Efficient Computation of Group Optimal Sequenced Routes in Road Networks

Samrose, Samiha; Hashem, Tanzima; Barua, Suman; Ali, Mohammed Eunus; Uddin, Mohammad Hafiz; Mahmud, Md. Iftekhar

doi:10.1109/mdm.2015.68

Cited by 20 publications

(6 citation statements)

References 15 publications

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“…In addition, Ohsawa et al [17] study the OSR query in Euclidean space and develop the EOSR algorithm based on Incremental Euclidean Restriction (IER) [2]. Furthermore, some works [1,5,[9][10][11]19] study the optimal sequenced route query among a group of users.…”

Section: Optimal Route Queriesmentioning

confidence: 99%

Top k Optimal Sequenced Route Query with POI Preferences

Zhu

Liu

et al. 2022

Data Sci. Eng.

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The optimal sequenced route (OSR) query, as a popular problem in route planning for smart cities, searches for a minimum-distance route passing through several POIs in a specific order from a starting position. In reality, POIs are usually rated, which helps users in making decisions. Existing OSR queries neglect the fact that the POIs in the same category could have different scores, which may affect users’ route choices. In this paper, we study a novel variant of OSR query, namely Rating Constrained Optimal Sequenced Route query (RCOSR), in which the rating score of each POI in the optimal sequenced route should exceed the query threshold. To efficiently process RCOSR queries, we first extend the existing TD-OSR algorithm to propose a baseline method, called MTDOSR. To tackle the shortcomings of MTDOSR, we try to design a new RCOSR algorithm, namely Optimal Subroute Expansion (OSE) Algorithm. To enhance the OSE algorithm, we propose a Reference Node Inverted Index (RNII) to accelerate the distance computation of POI pairs in OSE and quickly retrieve the POIs of each category. To make full use of the OSE and RNII, we further propose a new efficient RCOSR algorithm, called Recurrent Optimal Subroute Expansion (ROSE), which recurrently utilizes OSE to compute the current optimal route as the guiding path and update the distance of POI pairs to guide the expansion. Then, we extend our techniques to handle a variation of RCOSR query, namely RCkOSR query. The experimental results demonstrate that the proposed algorithm significantly outperforms the existing approaches.

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Section: Optimal Route Queriesmentioning

confidence: 99%

Top k Optimal Sequenced Route Query with POI Preferences

Zhu

Liu

et al. 2022

Data Sci. Eng.

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“…Ahmadi et al [2] study a variant of GTP queries, namely SGTP where the visiting POIs are predefined by users in road networks. At the same time, works [2,21] study group optimal sequenced route (GOSR) queries, which are inherently the same as SGTP queries. They employ the elliptical properties as pruning strategies to process the query.…”

Section: Different Destinationsmentioning

confidence: 99%

Querying Optimal Routes for Group Meetup

et al. 2021

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Motivated by location-based social networks which allow people to access location-based services as a group, we study a novel variant of optimal sequenced route (OSR) queries, optimal sequenced route for group meetup (OSR-G) queries. OSR-G query aims to find the optimal meeting POI (point of interest) such that the maximum users’ route distance to the meeting POI is minimized after each user visits a number of POIs of specific categories (e.g., gas stations, restaurants, and shopping malls) in a particular order. To process OSR-G queries, we first propose an OSR-Based (OSRB) algorithm as our baseline, which examines every POI in the meeting category and utilizes existing OSR (called E-OSR) algorithm to compute the optimal route for each user to the meeting POI. To address the shortcomings (i.e., requiring to examine every POI in the meeting category) of OSRB, we propose an upper bound based filtering algorithm, called circle filtering (CF) algorithm, which exploits the circle property to filter the unpromising meeting POIs. In addition, we propose a lower bound based pruning (LBP) algorithm, namely LBP-SP which exploits a shortest path lower bound to prune the unqualified meeting POIs to reduce the search space. Furthermore, we develop an approximate algorithm, namely APS, to accelerate OSR-G queries with a good approximation ratio. Finally the experimental results show that both CF and LBP-SP outperform the OSRB algorithm and have high pruning rates. Moreover, the proposed approximate algorithm runs faster than the exact OSR-G algorithms and has a good approximation ratio.

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“…have been studied by the researchers from spatio-temporal data management community [16,17,58,64,68]. More recently, the variants of group patterns and sequence routes have been explored [59,61], along with extensions that include hierarchical semantic similarity of the points of stopping/visiting [62]. However, these works are orthogonal to the problem(s) that we pursue: namely, they are focused on efficient query processing from given datasets, whereas we are focusing on the problem of learning about the relationships in sequentiality of POIs visits from users' check-ins.…”

Section: Rnn-based Trajectory Modelingmentioning

confidence: 99%

Semi-supervised Trajectory Understanding with POI Attention for End-to-End Trip Recommendation

Zhang

Trajcevski

et al. 2020

ACM Trans. Spatial Algorithms Syst.

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Trip planning/recommendation is an important task for a plethora of applications in urban settings (e.g., tourism, transportation, social outings), relying on services provided by Location-Based Social Networks (LBSN). To provide greater context-awareness in trajectory planning, LBSNs combine historical trajectories of users for generating various hand-crafted features—e.g., geo-tags of photos taken by tourists and textual characteristics derived from reviews. Those features are used to learn tourists’ preferences, which are then used to generate a travel plan recommendation. However, many such features are extracted based on prior knowledge or empirical analysis specific to particular datasets, rendering the corresponding solutions not to be generalizable to diverse data sources. Thus, one important question for managing mobility is how to learn an accurate tour planning model based solely on POI visits or user check-ins and without the efforts of hand-crafted feature engineering. Inspired by recent successes of deep learning in sequence learning, we develop a solution to the tour planning problem based on the semi-supervised learning paradigm. An important aspect of our solution is that it does not involve any feature engineering. Specifically, we propose the Trip Recommendation method via trajectory Encoder and Decoder—a novel end-to-end approach encoding historical trajectories into vectors, while capturing both the intrinsic characteristics of individual POIs and the transition patterns among POIs. We also incorporate historical attention mechanism in our sequence-to-sequence trip recommendation task to improve the effectiveness. Experiments conducted on multiple publicly available LBSN datasets demonstrate significantly superior performance of our method.

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Efficient Computation of Group Optimal Sequenced Routes in Road Networks

Cited by 20 publications

References 15 publications

Top k Optimal Sequenced Route Query with POI Preferences

Top k Optimal Sequenced Route Query with POI Preferences

Querying Optimal Routes for Group Meetup

Semi-supervised Trajectory Understanding with POI Attention for End-to-End Trip Recommendation

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