Event detection is gaining increasing attention in smart cities research. Large-scale mobility data serves as an important tool to uncover the dynamics of urban transportation systems, and more often than not the dataset is incomplete. In this article, we develop a method to detect extreme events in large traffic datasets, and to impute missing data during regular conditions. Specifically, we propose a robust tensor recovery problem to recover low rank tensors under fiber-sparse corruptions with partial observations, and use it to identify events, and impute missing data under typical conditions. Our approach is scalable to large urban areas, taking full advantage of the spatio-temporal correlations in traffic patterns. We develop an efficient algorithm to solve the tensor recovery problem based on the alternating direction method of multipliers (ADMM) framework. Compared with existing @math norm regularized tensor decomposition methods, our algorithm can exactly recover the values of uncorrupted fibers of a low rank tensor and find the positions of corrupted fibers under mild conditions. Numerical experiments illustrate that our algorithm can exactly detect outliers even with missing data rates as high as 40 , conditioned on the outlier corruption rate and the Tucker rank of the low rank tensor. Finally, we apply our method on a real traffic dataset corresponding to downtown Nashville, TN, USA and successfully detect the events like severe car crashes, construction lane closures, and other large events that cause significant traffic disruptions.
The outliers we are interested in this work are due to outliers caused by extreme events. Another related problem considers methods to detect outliers caused by data measurement errors, such as sensor malfunction, malicious tampering, or measurement error @cite_13 @cite_2 @cite_38 . The latter methods can be seen as a part of a standard data cleaning or data pre-processing step. On the other hand, outliers caused by extreme traffic have valuable information for congestion management, and can provide agencies with insights into the performance of urban network. The works @cite_1 @cite_35 @cite_21 explore the problem of outlier detection caused by events, while the works @cite_6 @cite_3 @cite_50 @cite_4 focus on determining the root causes of the outlier.
@cite_38 @cite_35 @cite_4 @cite_21 @cite_1 @cite_6 @cite_3 @cite_50 @cite_2 @cite_13
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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. In order to improve the veracity and reliability of a traffic model built, or to extract important and valuable information from collected traffic data, the technique of outlier mining has been introduced into the traffic engineering domain for detecting and analyzing the outliers in traffic data sets. Three typical outlier algorithms, respectively the statistics-based approach, the distance-based approach, and the density-based local outlier approach, are described with respect to the principle, the characteristics and the time complexity of the algorithms. A comparison among the three algorithms is made through application to intelligent transportation systems (ITS). Two traffic data sets with different dimensions have been used in our experiments carried out, one is travel time data, and the other is traffic flow data. We conducted a number of experiments to recognize outliers hidden in the data sets before building the travel time prediction model and the traffic flow foundation diagram. In addition, some artificial generated outliers are introduced into the traffic flow data to see how well the different algorithms detect them. Three strategies-based on ensemble learning, partition and average LOF have been proposed to develop a better outlier recognizer. The experimental results reveal that these methods of outlier mining are feasible and valid to detect outliers in traffic data sets, and have a good potential for use in the domain of traffic engineering. The comparison and analysis presented in this paper are expected to provide some insights to practitioners who plan to use outlier mining for ITS data. Traffic volume data is already collected and used for a variety of purposes in intelligent transportation system (ITS). However, the collected data might be abnormal due to the problem of outlier data caused by malfunctions in data collection and record systems. To fully analyze and operate the collected data, it is necessary to develop a validate method for addressing the outlier data. Many existing algorithms have studied the problem of outlier recovery based on the time series methods. In this paper, a multiway tensor model is proposed for constructing the traffic volume data based on the intrinsic multilinear correlations, such as day to day and hour to hour. Then, a novel tensor recovery method, called ADMM-TR, is proposed for recovering outlier data of traffic volume data. The proposed method is evaluated on synthetic data and real world traffic volume data. Experimental results demonstrate the practicability, effectiveness, and advantage of the proposed method, especially for the real world traffic volume data. Cross-scene regression tasks, such as congestion level detection and crowd counting, are useful but challenging. There are two main problems, which limit the performance of existing algorithms. The first one is that no appropriate congestion-related feature can reflect the real density in scenes. Though deep learning has been proved to be capable of extracting high level semantic representations, it is hard to converge on regression tasks, since the label is too weak to guide the learning of parameters in practice. Thus, many approaches utilize additional information, such as a density map, to guide the learning, which increases the effort of labeling. Another problem is that most existing methods are composed of several steps, for example, feature extraction and regression. Since the steps in the pipeline are separated, these methods face the problem of complex optimization. To remedy it, a deep metric learning-based regression method is proposed to extract density related features, and learn better distance measurement simultaneously. The proposed networks trained end-to-end for better optimization can be used for crowdedness regression tasks, including congestion level detection and crowd counting. Extensive experiments confirm the effectiveness of the proposed method.