On map matching of wireless positioning data

Weber, Matt; Liu, Ling; Jones, Kipp; Covington, Michael J.; Nachman, Lama; Pesti, Peter

doi:10.1145/1869790.1869832

“…If they are contiguous, we can obtain the road junction node that intersects these two road segments. If the two road segments happen to be not contiguous, we can obtain the sequence of road junction nodes connecting them on the travel path of the object using the map-matching approach [15]. Next, we insert the obtained junction node(s) as new points in between l i and l i+1 in the trajectory being examined.…”

Section: Partitioning Trajectories Into T-fragmentsmentioning

confidence: 99%

“…When a given set of trajectories are given as time series of geometric coordinates, NEAT will first preprocess the set of trajectories using mapmatching (MM) algorithms such that each point in a trajectory is mapped to a road network location as defined in Section 2.1. We use the SLAMM mapmatching algorithm [15] in this data prepocessing step. MM algorithms for bulk location data are more effective as noted in [15] because look-ahead and look-around algorithms can catch many known errors of earlier MM algorithms, such as map-matching location samples between two nearby parallel road segments.…”

Section: Partitioning Trajectories Into T-fragmentsmentioning

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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“…If they are contiguous, we can obtain the road junction node that intersects these two road segments. If the two road segments happen to be not contiguous, we can obtain the sequence of road junction nodes connecting them on the travel path of the object using the map-matching approach [14]. Next, we insert the obtained junction node(s) as new points in between l i and l i+1 in the trajectory being examined.…”

Section: A Base Cluster Formationmentioning

confidence: 99%

“…We use the SLAMM map-matching algorithm [14] in this data prepocessing step. Map-matching (MM) algorithms for bulk location data are more effective as noted in [14] because look-ahead and lookaround algorithms can catch many known errors of earlier MM algorithms, such as map-matching location samples between two nearby parallel road segments.…”

Section: A Base Cluster Formationmentioning

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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“…A second class primarily focuses on maximizing throughput without compromising quality, thus taking a data management perspective. Throughput-oriented techniques can be further characterized either as: (a) incremental based solely on positional samples [8,5], when they examine edge distances and orientation of movement so as to greedily expand the existing path with an additional edge for fast response, or (b) global [2,3,4,12,21,23], in case that they check against all possible trajectories to find the most similar to the actual movement with better accuracy. More specifically, [4] proposed an -road-snapped trajectory construction algorithm in a weighted graph representation that returns a path, whose Hausdorff distance to the vehicle trajectory does not exceed location-sensor error .…”

Section: Related Workmentioning

confidence: 99%

Event Processing and Real-Time Monitoring over Streaming Traffic Data

Patroumpas

¹

,

Sellis

²

2012

Lecture Notes in Computer Science

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Abstract. Tracking mobility of humans, animals or merchandise has recently given rise to a wide variety of location-based services and monitoring applications. In this paper, we particularly focus on real-time traffic surveillance over densely congested road networks in large metropolitan areas. In such a setting, streaming positional updates are being frequently relayed into a central server from numerous moving vehicles (buses, taxis, passenger cars etc.). Our analysis concerns two important aspects. First, we outline characteristics of a robust processing engine that is capable to efficiently manage such massive, transient, and perhaps noisy geospatial data. Our objective is to provide online aggregates and reliable estimates regarding the current traffic situation at multiple levels of resolution. At a second step, we design a framework for effective multi-modal dissemination of derived information to the end users, in the form of interactive maps for intuitive visualization as well as instant notifications via message feeds. As a proof of concept, we also report on our ongoing development of EPOPS; in its current version, this functional prototype of the proposed scheme is able to deliver cross-platform geographic, textual, and even multimedia content through web and smartphone interfaces.

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On map matching of wireless positioning data

Cited by 16 publications

References 12 publications

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

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

NEAT: Road Network Aware Trajectory Clustering

Event Processing and Real-Time Monitoring over Streaming Traffic Data

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