Discovering spatio-temporal causal interactions in traffic data streams

By Yu Zheng, Sanjay Chawla, Jing Yuan, Xing Xie. Knowledge Discovery and Data Mining, 2011

401 citations recorded by the source corpus.

The detection of outliers in spatio-temporal traffic data is an important research problem in the data mining and knowledge discovery community. However to the best of our knowledge, the discovery of relationships, especially causal interactions, among detected traffic outliers has not been investigated before. In this paper we propose algorithms which construct outlier causality trees based on temporal and spatial properties of detected outliers. Frequent substructures of these causality trees reveal not only recurring interactions among spatio-temporal outliers, but potential flaws in the design of existing traffic networks. The effectiveness and strength of our algorithms are validated by experiments on a very large volume of real taxi trajectories in an urban road network.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/aae986112d3b29cd26e290734c9165b395e9afe5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yu Zheng
2. Vipin Kumar
3. Anuj Karpatne
4. Yan Liu
5. Yaguang Li
6. Xing Xie
7. Krzysztof Janowicz
8. Ouri Wolfson

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Ouri Wolfson
4. Jing Yuan
5. Licia Capra
6. Hai Yang
7. Cyrus Shahabi
8. Krzysztof Janowicz

Citations in the corpus

• Spatio-Temporal Data Mining (2017, methodology): “A graph-based approach for detecting outliers in traffic data streams was developed in [Liu et al. 2011], where nodes are regions and edge weights represent the traffic flow between regions.”
• Discovering regions of different functions in a city using human mobility and POIs (2012, background): “Based on the traffic flow represented by taxi trajectories, the technology for detecting anomalies in urban areas has been reported in [10].”
• Urban Computing: Concepts, Methodologies, and Applications (2014, background): “2013], and detecting anomalies in a city [Liu et al. 2011; Chawla et al. 2012; Pan et al. 2013; Pang et al. 2011, 2013].”
• Methodologies for Cross-Domain Data Fusion: An Overview (2015, background): “nose traffic anomalies [15], [43], as well as find urban functional regions [74], [76].”
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (2017, methodology): “In time series community, data-driven methods such as Auto-Regressive Integrated Moving Average (ARIMA) model and Kalman filtering remain popular (Liu et al., 2011; Lippi et al., 2013).”
• Trajectory Data Mining (2015, background): “traffic anomalies [62][15] and urban functional regions [122][130].”
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting (2020, neutral): “Previous approaches such as time-series models [2] and Kilman filtering models [3] are mostly focus on short-term forecasting and perform quiet well.”
• Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting (2020, background): “Driven by big data machine learning models for traffic forecasting have attracted considerable attention from both academia and industry for several years (Dai et al., 2019; Liu, Zheng, Chawla, Yuan, & Xing, 2011; Lv, Duan, Kang, Li, & Wang, 2014; Yao et al., 2019; Zhao et al., 2019).”