Spatiotemporal and Semantic Zero-inflated Urban Anomaly Prediction

• URL: http://arxiv.org/abs/2304.01569v1
• Date: Tue, 4 Apr 2023 06:48:07 GMT
• Title: Spatiotemporal and Semantic Zero-inflated Urban Anomaly Prediction
• Authors: Yao Lu, Pengyuan Zhou, Yong Liao and Haiyong Xie
• Abstract summary: We propose STS to jointly capture the intra- and inter-dependencies between patterns and influential factors in three dimensions. We use a multi-task prediction module with a customized loss function to solve the zero-inflated issue. Experiments on two application scenarios with four real-world datasets demonstrate the superiority of STS.
• Score: 8.340857178859768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban anomaly predictions, such as traffic accident prediction and crime prediction, are of vital importance to smart city security and maintenance. Existing methods typically use deep learning to capture the intra-dependencies in spatial and temporal dimensions. However, numerous key challenges remain unsolved, for instance, sparse zero-inflated data due to urban anomalies occurring with low frequency (which can lead to poor performance on real-world datasets), and both intra- and inter-dependencies of abnormal patterns across spatial, temporal, and semantic dimensions. Moreover, a unified approach to predict multiple kinds of anomaly is left to explore. In this paper, we propose STS to jointly capture the intra- and inter-dependencies between the patterns and the influential factors in three dimensions. Further, we use a multi-task prediction module with a customized loss function to solve the zero-inflated issue. To verify the effectiveness of the model, we apply it to two urban anomaly prediction tasks, crime prediction and traffic accident risk prediction, respectively. Experiments on two application scenarios with four real-world datasets demonstrate the superiority of STS, which outperforms state-of-the-art methods in the mean absolute error and the root mean square error by 37.88% and 18.10% on zero-inflated datasets, and, 60.32% and 37.28% on non-zero datasets, respectively.

Related papers

• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Spatial-temporal Forecasting for Regions without Observations [13.805203053973772]
  We study spatial-temporal forecasting for a region of interest without any historical observations. We propose a model named STSM for the task. Our key insight is to learn from the locations that resemble those in the region of interest.
  arXiv  Detail & Related papers  (2024-01-19T06:26:05Z)
• Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution [89.16750999704969]
  Anomaly labels hinder traditional supervised models in time series anomaly detection. Various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. We propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD)
  arXiv  Detail & Related papers  (2023-11-19T05:37:18Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction [63.3021778885906]
  3D bounding boxes are a widespread intermediate representation in many computer vision applications. We propose methods for leveraging our autoregressive model to make high confidence predictions and meaningful uncertainty measures. We release a simulated dataset, COB-3D, which highlights new types of ambiguity that arise in real-world robotics applications.
  arXiv  Detail & Related papers  (2022-10-13T23:57:40Z)
• Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data [3.050919759387984]
  We build a general method for classifying anomalies in multi-dimensional time-series data. We demonstrate our proposed method on the important real-world task of global atmospheric pollution monitoring. The system successfully detects localized anomalies in air quality due to events such as COVID-19 lockdowns and wildfires.
  arXiv  Detail & Related papers  (2022-09-18T17:59:04Z)
• Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation [70.32536356351706]
  We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations. We derive suitable measures to quantify prediction uncertainty at both pose and joint level. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-29T07:14:58Z)
• Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments [110.79706180350507]
  We show that our proposed loss can be used to define temporal-temporal baryechecenters as Fr'teche means duality. Experiments on handwritten letters and brain imaging data confirm our theoretical findings.
  arXiv  Detail & Related papers  (2022-03-11T09:46:22Z)
• Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction [1.2862507359003323]
  We present a novel deep learning architecture that incorporates theories of spatial statistics into neural networks. DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends. We conduct a demonstration of DeepLATTE using publicly available data for an important public health topic, air quality prediction in a well-fitting, complex physical environment.
  arXiv  Detail & Related papers  (2021-12-10T03:21:19Z)
• Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction [16.654369376687296]
  We present a purely attentional approach to extract both short-term dynamics and long-term semantics of event propagation through two observation angles. The proposed contrastive learning objective significantly enhances the MAPSED's ability to capture semantics and dynamics of events.
  arXiv  Detail & Related papers  (2021-10-05T02:38:46Z)
• Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems [0.7340017786387767]
  In this paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches.
  arXiv  Detail & Related papers  (2021-06-15T17:33:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.