Supporting Pattern-Matching Queries over Trajectories on Road Networks

Roh, Gook-Pil; Roh, Jong-Won; Hwang, Seung-won; Yi, Byoung-Kee

doi:10.1109/tkde.2010.189

Cited by 24 publications

(14 citation statements)

References 21 publications

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“…Lee et al [20] proposed to use a frequent sequential pattern for trajectory classification. Roh et al [21] proposed a system for querying patterns in trajectory. Traffic condition estimation was studied in [11], [22], and [23].…”

Section: Related Work and Our Approachmentioning

confidence: 99%

Intelligent Carpool Routing for Urban Ridesharing by Mining GPS Trajectories

He¹,

Hwang

2014

IEEE Trans. Intell. Transport. Syst.

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To support an efficient carpooling service in heavy urban traffic, we propose an intelligent routing scheme based on mining Global Position System trajectories from shared riders. The carpooling system provides many-to-many services with multiple pickup and dropping points. To join a daily carpooling group, the riders must accept a compromised route that is efficient after merging the routes that are preferred by all qualified riders. We developed three frequency-correlated algorithms for route mining, rider selection, and route merging in an urban carpool service. Our approach can cope with the traffic dynamics to yield a suboptimal shared route. Our scheme was successfully tested under heavy Beijing traffic over hundreds of riders. We developed performance metrics to measure the service cost and mileage saved. The ultimate goal is to minimize the riding distances and the transportation costs, and thus alleviate urban traffic jams.Index Terms-Carpooling service, Global Positioning System (GPS) trajectory mining, intelligent route merging, urban traffic management.

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Section: Related Work and Our Approachmentioning

confidence: 99%

Intelligent Carpool Routing for Urban Ridesharing by Mining GPS Trajectories

He¹,

Hwang

2014

IEEE Trans. Intell. Transport. Syst.

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“…For example, there are works detecting regionof-interest from paths of moving objects [2], [12], and finding frequent patterns [3] by defining popular points among trajectories. There are also works considering partial trajectory patterns on traffic networks in classification [13] and pattern matching [14]. These works take only part of the trajectory data into analyzing.…”

Section: Related Workmentioning

confidence: 99%

Profiling Moving Objects by Dividing and Clustering Trajectories Spatiotemporally

Yeh

Chen

2013

IEEE Trans. Knowl. Data Eng.

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An object can move with various speeds and arbitrarily changing directions. Given a bounded area where a set of objects moving around, there are some typical moving styles of the objects at different local regions due to the geography nature or other spatiotemporal conditions. Not only the paths that the objects move along, we also want to know how different groups of objects move with various speeds. Therefore, given a set of collected trajectories spreading in a bounded area, we are interested in discovering the typical moving styles in different regions of all the monitored moving objects. These regional typical moving styles are regarded as the profile of the monitored moving objects, which may help reflect the geoinformation of the observed area and the moving behaviors of the observed moving objects. In this paper, we present DivCluST, an approach to finding regional typical moving styles by dividing and clustering the trajectories in consideration of both the spatial and temporal constraints. Different from the existing works that consider only the spatial properties or just the interesting regions of trajectories, DivCluST focuses more on typical movements in local regions of a bounded area and takes the temporal information into account when designing the criteria for trajectory dividing and the distance measurement for adaptive k-means clustering. Extensive experiments on three types of real data sets with specially designed visualization are presented to show the effectiveness of DivCluST.

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“…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%

“…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]. Recently, with the continuously increasing mobile devices and vehicles, the route recommendation service is becoming more and more important [1,5,6,9,17,19,20]. For road network based applications, the mobility of the vehicle is road network constrained.…”

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

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-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.

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Supporting Pattern-Matching Queries over Trajectories on Road Networks

Cited by 24 publications

References 21 publications

Intelligent Carpool Routing for Urban Ridesharing by Mining GPS Trajectories

Intelligent Carpool Routing for Urban Ridesharing by Mining GPS Trajectories

Profiling Moving Objects by Dividing and Clustering Trajectories Spatiotemporally

A Clustering Scheme for Discovering Congested Routes on Road Networks

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