The performance of neural network models is often limited by the availability of big datasets. To treat this problem, we survey and develop novel synthetic data generation and augmentation techniques for enhancing low/zero-sample learning in satellite imagery. In addition to extending synthetic data generation approaches, we propose a hierarchical detection architecture to improve the utility of synthetic training samples. We consider existing techniques for producing synthetic imagery–3D models and neural style transfer–as well as introducing our own adversarially trained reskinning network, the GAN-Reskinner, to blend 3D models. Additionally, we test the value of synthetic data in a two-stage, hierarchical detection/classification model of our own construction. To test the effectiveness of synthetic imagery, we employ it in the training of detection models and our two stage model, and evaluate the resulting models on real satellite images. All modalities of synthetic data are tested extensively on practical, geospatial analysis problems. Our experiments show that synthetic data developed using our approach can often enhance detection performance, particularly when combined with some real training images. When the only source of data is synthetic, our GAN-Reskinner often boosts performance over conventionally rendered 3D models and in all cases, the hierarchical model outperforms the baseline end-to-end detection architecture.

The development of algorithms to assess the effects of natural disasters plays an integral role in response efforts. There is a growing opportunity to leverage remote sensing data and computer vision to quickly analyze the scale of damage and organize a humanitarian response when extreme weather events occur. By automating the process of identifying damage to roads and infrastructure, we can significantly reduce response time, directing relief efforts on a time scale of minutes or hours rather than days. The SpaceNet 8 challenge featured a complex multi-class segmentation problem in the context of flood detection from remote sensing imagery. Competitors were tasked with leveraging both pre- and post-flooding event imagery to detect buildings and roads, as well as identify which of these object instances were affected by the flooding event. We examine the outcome of the SpaceNet 8 challenge and present an overview of the competition and a deeper look at the top-performing submissions.

Floods are one of the major types of natural disasters responsible for loss of life, destruction of buildings and infrastructure, erosion of arable land, and environmental hazards around the world. Climate change, increasing populations, and urbanisation of flood plains will only increase the risk of flooding in the next few years. SpaceNet 8 presents a dataset that combines building footprint detection, road network extraction, and flood detection covering 850km2, including ~32,000 buildings and ~ 1,300 km of roads, of which ~ 13% and ~ 15% are flooded, respectively.

In today’s era of big data, robust least-squares regression becomes a more challenging problem when considering the extremely corrupted labels along with explosive growth of datasets. Traditional robust methods can handle the noise but suffer from several challenges when applied in huge dataset including (1) computational infeasibility of handling an entire dataset at once, (2) existence of heterogeneously distributed corruption, and (3) difficulty in corruption estimation when data cannot be entirely loaded. This article proposes online and distributed robust regression approaches, both of which can concurrently address all the above challenges. Specifically, the distributed algorithm optimizes the regression coefficients of each data block via heuristic hard thresholding and combines all the estimates in a distributed robust consolidation. In addition, an online version of the distributed algorithm is proposed to incrementally update the existing estimates with new incoming data. Furthermore, a novel online robust regression method is proposed to estimate under a biased-batch corruption. We also prove that our algorithms benefit from strong robustness guarantees in terms of regression coefficient recovery with a constant upper bound on the error of state-of-the-art batch methods. Extensive experiments on synthetic and real datasets demonstrate that our approaches are superior to those of existing methods in effectiveness, with competitive efficiency.

The presence of data noise and corruptions has recently invoked increasing attention on robust least-squares regression ( RLSR ), which addresses this fundamental problem that learns reliable regression coefficients when response variables can be arbitrarily corrupted. Until now, the following important challenges could not be handled concurrently: (1) rigorous recovery guarantee of regression coefficients, (2) difficulty in estimating the corruption ratio parameter, and (3) scaling to massive datasets. This article proposes a novel Robust regression algorithm via Heuristic Corruption Thresholding ( RHCT ) that concurrently addresses all the above challenges. Specifically, the algorithm alternately optimizes the regression coefficients and estimates the optimal uncorrupted set via heuristic thresholding without a pre-defined corruption ratio parameter until its convergence. Moreover, to improve the efficiency of corruption estimation in large-scale data, a Robust regression algorithm via Adaptive Corruption Thresholding ( RACT ) is proposed to determine the size of the uncorrupted set in a novel adaptive search method without iterating data samples exhaustively. In addition, we prove that our algorithms benefit from strong guarantees analogous to those of state-of-the-art methods in terms of convergence rates and recovery guarantees. Extensive experiments demonstrate that the effectiveness of our new methods is superior to that of existing methods in the recovery of both regression coefficients and uncorrupted sets, with very competitive efficiency.

