novel Co-prediction method based on Spatio-Temporal neural Network, namely, CoST-Net. In particular, a deep convolutional neural network is constructed to decompose a spatial demand into a combination of hidden spatial demand bases. The combination weight vector is used as a representation of the decomposed spatial demand. Then, a heterogeneous Long Short-Term Memory (LSTM) is proposed to integrate the states of multiple transportation demands, and also model the dynamics of them mixedly. Last, the environmental features such as humidity and temperature are incorporated with the achieved overall hidden states to predict the multiple demands simultaneously. Experiments have been conducted on real-world taxi and sharing bike demand data, results demonstrate the superiority of the proposed method over both classical and the state-of-the-art transportation demand prediction methods. | |||||||||||
