TMN-tree: New Trajectory Index Structure for Moving Objects in Spatial Networks

Chang, Jae-Woo; Song, Myoung-Seon; Um, Jung-Ho

doi:10.1109/cit.2010.289

Cited by 13 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MON-Tree [11] can be seen as an improvement over the FNR-Tree, saving considerable space by indexing MBRs of larger network elements (edge segments or entire roads) and reducing the number of disk accesses at query time. Both indexes are outperformed by the TMN-Tree [27] in query time, which indexes whole trajectories of moving objects with a 2D R * -Tree and indexing the temporal component with a B + -Tree, which proves to be more efficient for that application than the R-Tree.…”

Section: Trajectory Indexingmentioning

confidence: 99%

A compact representation for trips over networks built on self-indexes

Brisaboa

Fariña

Galaktionov

et al. 2018

Information Systems

View full text Add to dashboard Cite

Representing the movements of objects (trips) over a network in a compact way while retaining the capability of exploiting such data effectively is an important challenge of real applications. We present a new Compact Trip Representation (CTR) that handles the spatio-temporal data associated with users' trips over transportation networks. Depending on the network and types of queries, nodes in the network can represent intersections, stops, or even street segments.CTR represents separately sequences of nodes and the time instants when users traverse these nodes. The spatial component is handled with a data structure based on the well-known Compressed Suffix Array (CSA), which provides both a compact representation and interesting indexing capabilities. The temporal component is self-indexed with either a Hu-Tucker-shaped Wavelet-tree or a Wavelet Matrix that solve range-interval queries efficiently. We show how CTR can solve relevant counting-based spatial, temporal, and spatio-temporal queries over large sets of trips. Experimental results show the space requirements (around 50-70% of the space needed by a compact non-indexed baseline) and query efficiency (most queries are solved in the range of 1-1000 microseconds) of CTR.

show abstract

Section: Trajectory Indexingmentioning

confidence: 99%

A compact representation for trips over networks built on self-indexes

Brisaboa

Fariña

Galaktionov

et al. 2018

Information Systems

View full text Add to dashboard Cite

show abstract

“…Since the rapidly increased satellites and GPS (global position system) technologies have developed, it is possible to collect a large amount of trajectory data of moving objects such as the vehicle position data, hurricane track data, and animal movement data [1][2][3][4]. The analysis over these trajectory data is becoming important for many applications, such as meteorological observation and forecast, animal habits observation, road traffic situation analysis, and navigation in transportations [5][6][7][8][9]. According to the recorded trajectory data and road networks, the moving pattern, traffic situation, and road recommendation services can be supported [1,2,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the existing schemes try to monitor and forecast the traffic by using the recorded history trajectory data of vehicles equipped with GPS devices. The index based schemes construct an index by adopting the trajectory data of the vehicles [5,6]. And then the routes are recommended according to the history trajectory data of the related vehicles.…”

Section: Introductionmentioning

confidence: 99%

Discovering Congested Routes Using Vehicle Trajectories in Road Networks

Bok

Lim

et al. 2015

Advances in Multimedia

View full text Add to dashboard Cite

The popular route recommendation and traffic monitoring over the road networks have become important in the location-based services. The schemes to find out the congested routes were proposed by considering the number of vehicles in a road segment. However, the existing schemes do not consider the features of each road segment such as width, length, and direction in a road network. Furthermore, the existing schemes fail to consider the average moving speed of vehicles. Therefore, they can detect the incorrect density routes. To overcome such problems, we propose a new discovering scheme of congested routes through the analysis of vehicle trajectories in a road network. The proposed scheme divides each road into segments with different width and length in a road network. And then, the congested road segment is detected through the saturation degree of the road segment and the average moving speed of vehicles in the road segment. Finally, we compute the final congested routes by using a clustering scheme. The experimental results have shown that the proposed scheme can efficiently discover the congested routes in the different directions of the roads.

show abstract

“…Since the rapidly increased satellites and GPS (Global Position System) technologies have developed, it is possible to collect a large amount of trajectory data of moving objects such as the vehicle position data, hurricane track data, and animal movement data [1,2,16,17]. The analysis over these trajectory data is becoming important for many applications, such as meteorological observation and forecast, animal habits observation, road traffic situation analysis, and navigation in transportations [3][4][5][6]8]. According to the recorded trajectory data and road networks, the moving pattern, traffic situation and road recommendation services can be supported [1,2,12,15,18].…”

Section: Introductionmentioning

confidence: 99%

“…The index based schemes construct an index by adopting the trajectory data of the moving objects [3,4]. And then, the routes are recommended according to the history trajectory data of the related moving objects.…”

Section: Introductionmentioning

confidence: 99%

A Clustering Scheme for Discovering Congested Routes on Road Networks

He¹,

Bok²,

Lim³

et al. 2015

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don't consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.

show abstract

TMN-tree: New Trajectory Index Structure for Moving Objects in Spatial Networks

Cited by 13 publications

References 6 publications

A compact representation for trips over networks built on self-indexes

A compact representation for trips over networks built on self-indexes

Discovering Congested Routes Using Vehicle Trajectories in Road Networks

A Clustering Scheme for Discovering Congested Routes on Road Networks

Contact Info

Product

Resources

About