The presence of data corruption in user-generated streaming data, such as social media, motivates a new fundamental problem that learns reliable regression coefficient when features are not accessible entirely at one time. Until now, several important challenges still cannot be handled concurrently: 1) corrupted data estimation when only partial features are accessible; 2) online feature selection when data contains adversarial corruption; and 3) scaling to a massive dataset. This paper proposes a novel RObust regression algorithm via Online Feature Selection (RoOFS) that concurrently addresses all the above challenges. Specifically, the algorithm iteratively updates the regression coefficients and the uncorrupted set via a robust online feature substitution method. Extensive empirical experiments in both synthetic and real-world data sets demonstrated that the effectiveness of our new method is superior to that of existing methods in the recovery of both feature selection and regression coefficients, with very competitive efficiency.

The scarcity of potable water is a critical challenge in many regions around the world. Previous studies have shown that knowledge of device-level water usage can lead to significant conservation. Although there is considerable interest in determining discriminative features via sparse coding for water disaggregation to separate whole-house consumption into its component appliances, existing methods lack a mechanism for fitting coefficient distributions and are thus unable to accurately discriminate parallel devices’ consumption. This article proposes a Bayesian discriminative sparse coding model, referred to as Virtual Metering (VM), for this disaggregation task. Mixture-of-Gammas is employed for the prior distribution of coefficients, contributing two benefits: (i) guaranteeing the coefficients’ sparseness and non-negativity, and (ii) capturing the distribution of active coefficients. The resulting method effectively adapts the bases to aggregated consumption to facilitate discriminative learning in the proposed model, and devices’ shape features are formalized and incorporated into Bayesian sparse coding to direct the learning of basis functions. Compact Gibbs Sampling (CGS) is developed to accelerate the inference process by utilizing the sparse structure of coefficients. The empirical results obtained from applying the new model to large-scale real and synthetic datasets revealed that VM significantly outperformed the benchmark methods.

The problems of recurrent and anomalous pattern discovery in time series, e.g., motifs and discords, respectively, have received a lot of attention from researchers in the past decade. However, since the pattern search space is usually intractable, most existing detection algorithms require that the patterns have discriminative characteristics and have its length known in advance and provided as input, which is an unreasonable requirement for many real-world problems. In addition, patterns of similar structure, but of different lengths may co-exist in a time series. Addressing these issues, we have developed algorithms for variable-length time series pattern discovery that are based on symbolic discretization and grammar inference—two techniques whose combination enables the structured reduction of the search space and discovery of the candidate patterns in linear time. In this work, we present GrammarViz 3.0—a software package that provides implementations of proposed algorithms and graphical user interface for interactive variable-length time series pattern discovery. The current version of the software provides an alternative grammar inference algorithm that improves the time series motif discovery workflow, and introduces an experimental procedure for automated discretization parameter selection that builds upon the minimum cardinality maximum cover principle and aids the time series recurrent and anomalous pattern discovery.

In this era of big data, many domains on the web naturally have massive amount of labeled text data that are growing over time, for example, digital publication archives, social media posts, and question-answer forums. Probabilistic graphical models have shown great potential for mining such text corpora in recent years. Some of these algorithms utilize explicit annotations and labels associated with documents to guide the probabilistic model to find hidden themes. A few techniques attempt to utilize the timestamps associated with documents to model the evolution of those latent topics. However, no effort has been devoted to utilize these two different dimensions of information together — timestamps and labels or annotations — to discover evolution of labeled themes. In this paper, we present a new topical model called the Supervised Topical Evolution Model (STEM), which is a monolithic graphical model capable of using annotations, timestamps, and textual contents to discover interpretable and evolving themes from big text datasets. STEM simultaneously learns latent themes and their changes over time using a stochastic process that is driven by labels or annotations. In addition, we provide an asynchronously distributed inference process for STEM that results in significant speedup in learning time, making the model scalable for large datasets. Extensive experiments demonstrate that our proposed model is able to infer highly interpretable topics that reflect temporal patterns, in much less time than other comparable topic modeling methods.

