Collective Travel Planning in Spatial Networks

Shang, Shuo; Chen, Lisi; Wei, Zhewei; Jensen, Christian S.; Wen, Ji-Rong; Kalnis, Panos

doi:10.1109/tkde.2015.2509998

Cited by 96 publications

(33 citation statements)

References 22 publications

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“…Notice that the modeling of spatial networks and trajectories align with previous studies [14,15,17,18].…”

Section: Definition: Trajectorysupporting

confidence: 80%

“…If two nodes n and n have the same parent node and their child nodes are not in H, we merge the results for n, n , and their parent node (e.g., n3, n4, and n14 in Figure 2) and store the qualified trajectory pairs in n.parent. Next, we add n.parent to H, and remove n and n from H (lines [12][13][14][15][16][17][18]. If H = 1, the root node is reached, and all qualified trajectory pairs are returned (lines 19-20).…”

mentioning

confidence: 99%

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Trajectory similarity join in spatial networks

Chen²,

et al. 2017

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The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider the case of trajectory similarity join (TS-Join), where the objects are trajectories of vehicles moving in road networks. Thus, given two sets of trajectories and a threshold θ, the TS-Join returns all pairs of trajectories from the two sets with similarity above θ. This join targets applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction.With these applications in mind, we provide a purposeful definition of similarity. To enable efficient TS-Join processing on large sets of trajectories, we develop search space pruning techniques and take into account the parallel processing capabilities of modern processors. Specifically, we present a two-phase divideand-conquer algorithm. For each trajectory, the algorithm first finds similar trajectories. Then it merges the results to achieve a final result. The algorithm exploits an upper bound on the spatiotemporal similarity and a heuristic scheduling strategy for search space pruning. The algorithm's per-trajectory searches are independent of each other and can be performed in parallel, and the merging has constant cost. An empirical study with real data offers insight in the performance of the algorithm and demonstrates that is capable of outperforming a well-designed baseline algorithm by an order of magnitude.

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“…Notice that the modeling of spatial networks and trajectories align with previous studies [14,15,17,18].…”

Section: Definition: Trajectorysupporting

confidence: 80%

mentioning

confidence: 99%

Trajectory similarity join in spatial networks

Chen²,

et al. 2017

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“…Shang et al [31] propose and study a novel problem for dynamically monitoring the shortest path in spatial network, with the aim of accelerating the shortest path computation in a dynamic spatial network. Shang et al [32] design an exact algorithm and an approximation algorithm to solve Collective Travel Planning query problem. The query finds the lowest cost route connecting multiple sources and a destination with up to meeting points.…”

Section: Related Workmentioning

confidence: 99%

Finding the Shortest Path with Vertex Constraint over Large Graphs

Yang

Wang

et al. 2019

Complexity

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Graph is an important complex network model to describe the relationship among various entities in real applications, including knowledge graph, social network, and traffic network. Shortest path query is an important problem over graphs and has been well studied. This paper studies a special case of the shortest path problem to find the shortest path passing through a set of vertices specified by user, which is NP-hard. Most existing methods calculate all permutations for given vertices and then find the shortest one from these permutations. However, the computational cost is extremely expensive when the size of graph or given set of vertices is large. In this paper, we first propose a novel exact heuristic algorithm in best-first search way and then give two optimizing techniques to improve efficiency. Moreover, we propose an approximate heuristic algorithm in polynomial time for this problem over large graphs. We prove the ratio bound is 3 for our approximate algorithm. We confirm the efficiency of our algorithms by extensive experiments on real-life datasets. The experimental results validate that our algorithms always outperform the existing methods even though the size of graph or given set of vertices is large.

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“…Second, it is of interest to use POI data and geo-tagged social media data to discover significant locations/regions [13,41,[45][46][47][48]52,60]. Third, it is of interest to study integrate text data to routing problem [35,36,40,[42][43][44]56,59,66] and to study spatio-textual routing problem. Fourth, it is of interest to consider streaming data sampling and analysis, and other query types [7-9, 16, 17, 21, 23, 25, 53, 54, 57, 58, 61].…”

Section: Related Workmentioning

confidence: 99%

Approximate spatio-temporal top-k publish/subscribe

Chen

Shang

2018

World Wide Web

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Location-based publish/subscribe plays a significant role in mobile information disseminations. In this light, we propose and study a novel problem of processing location-based top-k subscriptions over spatio-temporal data streams. We define a new type of approximate location-based top-k subscription, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription, that continuously feeds users with relevant spatio-temporal messages by considering textual similarity, spatial proximity, and information freshness. Different from existing location-based top-k subscriptions, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription can automatically adjust the triggering condition by taking the triggering score of other subscriptions into account. The group filtering efficacy can be substantially improved by sacrificing the publishing result quality with a bounded guarantee. We conduct extensive experiments on two real datasets to demonstrate the performance of the developed solutions.

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Collective Travel Planning in Spatial Networks

Cited by 96 publications

References 22 publications

Trajectory similarity join in spatial networks

Trajectory similarity join in spatial networks

Finding the Shortest Path with Vertex Constraint over Large Graphs

Approximate spatio-temporal top-k publish/subscribe

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