On efficiently searching trajectories and archival data for historical similarities

Sherkat, Reza; Rafiei, Davood

doi:10.14778/1453856.1453953

Cited by 19 publications

(9 citation statements)

References 40 publications

(73 reference statements)

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“…Other related work includes path planning by considering traffic uncertainty [17], searching similar trajectories [18], [19], [20], [21], [22], shortest path [6], [23], shortest path on time-dependent networks [24], finding the fastest path by speed patterns [25], etc. Nevertheless, all the work above is not able to address the problem of capturing and deriving the popularity of a route between two given locations.…”

Section: Related Workmentioning

confidence: 99%

Discovering popular routes from trajectories

Chen

Shen

Zhou

2011

2011 IEEE 27th International Conference on Data Engineering

297

203

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Abstract-The booming industry of location-based services has accumulated a huge collection of users' location trajectories of driving, cycling, hiking, etc. In this work, we investigate the problem of discovering the Most Popular Route (MPR) between two locations by observing the traveling behaviors of many previous users. This new query is beneficial to travelers who are asking directions or planning a trip in an unfamiliar city/area, as historical traveling experiences can reveal how people usually choose routes between locations.To achieve this goal, we firstly develop a Coherence Expanding algorithm to retrieve a transfer network from raw trajectories, for indicating all the possible movements between locations. After that, the Absorbing Markov Chain model is applied to derive a reasonable transfer probability for each transfer node in the network, which is subsequently used as the popularity indicator in the search phase. Finally, we propose a Maximum Probability Product algorithm to discover the MPR from a transfer network based on the popularity indicators in a breadth-first manner, and we illustrate the results and performance of the algorithm by extensive experiments.

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Section: Related Workmentioning

confidence: 99%

Discovering popular routes from trajectories

Chen

Shen

Zhou

2011

2011 IEEE 27th International Conference on Data Engineering

297

203

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“…Some representative works are: Chen et al propose the edit distance [9]; Sherkat et al develop a uDAE-based MBR approximation approach [13]. Those methods define the similarity functions on the shape of trajectories, but they do not consider the spatial properties.…”

Section: Discussionmentioning

confidence: 99%

“…Most traditional k-NN query processing methods are designed to find point objects [11,6,5]. On the other hand, the traditional trajectory search techniques focus on retrieving the results with similar shapes to a sample trajectory [9,13]. The new problem, searching top-k trajectories given a set of geospatial locations, poses the following challenges:…”

Section: Introductionmentioning

confidence: 99%

Retrieving k-Nearest Neighboring Trajectories by a Set of Point Locations

Tang

Zheng

Xie

et al. 2011

Advances in Spatial and Temporal Databases

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Abstr act. The advance of object tracking technologies leads to huge volumes of spatio-temporal data accumulated in the form of location trajectories. Such data bring us new opportunities and challenges in efficient trajectory retrieval.In this paper, we study a new type of query that finds the k Nearest Neighboring Trajectories (k-NNT) with the minimum aggregated distance to a set of query points. Such queries, though have a broad range of applications like trip planning and moving object study, cannot be handled by traditional k-NN query processing techniques that only find the neighboring points of an object. To facilitate scalable, flexible and effective query execution, we propose a k-NN trajectory retrieval algorithm using a candidate-generation-and-verification strategy. The algorithm utilizes a data structure called global heap to retrieve candidate trajectories near each individual query point. Then, at the verification step, it refines these trajectory candidates by a lower-bound computed based on the global heap. The global heap guarantees the candidate's completeness (i.e., all the k-NNTs are included), and reduces the computational overhead of candidate verification. In addition, we propose a qualifier expectation measure that ranks partial-matching candidate trajectories to accelerate query processing in the cases of non-uniform trajectory distributions or outlier query locations. Extensive experiments on both real and synthetic trajectory datasets demonstrate the feasibility and effectiveness of proposed methods.

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“…If there exists a query source sj ∈ {Oq ∪ Kq} and labelST (sj) > labelST (si), sj will replace si as the new expansion center and the expansion search from si terminates. When a trajectory is scanned by all the query sources, the value of ST dist (q, τ ) can be calculated and UB need to be updated (line [28][29][30][31] …”

Section: Heuristic Trajectory Searchmentioning

confidence: 99%

User oriented trajectory search for trip recommendation

Shang

Ding

Yuan

et al. 2012

Proceedings of the 15th International Conference on Extending Database Technology

146

110

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Trajectory sharing and searching have received significant attentions in recent years. In this paper, we propose and investigate a novel problem called User Oriented Trajectory Search (UOTS) for trip recommendation. In contrast to conventional trajectory search by locations (spatial domain only), we consider both spatial and textual domains in the new UOTS query. Given a trajectory data set, the query input contains a set of intended places given by the traveler and a set of textual attributes describing the traveler's preference. If a trajectory is connecting/close to the specified query locations, and the textual attributes of the trajectory are similar to the traveler'e preference, it will be recommended to the traveler for reference. This type of queries can bring significant benefits to travelers in many popular applications such as trip planning and recommendation.There are two challenges in the UOTS problem, (i) how to constrain the searching range in two domains and (ii) how to schedule multiple query sources effectively. To overcome the challenges and answer the UOTS query efficiently, a novel collaborative searching approach is developed. Conceptually, the UOTS query processing is conducted in the spatial and textual domains alternately. A pair of upper and lower bounds are devised to constrain the searching range in two domains. In the meantime, a heuristic searching strategy based on priority ranking is adopted for scheduling the multiple query sources, which can further reduce the searching range and enhance the query efficiency notably. Furthermore, the devised collaborative searching approach can be extended to situations where the query locations are ordered. The performance of the proposed UOTS query is verified by extensive experiments based on real and synthetic trajectory data in road networks.

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On efficiently searching trajectories and archival data for historical similarities

Cited by 19 publications

References 40 publications

Discovering popular routes from trajectories

Discovering popular routes from trajectories

Retrieving k-Nearest Neighboring Trajectories by a Set of Point Locations

User oriented trajectory search for trip recommendation

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