In the era of information overload, people are struggling to make sense of complex story events in massive social media data. Most existing approaches are designed to address event extraction in news reports, documents and abstracts, but such approaches are not suitable for Twitter data streams due to their unstructured language, short-length messages, and heterogeneous features; few existing approach generates a story by considering both the shared topics throughout the story and the smooth connection between successive nodes simultaneously. In this paper, a novel Twitter stoRy generation framework via shAred subspaCe and tEmporal Smoothness called TRACES is proposed. Given a query of an ongoing event, a novel multi-task clustering method integrated with shared subspace and temporal smoothness (STMTC) is proposed to generate the event stories. Extensive experimental evaluations of data sets for different events demonstrate the effectiveness of this new approach.

In today's era of information overload, people are struggling to detect the evolution of hot topics from massive news media and microblogs such as Twitter. Reports from mainstream news agencies and discussions from microblogs could complement each other to form a complete picture of major events. Existing work has generally focused on a single source, seldom attempting to combine multiple sources to track the evolution of topics: emerging, evolving and fading phrases as this would require a considerably more sophisticated model. This paper proposes a novel story discovery model that integrates evolutionary topics in news and Twitter data sources using an incremental algorithm by 1) discovering complementary information from news and microblogs that provides a more complete view of major events; 2) modeling emerging, evolving and fading topics and features throughout ongoing events; and 3) creating a scalable algorithm that is capable of handling massive data from news and social media. The parameters of the new model are optimized using a novel algorithm based on the alternative direction method of multipliers (ADMM). Extensive experimental evaluations on multiple datasets from different domains demonstrate the effectiveness and efficiency of our proposed approach.

Hyper-local pricing data, e.g., about foods and commodities, exhibit subtle spatiotemporal variations that can be useful as crucial precursors of future events. Three major challenges in modeling such pricing data include: i) temporal dependencies underlying features; ii) spatiotemporal missing values; and iii) constraints underlying economic phenomena. These challenges hinder traditional event forecasting models from being applied effectively. This paper proposes a novel spatiotemporal event forecasting model that concurrently addresses the above challenges. Specifically, given continuous price data, a new soft time-lagged model is designed to select temporally dependent features. To handle missing values, we propose a data tensor completion method based on price domain knowledge. The parameters of the new model are optimized using a novel algorithm based on the Alternative Direction Methods of Multipliers (ADMM). Extensive experimental evaluations on multiple datasets demonstrate the effectiveness of our proposed approach.

In today's era of big data, robust least-squares regression becomes a more challenging problem when considering the adversarial corruption along with explosive growth of datasets. Traditional robust methods can handle the noise but suffer from several challenges when applied in huge dataset including 1) computational infeasibility of handling an entire dataset at once, 2) existence of heterogeneously distributed corruption, and 3) difficulty in corruption estimation when data cannot be entirely loaded. This paper proposes online and distributed robust regression approaches, both of which can concurrently address all the above challenges. Specifically, the distributed algorithm optimizes the regression coefficients of each data block via heuristic hard thresholding and combines all the estimates in a distributed robust consolidation. Furthermore, an online version of the distributed algorithm is proposed to incrementally update the existing estimates with new incoming data. We also prove that our algorithms benefit from strong robustness guarantees in terms of regression coefficient recovery with a constant upper bound on the error of state-of-the-art batch methods. Extensive experiments on synthetic and real datasets demonstrate that our approaches are superior to those of existing methods in effectiveness, with competitive efficiency.

Deriving event storylines is an effective summarization method to succinctly organize extensive information, which can significantly alleviate the pain of information overload. The critical challenge is the lack of widely recognized definition of storyline metric. Prior studies have developed various approaches based on different assumptions about users' interests. These works can extract interesting patterns, but their assumptions do not guarantee that the derived patterns will match users' preference. On the other hand, their exclusiveness of single modality source misses cross-modality information. This paper proposes a method, multimodal imitation learning via Generative Adversarial Networks(MIL-GAN), to directly model users' interests as reflected by various data. In particular, the proposed model addresses the critical challenge by imitating users' demonstrated storylines. Our proposed model is designed to learn the reward patterns given user-provided storylines and then applies the learned policy to unseen data. The proposed approach is demonstrated to be capable of acquiring the user's implicit intent and outperforming competing methods by a substantial margin with a user study.

