NEAT: Road Network Aware Trajectory Clustering

Han, Binh; Liu, Ling; Omiecinski, Edward

doi:10.1109/icdcs.2012.31

Cited by 40 publications

(18 citation statements)

References 21 publications

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“…Usually, distance measures are no longer based on the Euclidian distance but on algorithms for finding the shortest path in a graph [Tiakas et al 2009]. In [Han et al 2012] the authors find clusters of segments of cars' trajectories by looking for sequences of contiguous road segments that are followed by continuous traffic flows. Trajectory clustering has been used in several contexts as trajectory searching and querying [Panagiotakis et al 2012], trajectory visualization [Rinzivillo et al 2008], and processing of trajectory uncertainty (e.g.…”

Section: General Characteristics For Trajectory Knowledge Discoverymentioning

confidence: 99%

Semantic trajectories modeling and analysis

et al. 2013

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___________________________________________________________________Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility data management, and a survey of the approaches and techniques for i) constructing trajectories from movement tracks, ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the paper surveys the new privacy issues that rise due to the semantic aspects of trajectories.

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Section: General Characteristics For Trajectory Knowledge Discoverymentioning

confidence: 99%

Semantic trajectories modeling and analysis

et al. 2013

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“…Once trajectory clusters are obtained, the next step is to define certain representative features for each cluster so that a new trajectory can be evaluated and classified. For a given cluster, a certain representative trajectory could be constructed based on the trajectories in the given cluster (Lee et al, 2007) or the underlying road segments could be used if trajectories are mapped to the underlying road networks (Han et al, 2012;Kharrat et al, 2008;Roh and Hwang, 2010). In this paper, we propose another approach based on the LCS between trajectories that were obtained in the first step of the framework.…”

Section: Generating Cluster Representative Subsequencesmentioning

confidence: 98%

“…Roh and Hwang (2010) proposed a distance measure that reflects road-network proximity, computed using the shortest path calculation, and applied in their trajectory clustering algorithm (NNCluster). Han et al (2012) proposed a road network aware approach for clustering trajectories (NEAT) and Mahrsi and Rossi (2013) proposed a graph-based approach to clustering network-constrained trajectory data.…”

Section: Related Workmentioning

confidence: 99%

Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories

Kim

Mahmassani

2015

Transportation Research Part C: Emerging Technologies

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a b s t r a c tThis paper presents a trajectory clustering method to discover spatial and temporal travel patterns in a traffic network. The study focuses on identifying spatially distinct traffic flow groups using trajectory clustering and investigating temporal traffic patterns of each spatial group. The main contribution of this paper is the development of a systematic framework for clustering and classifying vehicle trajectory data, which does not require a pre-processing step known as map-matching and directly applies to trajectory data without requiring the information on the underlying road network. The framework consists of four steps: similarity measurement, trajectory clustering, generation of cluster representative subsequences, and trajectory classification. First, we propose the use of the Longest Common Subsequence (LCS) between two vehicle trajectories as their similarity measure, assuming that the extent to which vehicles' routes overlap indicates the level of closeness and relatedness as well as potential interactions between these vehicles. We then extend a density-based clustering algorithm, DBSCAN, to incorporate the LCS-based distance in our trajectory clustering problem. The output of the proposed clustering approach is a few spatially distinct traffic stream clusters, which together provide an informative and succinct representation of major network traffic streams. Next, we introduce the notion of Cluster Representative Subsequence (CRS), which reflects dense road segments shared by trajectories belonging to a given traffic stream cluster, and present the procedure of generating a set of CRSs by merging the pairwise LCSs via hierarchical agglomerative clustering. The CRSs are then used in the trajectory classification step to measure the similarity between a new trajectory and a cluster. The proposed framework is demonstrated using actual vehicle trajectory data collected from New York City, USA. A simple experiment was performed to illustrate the use of the proposed spatial traffic stream clustering in application areas such as network-level traffic flow pattern analysis and travel time reliability analysis.

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“…NEAT innovates TraClus framework in a creative manner with higher efficiency (due to reduction of network distance computation) and higher accuracy (due to incorporation of flow semantics). This paper provides a full-fledged development of the initial NEAT approach [34] with a thorough study on different merging decisions and an adaptive parameter assignment scheme capturing the most critical characteristics of a traffic stream in the core of NEAT, which allows it to discover important flow clusters in an automated manner.…”

Section: Related Workmentioning

confidence: 99%

Road-Network Aware Trajectory Clustering: Integrating Locality, Flow, and Density

Han

Liu

Omiecinski

2015

IEEE Trans. on Mobile Comput.

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Abstract-Mining trajectory data has been gaining significant interest in recent years. However, existing approaches to trajectory clustering are mainly based on density and Euclidean distance measures. We argue that when the utility of spatial clustering of mobile object trajectories is targeted at road-network aware locationbased applications, density and Euclidean distance are no longer the effective measures. This is because traffic flows in a road network and the flow-based density characterization become important factors for finding interesting trajectory clusters. We propose NEAT a road-network aware approach for fast and effective clustering of trajectories of mobile objects travelling in road networks. Our approach carefully considers the traffic locality characterized by the physical constraints of the road network, the traffic flow among consecutive road segments, and the flow-based density to organize trajectories into spatial clusters in a comprehensive three-phase clustering framework. NEAT discovers spatial clusters as groups of sub-trajectories which describe both dense and highly continuous flows of mobile objects. We perform extensive experiments with mobility traces generated using different scales of real road networks. Experimental results demonstrate the flexibility of the NEAT system and show that NEAT is highly accurate and runs orders of magnitude faster than existing density-based trajectory clustering approaches.

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NEAT: Road Network Aware Trajectory Clustering

Cited by 40 publications

References 21 publications

Semantic trajectories modeling and analysis

Semantic trajectories modeling and analysis

Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories

Road-Network Aware Trajectory Clustering: Integrating Locality, Flow, and Density

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