Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at each time step and then connected each node with itself between adjacent time steps to create a spatio-temporal graph. However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks. Additionally, those models overlooked the dynamic spatio-temporal correlations among nodes by using the same adjacency matrix across different time steps. To address these limitations, we propose a novel approach called Spatio-Temporal Joint Graph Convolutional Networks (STJGCN) for accurate traffic forecasting on road networks over multiple future time steps. Specifically, our method encompasses the construction of both pre-defined and adaptive spatio-temporal joint graphs (STJGs) between any two time steps, which represent comprehensive and dynamic spatio-temporal correlations. We further introduce dilated causal spatio-temporal joint graph convolution layers on the STJG to capture spatio-temporal dependencies from distinct perspectives with multiple ranges. To aggregate information from different ranges, we propose a multi-range attention mechanism. Finally, we evaluate our approach on five public traffic datasets and experimental results demonstrate that STJGCN is not only computationally efficient but also outperforms 11 state-of-the-art baseline methods.

Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires inductive learning. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://github.com/zhengchuanpan/INCREASE.

Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. First, we integrate the graphs with spatio-temporal similarity as mobility patterns through a mobility graph fusion module. Then, in the mobility pattern joint learning module, we design the multi-level cross-attention mechanism to learn the comprehensive embeddings from multiple mobility patterns based on intra-pattern and inter-pattern messages. Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 12.35% improvement. https://github.com/wushangbin/MGFN

Accurate spatio-temporal traffic forecasting serves as the basis of dynamic strategy and applications for intelligent transportation systems, which is of great practical significance for improving traffic safety and mitigating road congestion. Recently, deep learning methods such as convolutional neural networks (CNN) have been applied to traffic flow forecasting, which exhibits better performance than conventional methods. However, these CNN-based methods typically learn traffic as images to model spatial correlation, which is only applicable to Euclidean grid map data rather than non-Euclidean multi-sensor data. To address this problem, we propose a graph-based temporal attention framework GTA, which considers both spatial and temporal correlation, to forecast traffic flow based on data collected from multiple sensors. More specifically, GTA can better capture spatial dependencies leveraging graph embedding techniques on sensor networks because it preserves more details in the algorithms. We also introduce an attention mechanism to adaptively identify the relations among temporal submodules. Spatio-temporal dependencies are more effectively and comprehensively integrated due to the full use of the topological properties of transportation networks. We evaluate GTA with a large-scale traffic dataset from England and enhance it with topology information. The experimental results show that our approach outperforms several state-of-the-art baselines.

Nowadays, a large number of sensors are equipped on mobile or stationary platforms, which continuously generate geo-tagged and time-stamped readings (i.e., geo-sensory data) that contain rich information about the surrounding environment. These data have irregular space and time coordinates. To represent geo-sensory data, there have been extensive research efforts using time sequences, grid-like images, and graph signals. However, there still lacks a proper representation that can describe both the mobile and stationary geo-sensory data without the information-losing discretization in spatial and temporal dimensions. In this paper, we propose to represent massive geo-sensory data asspatio-temporal point clouds(STPC), and presentSTPC-Net, a novel deep neural network for processing STPC. STPC leverages the original irregular space-time coordinates, and STPC-Net captures intra-sensor and inter-sensor correlations from STPC. In this way, STPC-Net learns the key information of STPC, and overcomes challenges in data irregularity. Experiments using real-world datasets show that STPC-Net achieves state-of-the-art performance in different tasks on both mobile and stationary geo-sensory data. The source code is available athttps://github.com/zhengchuanpan/STPC-Net.

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.

Deep learning techniques have been widely applied to traffic flow prediction, considering underlying routine patterns, and multiple context factors (e.g., time and weather). However, the complex spatio-temporal dependencies between inherent traffic patterns and multiple disturbances have not been fully addressed. In this paper, we propose a two-phase end-to-end deep learning framework, namely DeepSTD to uncover the spatio-temporal disturbances (STD) to predict the citywide traffic flow. In the STD Modeling phase, we propose an STD modeling method to model both the different regional disturbances caused by various region functions and the spatio-temporal propagating effects. In the Prediction phase, we eliminate the STD from the historical traffic flow to enhance the leaning of inherent traffic patterns and combine the STD at the prediction time interval to consider the future disturbances. The experimental results on two real-world datasets demonstrate that DeepSTD outperforms the state-of-the-art methods.

Unlicensed taxis are widely considered as major obstacles to city traffic regulation and public safety. Thus, many governments have issued restrictions for car-hailing services and alleged that the use of unlicensed vehicles was illegal. However, it is very challenging that traffic administrative enforcements face limited manpower to prohibit unlicensed taxis, due to costly and time-consuming procedure of on-site evidence collection. In this paper, we propose an effective service to incorporate human mobility mechanism into unlicensed taxis detection from massive city-wide vehicles. We first extract 276 spatio-temporal features, which are grouped into two categories, including daily behaviors and sustainable behaviors to capture the mobility characteristics of unlicensed taxis. Second, we investigate the detection accuracy of three machine learning techniques, viz. support vector machines, decision tree, and logical regression. We illustrate our approach using real-world vehicle license plate recognition dataset in Xiamen, China, which contains 336 million passing records for 6.2 million vehicles filmed by 439 devices in August 2016. Experimental results reveal that LR outperforms SVM and DT in prediction accuracy and F-score measurement, while SVM is capable of identifying the largest number of unlicensed taxis.