英文摘要 | As one of the core component of Intelligent Transportation Systems, urban traffic flow prediction provides critical technical support for multiple intelligent transportation services.
For instance, accurate prediction results can help urban residents design travel plans, assist traffic management departments in optimizing traffic control strategies, and improve the quality of traffic services. In general, the task of urban traffic flow prediction plays a significant role in modern urban life, and has attracted widespread attention from the fields of academic and industry.
However, due to the complicated spatio-temporal correlations of urban traffic networks, this task is still faced with the following challenges. First, urban traffic flow usually presents multiple types of temporal dependencies. To be specific, traffic flow renders both short-term (about one hour) neighboring and long-term (one day, one week, or longer) periodic temporal features. How to model multiple types of temporal dependencies to improve the performance of urban traffic flow prediction is a key issue that require further research. Second, there exists complicated spatial dependences on urban traffic flow. Since the flow features on traffic nodes are largely affected by the nearby urban functions, both local and non-local spatial correlations could be implied. Third, external factors such as weather conditions, holiday notices, and major events affects urban traffic flow. The data associated with these events are multi-source and heterogeneous. How to fuse these data is a difficult research problem.
To address the above problems, this dissertation proposes a variety of solutions, and the main contributions and innovations are summarized as follows:
- A global spatio-temporal network (GSTNet) for urban traffic flow prediction is proposed. By constructing the multi-resolution temporal module and the global correlated spatial module, the GSTNet can extract multi-type temporal features, as well as the local and non-local spatial features on traffic networks. Specifically, the temporal module is composed by multi-layer stacked tensor causal convolution. The low-layer convolution has a shorter receptive field and extracts short-term neighboring temporal features, while the high-layer convolution has a longer receptive field and captures long-term periodic features. On the other hand, the spatial module consists of localized spectral graph convolution and non-local correlated mechanism. Among them, the localized spectral graph convolution can model the spatial correlations of neighboring traffic nodes, while the non-local correlated mechanism mines the relationships of long-distance traffic nodes. Experiments on three real-world datasets verify the effectiveness of the proposed method.
- A multi-source spatio-temporal network (MSNet) for urban traffic flow prediction is proposed. The core idea of MSNet is to extract and integrate the traffic spatio-temporal features and the characteristics of multi-source heterogeneous external data. Specifically, an dilated attentional graph convolution~(DAGC) is first designed to extract the non-local spatial features. Second, to consider traffic flow feature extraction, the multi-layer stacked DAGC is adopted to capture the local and non-local spatial features, which also combines the short-term neighboring and the long-term periodic temporal features. Then, to consider external data, weather information, holiday notices, and the spatial distribution of points of interest are embedded based on the attributes. Finally, to consider multi-source data fusion, the traffic features and the distribution features of points of interest are first merged on spatial dimension, and then the weather and holiday features are fused on temporal dimension. The effectiveness of the proposed method is verified on three real-world datasets.
- A meta-learning based multi-source spatio-temporal network (MetaMSNet) for urban traffic flow prediction is proposed. The core idea of MetaMSNet is to adopt meta-learning to fuse traffic data with multi-source heterogeneous spatio-temporal external data. To consider the semantic gap between multi-source heterogeneous data, two meta-learning based feature fusion modules are proposed on temporal and spatial dimensions, respectively. Under the framework of meta-learning, the fusion intensity of between external and traffic data is not fixed in advance, but is learned by meta-learner. It not only eliminates the semantic gap between multi-source heterogeneous data, but also reduces the dependence of the fusion operation on the prior knowledge of the data source. To consider the different functions provided by the neighboring and periodic temporal data, as well as the error accumulation in multi-step prediction tasks, the MetaMSNet is designed as an encoder-decoder structure. The encoder extracts the short-term neighboring trend of traffic flow, and the decoder adoptes the periodic historical data as an approximate estimate, and adjusts the estimation through combining the short-term neighboring features. The effectiveness of the proposed method is verified on multiple real-world traffic datasets containing multi-source heterogeneous data.
- A novel multi-source spatio-temporal network via automatic neural architecture search (AutoMSNet) for urban traffic flow prediction is proposed. The core idea is to extract local and non-local spatial features based on the technique of automatic network structure search. Specifically, to consider the different strengthes of non-local spatial correlations in different traffic scenarios, the technique of neural architecture search is adopted to automatically select and combine graph convolution operators with different receptive fields to construct the optimal model structure. In addition, the proposal AutoMSNet inherits two significant features from the previous MetaMSNet: the meta-learning based multi-source feature fusion strategy, and the overall encoder-decoder structure of the model. Therefore, it can simultaneously solve the issue of local and non-local spatial correlations, multi-types of temporal features, multi-source heterogeneous data fusion, and error accumulation in multi-step prediction tasks. Extensive experiments under different real traffic scenarios such as Beijing subway, bus, and taxi have verified the effectiveness of the proposed method.
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