Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines.Comment: 8 pages, 9 figures, AAAI-202

Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks

Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weather prediction, most of them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On the one hand, the accurate prediction of one task can help improve another task's performance. On the other hand, geospatially distributed air quality and weather monitoring stations provide additional hints for city-wide spatiotemporal dependency modeling. Inspired by the above two insights, in this paper, we propose the Multi-adversarial spatiotemporal recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather predictions. Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations. Then, we develop a multi-adversarial graph learning framework to against observation noise propagation introduced by spatiotemporal modeling. Moreover, we present an adaptive training strategy by formulating multi-adversarial learning as a multi-task learning problem. Finally, extensive experiments on two real-world datasets show that MasterGNN achieves the best performance compared with seven baselines on both air quality and weather prediction tasks.Comment: 9 pages, 6 figure

Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network

Human mobility prediction is a fundamental task essential for various applications, including urban planning, location-based services and intelligent transportation systems. Existing methods often ignore activity information crucial for reasoning human preferences and routines, or adopt a simplified representation of the dependencies between time, activities and locations. To address these issues, we present Hierarchical Graph Attention Recurrent Network (HGARN) for human mobility prediction. Specifically, we construct a hierarchical graph based on all users' history mobility records and employ a Hierarchical Graph Attention Module to capture complex time-activity-location dependencies. This way, HGARN can learn representations with rich human travel semantics to model user preferences at the global level. We also propose a model-agnostic history-enhanced confidence (MAHEC) label to focus our model on each user's individual-level preferences. Finally, we introduce a Temporal Module, which employs recurrent structures to jointly predict users' next activities (as an auxiliary task) and their associated locations. By leveraging the predicted future user activity features through a hierarchical and residual design, the accuracy of the location predictions can be further enhanced. For model evaluation, we test the performances of our HGARN against existing SOTAs in both the recurring and explorative settings. The recurring setting focuses on assessing models' capabilities to capture users' individual-level preferences, while the results in the explorative setting tend to reflect the power of different models to learn users' global-level preferences. Overall, our model outperforms other baselines significantly in all settings based on two real-world human mobility data benchmarks. Source codes of HGARN are available at https://github.com/YihongT/HGARN.Comment: 11 page

Towards interactive betweenness centrality estimation for transportation network using capsule network

Includes bibliographical references.2022 Fall.The node importance of a graph needs to be estimated for many graph-based applications. One of the most popular metrics for measuring node importance is betweenness centrality, which measures the amount of influence a node has over the flow of information in a graph. However, the computation complexity of calculating betweenness centrality is extremely high with large- scale graphs. This is especially true when analyzing the road networks of states with millions of nodes and edges, making it infeasible to calculate their betweenness centrality (BC) in real- time using traditional iterative methods. The application of a machine learning model to predict the importance of nodes provides opportunities to address this issue. Graph Neural Networks (GNNs), which have been gaining popularity in recent years, are particularly well-suited for graph analysis. In this study, we propose a deep learning architecture RoadCaps to estimate the BC by merging Capsule Neural Networks with Graph Convolutional Networks (GCN), a convolution operation based GNN. We target the effective aggregation of features from neighbor nodes to approximate the correct BC of a node. We leverage patterns capturing the strength of the capsule network to effectively estimate the node level BC from the high-level information generated by the GCN block. We further compare the model accuracy and effectiveness of RoadCaps with the other two GCN-based models. We also analyze the efficiency and effectiveness of RoadCaps for different aspects like scalability and robustness. We perform one empirical benchmark with the road network for the entire state of California. The overall analysis shows that our proposed network can provide more accurate road importance estimation, which is helpful for rapid response planning such as evacuation during wildfires and flooding

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. Approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: Forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and, finally, discuss mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting both the foundations, practical applications, and opportunities of graph neural networks for time series analysis.Comment: 27 pages, 6 figures, 5 table