论文标题
朝着时空跨平台图嵌入城市交通流量预测的融合
Towards Spatio-Temporal Cross-Platform Graph Embedding Fusion for Urban Traffic Flow Prediction
论文作者
论文摘要
在本文中，我们提出了STC-GEF，这是一种新型的时空跨平台图嵌入城市交通流量预测的融合方法。我们已经基于图形卷积网络（GCN）设计了一个空间嵌入模块，以提取流量流数据中的复杂空间特征。此外，为了从各个时间间隔捕获交通流数据之间的时间依赖性，我们设计了一个基于复发神经网络的时间嵌入模块。基于观察结果，可以将不同的运输平台跳闸数据（例如出租车，Uber和Lyft）相关联，我们设计了一种有效的融合机制，该机制结合了来自不同运输平台的Trip数据，并进一步将它们用于交叉平台交通流量流量预测（例如，集成出租车和乘车交通流量流量流量流量流量流量，预测）。我们基于纽约市（NYC）的黄色出租车和乘车共享（LYFT）的现实旅行数据进行了广泛的现实实验研究，并验证了STC-GEF在融合不同的运输平台数据中的准确性和有效性，并预测了交通流。
In this paper, we have proposed STC-GEF, a novel Spatio-Temporal Cross-platform Graph Embedding Fusion approach for the urban traffic flow prediction. We have designed a spatial embedding module based on graph convolutional networks (GCN) to extract the complex spatial features within traffic flow data. Furthermore, to capture the temporal dependencies between the traffic flow data from various time intervals, we have designed a temporal embedding module based on recurrent neural networks. Based on the observations that different transportation platforms trip data (e.g., taxis, Uber, and Lyft) can be correlated, we have designed an effective fusion mechanism that combines the trip data from different transportation platforms and further uses them for cross-platform traffic flow prediction (e.g., integrating taxis and ride-sharing platforms for taxi traffic flow prediction). We have conducted extensive real-world experimental studies based on real-world trip data of yellow taxis and ride-sharing (Lyft) from the New York City (NYC), and validated the accuracy and effectiveness of STC-GEF in fusing different transportation platform data and predicting traffic flows.