The presence of data noise and corruptions recently invokes increasing attention on Robust Least Squares Regression (RLSR), which addresses the fundamental problem that learns reliable regression coefficients when response variables can be arbitrarily corrupted. Until now, several important challenges still cannot be handled concurrently: 1) exact recovery guarantee of regression coefficients 2) difficulty in estimating the corruption ratio parameter; and 3) scalability to massive dataset. This paper proposes a novel Robust Least squares regression algorithm via Heuristic Hard thresholding (RLHH), that concurrently addresses all the above challenges. Specifically, the algorithm alternately optimizes the regression coefficients and estimates the optimal uncorrupted set via heuristic hard thresholding without corruption ratio parameter until it converges. We also prove that our algorithm benefits from strong guarantees analogous to those of state-of-the-art methods in terms of convergence rates and recovery guarantees. We provide empirical evidence to demonstrate that the effectiveness of our new method is superior to that of existing methods in the recovery of both regression coefficients and uncorrupted sets, with very competitive efficiency.

Creation of summaries of events of interest from multitude of unstructured data is a challenging task commonly faced by intelligence analysts while seeking increased situational awareness. This paper proposes a framework called Storyboarding that leverages unstructured text and images to explain events as sets of sub-events. The framework first generates a textual context for each human face detected from images and then builds a chain of coherent documents where two consecutive documents of the chain contain a common theme as well as a context. Storyboarding helps analysts quickly narrow down large number of possibilities to a few significant ones for further investigation. Empirical studies on Wikipedia documents, images and news articles show that Storyboarding is able to provide deeper insights on events of interests.

Connecting the dots between diverse entities such as people and organizations is a vital task for forming hypotheses and uncovering latent relationships among complex and large datasets. Most existing approaches are designed to address the relationship of entities in news reports, documents and abstracts, but such approaches are not suitable for Twitter data streams due to their unstructured languages, short-length messages, heterogeneous features and massive size. The sheer size of Twitter data requires more efficient algorithms to connect the dots within a short period of time. We present a system that automatically constructs stories by connecting entities in Twitter datasets. An entity similarity model is designed that combines both traditional entity-related features and social network attributes and a novel story generation algorithm applied on the similarity model is proposed to cope with the massive Twitter datasets. Extensive experimental evaluations were conducted to demonstrate the effectiveness of this new approach.

Time series classication is an important problem that has received a great amount of attention by researchers and practitioners in the past two decades. In this work, we propose a novel algorithm for time series classication based on the discovery of class-specic representative patterns. We dene representative patterns of a class as a set of subsequences that has the greatest discriminative power to distinguish one class of time series from another. Our approach rests upon two techniques with linear complexity: symbolic discretization of time series, which generalizes the structural patterns, and grammatical inference, which automatically nds recurrent correlated patterns of variable length, producing an initial pool of common patterns shared by many instances in a class. From this pool of candidate patterns, our algorithm selects the most representative patterns that capture the class specicities, and that can be used to eectively discriminate between time series classes. Through an exhaustive experimental evaluation we show that our algorithm is competitive in accuracy and speed with the stateof-the-art classication techniques on the UCR time series repository, robust on shifted data, and demonstrates excellent performance on real-world noisy medical time series.

Traditional statistical analysis on speed, volume, and occupancy has dominated the field of Arterial Incident Management Study (AIMS). However, few previous works have focused on investigating into the causality of the incidents. In this paper, we present ButterFly, a social media based arterial incident detection and analysis system. The proposed system is dedicated to identify the traffic incident from a novel perspective and discover causalities between traffic incidents. The main functionalities of the proposed system include: 1) Traffic incident detection based on userinput social media contents, 2) Transportation incidents storyline generation, and 3) Traffic incidents causalities analysis and visualization. We demonstrate the system by considering the Washington DC area as our experimental environment. ButterFly is targeted to provide effective and convenient real-time and historical traffic incidents analysis interfaces for transportation management agencies and academies. Our proposed system, integrated with multiple social media resources, can greatly broaden the visions for traffic incidents analysis.

The Probability Density Function (PDF) is the fundamental data model for a variety of stream mining algorithms. Existing works apply the standard nonparametric Kernel Density Estimator (KDE) to approximate the PDF of data streams. As a result, the stream-based KDEs cannot accurately capture complex local density features. In this article, we propose the use of Local Region (LRs) to model local density information in univariate data streams. In-depth theoretical analyses are presented to justify the effectiveness of the LR-based KDE. Based on the analyses, we develop the General Local rEgion AlgorithM (GLEAM) to enhance the estimation quality of structurally complex univariate distributions for existing stream-based KDEs. A set of algorithmic optimizations is designed to improve the query throughput of GLEAM and to achieve its linear order computation. Additionally, a comprehensive suite of experiments was conducted to test the effectiveness and efficiency of GLEAM.

