Path prediction and predictive range querying in road network databases

Jeung, Hoyoung; Yiu, Man Lung; Zhou, Xiaofang; Jensen, Christian S.

doi:10.1007/s00778-010-0181-y

Cited by 139 publications

(81 citation statements)

References 25 publications

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“…However, the short and frequent movements of users identify more important behaviors. Existing works in [16,17,18] consider routine patterns of movement for analyzing user mobility.…”

Section: Modeling User Behaviormentioning

confidence: 99%

An approach to compute user similarity for GPS applications

Mazumdar

Patra

Lock

et al. 2016

Knowledge-Based Systems

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The proliferation of GPS enabled devices has led people to share locations both consciously and unconsciously. Large spatio-temporal data comprising of shared locations and whereabouts are now being routinely collected for analysis. As user movements are generally driven by their interests, so mining these mobility patterns can reveal commonalities between a pair of users. In this paper, we present a framework for mining the published trajectories to identify patterns in user mobility. In this framework, we extract the locations where a user stays for a period of time popularly known as the stay points. These stay points help to identify the interests of a user. The statistics of pattern and check-in distributions over the GPS data are used to formulate similarity measures for finding K -nearest neighbors of an active user. In this work, we categorize the neighbors into three groups namely strongly similar, closely similar and weakly similar. We introduce three similarity measures to determine them, one for each of the categories. We perform experiments on a real-world GPS log data to find the similarity scores between a pair of users and subsequently find the effective K -neighbors. Experimental results show that our proposed metric outperforms existing metrics in literature.

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“…However, the short and frequent movements of users identify more important behaviors. Existing works in [16,17,18] consider routine patterns of movement for analyzing user mobility.…”

Section: Modeling User Behaviormentioning

confidence: 99%

An approach to compute user similarity for GPS applications

Mazumdar

Patra

Lock

et al. 2016

Knowledge-Based Systems

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“…The other is a greedy algorithm [61] that uses maximum likelihood to project a vehicle's long-term path with a greedy algorithm to predict the next road segment. These methods are outside mobile IP research, but are chosen so that the proposed method is compared with more recent vehicle prediction methods.…”

Section: Prediction Analysismentioning

confidence: 99%

Predictive mobile IP handover for vehicular networks

Magnano

Xin

Boukerche

2015

2015 IEEE 40th Conference on Local Computer Networks (LCN)

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Vehicular networks are an emerging technology that offer potential for providing a variety of new services. However, extending vehicular networks to include IP connections is still problematic, due in part to the incompatibility of mobile IP handovers with the increased mobility of vehicles. The handover process, consisting of discovery, registration, and packet forwarding, has a large overhead and disrupts connectivity. With increased handover frequency and smaller access point dwell times in vehicular networks, the handover causes a large degradation in performance. This thesis proposes a predictive handover solution, us-

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“…The trajectories considered in distanttime query [5] are high-sampling-rate trajectories that reveal detailed movements of users. Same as in [5], [8], [6], the next location prediction is based on the high-sampling-rate trajectories. As users could easilyl perform check-in services (e.g., Foursquare) to note their locations with a mobile phone, low-sampling-rate trajectories becomes ubiquitous.…”

Section: Introductionmentioning

confidence: 99%

“…Prior works assume that recent locations and velocities are available and employ a (non)linear movement model to determine near-future location mainly based on recent location and velocities. On the other hand, more complex location prediction models have also been studied in [5], [8], [6] to support distant-time queries. We mention in passing that the authors in [8] proposed a location prediction model, which infers next location of a user based on collective frequent patterns discovered from previous trajectories of all users.…”

Section: Introductionmentioning

confidence: 99%

“…We mention in passing that the authors in [8] proposed a location prediction model, which infers next location of a user based on collective frequent patterns discovered from previous trajectories of all users. In [6], a path prediction model is proposed, which predicts the travel path of an object up to a specified time interval in the future. A hybrid prediction model (HPM) is proposed in [5], which is able to answer distanttime future locations.…”

Section: Introductionmentioning

confidence: 99%

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Distant-Time Location Prediction in Low-Sampling-Rate Trajectories

Chiang

Zhu

Peng

et al. 2013

2013 IEEE 14th International Conference on Mobile Data Management

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Abstract-With the growth of location-based services and social services, low-sampling-rate trajectories from check-in data or photos with geo-tag information becomes ubiquitous. In general, most detailed moving information in low-sampling-rate trajectories are lost. Prior works have elaborated on distant-time location prediction in high-sampling-rate trajectories. However, existing prediction models are pattern-based and thus not applicable due to the sparsity of data points in low-sampling-rate trajectories. For example, it becomes difficult to derive trajectory patterns, let alone utilizing trajectory patterns for distant-time location prediction. In this paper, given a query time, the current location and time, we aim to predict the location of an object at the query time. To address the sparsity in low-sampling-rate trajectories, we develop a Reachability-based prediction model on Time-constrained Mobility Graph (abbreviated as RTMG) to predict locations for distant-time queries. Specifically, we design an adaptive temporal exploration approach to extract effective supporting trajectories that are temporally close to the query time. These data points are then represented as a Timeconstrained user mobility Graph (refers to as TG). In light of TG, we further derive the reachability probabilities among locations in TG. Thus, a location with maximum reachability from the current location among all possible locations in supporting trajectories is considered as the prediction result. To efficiently process queries, we proposed an index structure SOIT to organize location records for on-line query processing. We conduct extensive experiments on real low-sampling-rate datasets and demonstrate the effectiveness and efficiency of RTMG.

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Path prediction and predictive range querying in road network databases

Cited by 139 publications

References 25 publications

An approach to compute user similarity for GPS applications

An approach to compute user similarity for GPS applications

Predictive mobile IP handover for vehicular networks

Distant-Time Location Prediction in Low-Sampling-Rate Trajectories

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