Practical Data Management Techniques for Vehicle Tracking Data

Brakatsoulas, Sotiris; Pfoser, Dieter; Tryfona, Nectaria

doi:10.1109/icde.2005.109

“…1). The client keeps extracting coordinates from the GPS stream until recording is deactivated (6)(7)(8). Upon deactivation, the end of the route is noted (9) for further analysis.…”

Section: Client-side Route Recordingmentioning

confidence: 99%

Enabling Routes of Road Network Constrained Movements as Mobile Service Context

Brilingaitė

¹

,

Jensen

²

2007

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With the continuing advances in wireless communications, geo-positioning, and portable electronics, an infrastructure is emerging that enables the delivery of on-line, location-enabled services to very large numbers of mobile users. A typical usage situation for mobile services is one characterized by a small screen and no keyboard, and by the service being only a secondary focus of the user. Under such circumstances, it is particularly important to deliver the "right" information and service at the right time, with as little user interaction as possible. This may be achieved by making services context aware. Mobile users frequently follow the same route to a destination as they did during previous trips to the destination, and the route and destination constitute important aspects of the context for a range of services. This paper presents key concepts underlying a software component that identifies and accumulates the routes of a user along with their usage patterns and that makes the routes available to services. The problems associated with of route recording are analyzed, and algorithms that solve the problems are presented. Experiences from using the component on logs of GPS positions acquired from vehicles traveling within a real road network are reported.

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“…cellular phones, automobiles). With the number of smartphones in use world wide reaches 1.038 billion units in 2012 and is predicted to reach 2 billion units by 2015 1 , LBS revenue is forecasted to reach an annual global total of $13.5 billion by 2015 2 , up from $4 billion in 2012 3 . Ubiquitous GPS/WiFienabled mobile devices generate a huge amount of trajectory data, which are sequences of time-ordered locations of mobile objects.…”

Section: Introductionmentioning

confidence: 99%

“…Ubiquitous GPS/WiFienabled mobile devices generate a huge amount of trajectory data, which are sequences of time-ordered locations of mobile objects. There has been a lot of work on collecting, storing, indexing and querying trajectories of mobile objects [1] [2] [3] [4] [5]. We refer to the trajectories of mobile objects in a road network as MO trajectories.…”

Section: Introductionmentioning

confidence: 99%

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

Han

¹

,

Liu

²

,

Omiecinski

³

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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“…Ubiquitous GPS/WiFi-enabled mobile devices generate a huge amount of trajectory data, which are sequences of time-ordered locations of mobile objects. There has been a lot of work on collecting, storing, indexing and querying trajectories of mobile objects [1] [2] [3] [4] [5]. We refer to the trajectories of mobile objects in a road network as MO trajectories.…”

mentioning

confidence: 99%

NEAT: Road Network Aware Trajectory Clustering

Han

¹

,

Liu

²

,

Omiecinski

³

2012

2012 IEEE 32nd International Conference on Distributed Computing Systems

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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 location based 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 of mobile objects travelling in road networks. In this paper, we propose NEAT−a road network aware approach for fast and effective clustering of spatial trajectories of mobile objects travelling in road networks. Our method takes into account the physical constraints of the road network, the network proximity and the traffic flows among consecutive road segments to organize trajectories into spatial clusters. The clusters discovered by NEAT are groups of sub-trajectories which describe both dense and highly continuous traffic flows of mobile objects. We perform extensive experiments with mobility traces generated using different scales of real road network maps. Our experimental results demonstrate that the NEAT approach is highly accurate and runs orders of magnitude faster than existing density-based trajectory clustering approaches. I. INTRODUCTIONLocation-based services (LBSs) and applications are a rapidly growing field due to the pervasive use of GPS receivers and WiFi or location sensing technology embedded in mobile devices (e.g. cellular phones, automobiles). With shipments of smartphones projected to grow from 295 million units in 2010 to 1.2 billion units in 2015 1 , LBS revenue is forecasted to reach an annual global total of $10.3 billion in 2015, up from $2.8 billion in 2010 2 . Ubiquitous GPS/WiFi-enabled mobile devices generate a huge amount of trajectory data, which are sequences of time-ordered locations of mobile objects. There has been a lot of work on collecting, storing, indexing and querying trajectories of mobile objects. We refer to the trajectories of mobile objects in a road network as MO trajectories. Clustering trajectories of these objects provides the most value and has a wide range of LBS applications. For example, the resulting clusters would help provide knowledge about traffic flows as well as dense areas in a road network. Such knowledge is very useful for applications in vehicular ad hoc network (VANET) [6] [7], traffic monitoring [8], transportation planning [9] and location-based advertising [10]. We briefly present below two interesting application scenarios which show the usefulness of trajectory clustering and motivate us to study the

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Practical Data Management Techniques for Vehicle Tracking Data

Cited by 36 publications

References 6 publications

Enabling Routes of Road Network Constrained Movements as Mobile Service Context

Enabling Routes of Road Network Constrained Movements as Mobile Service Context

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

NEAT: Road Network Aware Trajectory Clustering

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