Spatial trajectory analysis is crucial to uncovering insights into the motives and nature of human behavior. In this work, we study the problem of discovering motifs in trajectories based on symbolically transformed representations and context free grammars. We propose a fast and robust grammar induction algorithm called mSEQUITUR to infer a grammar rule set from a trajectory for motif generation. Second, we designed the Symbolic Trajectory Analysis and VIsualization System (STAVIS), the first of its kind trajectory analytical system that applies grammar inference to derive trajectory signatures and enable mining tasks on the signatures. Third, an empirical evaluation is performed to demonstrate the efficiency and effectiveness of mSEQUITUR for generating trajectory signatures and discovering motifs.

A fundamental problem in analyzing trajectory data is to identify common patterns between pairs or among groups of trajectories. In this paper, we consider the problem of matching similar portions between a pair of trajectories, each observed as a sequence of points sampled from it. We present new measures of trajectory similarity --- both local and global --- between a pair of trajectories to distinguish between similar and dissimilar portions. We then use this model to perform segmentation of a set of trajectories into fragments, contiguous portions of trajectories shared by many of them.

Energy crisis and climate change have caused a global concern and motivated efforts to reduce energy consumption. Studies have shown that providing appliance-level consumption information can help users conserve a significant amount of energy. Existing methods focus on learning parallel signal signatures, but the inherent relationships between the signatures have not been well explored. This paper presents the Hierarchical Probabilistic Model for Energy Disaggregation (HPMED). We derive the discriminative features from low sample rate power readings to characterise device functional modes. The HPMED model bridges the discriminative features, working states, and aggregated consumption. To address the analytical intractable problem, an efficient algorithm is proposed to approximately infer the latent states for disaggregation task. Extensive experiments on a real-world dataset demonstrated the effectiveness of the proposed approach.

This paper presents an integrated framework for Spatio-Temporal-Textual (STT) information retrieval and knowledge discovery system. The proposed ensemble framework contains an efficient STT search engine with multiple indexing, ranking and scoring schemes, an effective STT pattern miner with Spatio-Temporal (ST) analytics, and novel STT topic modeling. Specifically, we design an effective prediction prototype with a third-order linear regression model, and present an innovative STT topic modeling relevance ranker to score documents based on inherent STT features under topical space. We demonstrate the framework with a crime dataset from the Washington, DC area from 2006 to 2010 and a global terrorism dataset from 2004 to 2010.

As the issue of freshwater shortage is increasing daily, it's critical to take effective measures for water conservation. Based on previous studies, device level consumption could lead to significant conservation of freshwater. However, current smart meter deployments only produce low sample rate aggregated data. In this paper, we examine the task of separating whole-home water consumption into its component appliances. A key challenge is to address the unique features of low sample rate data. To this end, we propose Sparse Coding with Featured Discriminative Dictionary (SCFDD) by incorporating inherent shape and activation features to capture the discriminative characteristics of devices. In addition, extensive experiments were performed to validate the effectiveness of SCFDD.

Intelligence analysts grapple with many challenges, chief among them is the need for software support in storytelling, i.e., automatically 'connecting the dots' between disparate entities (e.g., people, organizations) in an effort to form hypotheses and suggest non-obvious relationships. We present a system to automatically construct stories in entity networks that can help form directed chains of relationships, with support for co-referencing, evidence marshaling, and imposing syntactic constraints on the story generation process. A novel optimization technique based on concept lattice mining enables us to rapidly construct stories on massive datasets. Using several public domain datasets, we illustrate how our approach overcomes many limitations of current systems and enables the analyst to efficiently narrow down to hypotheses of interest and reason about alternative explanations.

With modern focus on LiDAR technology the amount of topographic data, in the form of massive point clouds, has increased dramatically. Furthermore, due to the popularity of LiDAR, repeated surveys of the same areas are becoming more common. This trend will only increase as topographic changes prompt surveys over already scanned terrain, in which case we obtain large spatio-temporal data sets.

Geographic feature expansion is a common task in Geographic Information Systems (GIS). Identifying and integrating geographic features is a challenging task since many of their spatial and non-spatial properties are described in different sources. We tackle this expansion problem by defining semantic footprints as a measure of similarity among features. Furthermore, we propose three quantifiers of semantic similarity: spatial, dimensional, and ontological affinity. We show how these measures dilute, concentrate, harden, or concede the feature space, and provide useful insights into the semantic relationships of the spatial entities. Experiments demonstrate the effectiveness of our approach in semantically associating the most appropriate spatial features.

