Traffic information can reflect the operating status of a city, and accurate traffic forecasting is critical in intelligent transportation systems (ITS) and urban planning. However, traffic information has complex nonlinearity and dynamic spatial -temporal dependencies due to human mobility, bringing new traffic forecasting challenges. This paper proposed a graph spatial -temporal transformer network for traffic prediction (GSTTN) to cope with the above problems. Specifically, the proposed framework explores spatial characteristics of the acrossroad network of traffic information hidden in human behavior patterns via a multi -view graph convolutional network (GCN). Furthermore, the transformer network with a multi -head attention mechanism is adopted to capture the random disturbance in the time series characteristics of traffic information. As a result, these two components can be used to model spatial relations and temporal trends. Finally, we examine real -world datasets, and the experiments show that the proposed framework outperforms the current state-of-the-art baselines.

Pre-travel recommendation is developed to provide a variety of out-of-town Point-of-Interests (POIs) for users planning to travel away from their hometowns but have not yet decided on their destination. Existing out-of-town recommender systems work on constructing users' latent preferences and inferring travel intentions from their check-in sequences. However, there are still two challenges that hamper the performance of these approaches: i) Users' interactive data (including hometown and out-of-town check-ins) tend to be rare, and while candidate POIs that come from different regions contain various semantic information; ii) The causes for user check-in include not only interest but also conformity, which are easily entangled and overlooked. To fill these gaps, we propose a Knowledge-Driven Disentangled Causal metric learning framework (KDDC) that mitigates interaction data sparsity by enhancing POI semantic representation and considers the distributions of two causes (i.e., conformity and interest) for pre-travel recommendation. Specifically, we pretrain a constructed POI attribute knowledge graph through a segmented interaction method and POI semantic information is aggregated via relational heterogeneity. In addition, we devise a disentangled causal metric learning to model and infer userrelated representations. Extensive experiments on two real-world nationwide datasets display the consistent superiority of our KDDC over state-of-theart baselines.

Accurate traffic forecasting plays a critical role in the construction of intelligent transportation systems. However, due to the across road-network isomorphism in the spatial dimension and the periodic drift in the temporal dimension, existing traffic forecasting methods cannot satisfy the intricate spatial-temporal characteristics well. In this article, a spatial-temporal hypergraph convolutional network for traffic forecasting (ST-HCN) is proposed to tackle the problems mentioned above. Specifically, the proposed framework applies the K-means clustering algorithm and the connection characteristics of the physical road network itself to unify the local correlation and across road-network isomorphism. Then, a dual-channel hypergraph convolution to capture high-order spatial relationships in traffic data is established. Furthermore, the proposed framework utilizes a long short-term memory network with a convolution module (ConvLSTM) to deal with the periodic drift problem. Finally, the experiments in the real world demonstrate that the proposed framework outperforms the state-of-the-art baselines.

Modern enterprises attach much attention to the selection of commercial locations. With the rapid development of urban data and machine learning, we can discover the patterns of human mobility with these data and technology to guide commercial district discovery. In this article, we propose an unsupervised commercial district discovery framework via embedding space clustering on graph convolution networks to solve the problem of commercial district discovery. Specifically, the proposed framework aggregates human mobility features according to geographic similarity by graph convolution networks. Based on the graph convolution network embedding space, we apply hierarchical clustering to mine the latent functional regions hidden in different human patterns. Then, with the kernel density estimation, we can obtain the semantic labels for the clustering results to discover commercial districts. Finally, we analyze the multisource data of the Xiaoshan District and Chengdu City, and experiments verify the effectiveness of our framework.