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1. Deep multi-view graph-based network for citywide ride-hailing demand prediction.

Author

Jin, Guangyin, Xi, Zhexu, Sha, Hengyu, Feng, Yanghe, and Huang, Jincai

Subjects
*DEMAND forecasting, *RECURRENT neural networks, *CONVOLUTIONAL neural networks, *INTELLIGENT transportation systems, *ARTIFICIAL neural networks, *DEEP learning
Abstract

Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction. Predictable ride-hailing demand can facilitate more reasonable vehicle scheduling and online car-hailing platform dispatch. Conventional deep learning methods with no external structured data can be accomplished via hybrid models of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) by meshing plentiful pixel-level labeled data, but data sparsity of high grid granularity in spatial perspective and limited learning capabilities of long-term dependencies in temporal perspective are still two striking bottlenecks. To address these problems, we propose a novel virtual graph modeling approach to focus on significant demand regions and a novel Deep Multi-View Spatio-temporal Virtual Graph Neural Network (DMVST-VGNN) to strengthen the learning capabilities of spatial dynamics and long-term temporal dependencies. Specifically, DMVST-VGNN integrates structures of 1D CNN, Multi-Graph Attention Neural Network and Transformer Network, which correspond to short-term temporal dynamics view, spatial dynamics view and long-term temporal dynamics view respectively. In this paper, multiple experiments are conducted on two large-scale New York City datasets in higher granularity prediction scenes. And the experimental results demonstrate the effectiveness of DMVST-VGNN framework in ride-hailing demand prediction, no matter in spatial scale or the temporal scale. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning.

Author

Peng, Hao, Du, Bowen, Liu, Mingsheng, Liu, Mingzhe, Ji, Shumei, Wang, Senzhang, Zhang, Xu, and He, Lifang

Subjects
*TRAFFIC flow, *DEEP learning, *REINFORCEMENT learning, *RECURRENT neural networks, *FLOWGRAPHS, *STATISTICS
Abstract

Exploiting deep learning techniques for traffic flow prediction has become increasingly widespread. Most existing studies combine CNN or GCN with recurrent neural network to extract the spatio-temporal features in traffic networks. The traffic networks can be naturally modeled as graphs which are effective to capture the topology and spatial correlations among road links. The issue is that the traffic network is dynamic due to the continuous changing of the traffic environment. Compared with the static graph, the dynamic graph can better reflect the spatio-temporal features of the traffic network. However, in practical applications, due to the limited accuracy and timeliness of data, it is hard to generate graph structures through frequent statistical data. Therefore, it is necessary to design a method to overcome data defects in traffic flow prediction. In this paper, we propose a long-term traffic flow prediction method based on dynamic graphs. The traffic network is modeled by dynamic traffic flow probability graphs, and graph convolution is performed on the dynamic graphs to learn spatial features, which are then combined with LSTM units to learn temporal features. In particular, we further propose to use graph convolutional policy network based on reinforcement learning to generate dynamic graphs when the dynamic graphs are incomplete due to the data sparsity i sue. By testing our method on city-bike data in New York City, it demonstrates that our model can achieve stable and effective long-term predictions of traffic flow, and can reduce the impact of data defects on prediction results. [ABSTRACT FROM AUTHOR]

Published
2021
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