Local based approach is a major category of methods for spatial outlier detection (SOD). Currently, there is a lack of systematic analysis on the statistical properties of this framework. For example, most methods assume identical and independent normal distributions (i.i.d. normal) for the calculated local differences, but no justifications for this critical assumption have been presented. The methods' detection performance on geostatistic data with linear or nonlinear trend is also not well studied. In addition, there is a lack of theoretical connections and empirical comparisons between local and global based SOD approaches. This paper discusses all these fundamental issues under the proposed Generalized Local Statistical (GLS) framework. Furthermore, robust estimation and outlier detection methods are designed for the new GLS model. Extensive simulations demonstrated that the SOD method based on the GLS model significantly outperformed all existing approaches when the spatial data exhibits a linear or nonlinear trend.

The hierarchical smoothed round robin (HSRR) packet scheduler that improves the performance of the smoothed round robin (SRR) for real-world packet scheduling scenarios is presented. The SRR suffers from an inability to fairly distribute service in highly skewed weight distributions where such distributions often occur in practice. As a result, the transmission behaviour of the SRR can be bursty and non-smooth for the high weighted (or high priority) flows. A novel aggregation strategy is proposed to reshape the sessions' weight distribution that aims to minimise the variance of the high-priority flows' intra-session service times. The aggregation scheme also provides a new capability that supports the provisioning of high-priority service to an arbitrary set of selected flows. The reshaped weight distribution results in guaranteed lower bound in maximum packet delay for the high-priority flows. Furthermore, empirical analyses show that the cumulative average packet delay and jitter of the high-priority flows are significantly reduced from the original SRR and also overall lower for all flows. Along with the enhanced performance in packet delays, the HSRR scheduler attains a worst-case time complexity of O(1).

Probability density function estimation is a fundamental component in several stream mining tasks such as outlier detection and classification. The nonparametric adaptive kernel density estimate (AKDE) provides a robust and asymptotically consistent estimate for an arbitrary distribution. However, its extensive computational requirements make it difficult to apply this technique to the stream environment. This paper tackles the issue of developing efficient and asymptotically consistent AKDE over data streams while heeding the stringent constraints imposed by the stream environment. We propose the concept of local regions to effectively synopsize local density features, design a suite of algorithms to maintain the AKDE under a time-based sliding window, and analyze the estimates' asymptotic consistency and computational costs. In addition, extensive experiments were conducted with real-world and synthetic data sets to demonstrate the effectiveness and efficiency of our approach.

This work proposes high-performance critical visualization techniques for exploring real-time and historical traffic loop-detector data. Until recently, it has been difficult to discover trends, identify patterns, or locate abnormalities within the massive collection of traffic data. Many of the current visualization techniques do not scale to large data sets and are not practical for interactive visualization. We have developed an effective visualization system, Highway Operation Monitoring and Evaluation System (HOMES), for observing the summarization of spatiotemporal patterns and trends in traffic data. HOMES is designed for browsing the spatial-temporal dimension hierarchy via integrated roll-up and drill-down operations. The identified traffic patterns and rules can assist decision-making for transportation managers, establish traffic models for researchers and planners, and allow travelers to select commuting routes.

Locally linear classification by pairwise coupling addresses a nonlinear classification problem by three basic phases: decompose the classes of complex concepts into linearly separable subclasses, learn a linear classifier for each pair, and combine pairwise classifiers into a single classifier. A number of methods have been proposed in this framework. However, these methods have two major deficiencies: 1) lack of systematic evaluation of this framework; 2) naive application of clustering algorithms to generate subclasses. This paper proves the equivalence between three popular combination schemas under general settings, defines several global criterion functions for measuring the goodness of subclasses, and presents a supervised greedy clustering algorithm to optimize the proposed criterion functions. Extensive experiments were conducted to validate the effectiveness of the proposed techniques.

Transportation and the highway network form the backbone of the total public infrastructure system. As such, planning and monitoring for an effective transportation system is crucial in the building and maintenance of a region’s economy and safety. However, demand for road travel continues to expand as population increases (particularly in the metropolitan areas) while new constructions have not kept pace. According to the Federal Highway Administration, it is forecasted that the volume of freight movement alone is to nearly double by 2020 [1]. Congestion and looming gridlock crises seriously threaten any region’s mobility, safety and economic vitality. A crucial component in addressing these concerns is the development of specific technologies to monitor, model, and optimize traffic flow.