Optimal time-dependent sequenced route queries in road networks

Costa, Camila F.; Nascimento, Mário A.; Macêdo, José Antônio Fernandes de; Theodoridis, Yannis; Pelekis, Nikos; Machado, Javam C.

doi:10.1145/2820783.2820846

Cited by 12 publications

(10 citation statements)

References 20 publications

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“…Chen et al [4] study the multi-rule Partial Sequenced Route (MRPSR) query, which finds the optimal route via a number of POIs in a partial visiting order defined by the user. Costa et al [6] study optimal sequenced route queries in timedependent road networks and propose an effective algorithm called TD-OSR, which is based on the A* scheme with an admissible heuristic function. In this paper, we first modify the TD-OSR to answer our RCOSR query as our baseline.…”

Section: Optimal Route Queriesmentioning

confidence: 99%

“…In this section, we present our baseline algorithm, namely MTDOSR, which extends the TD-OSR algorithm [6] to address the RCOSR problem. The main idea of MTDOSR is to utilize A* scheme with an admissible heuristic function to guide the network expansion.…”

Section: Baseline For Rcosrmentioning

confidence: 99%

“…An idea is to filter the POIs that do not satisfy the rating score threshold to avoid unnecessary expanding exploration. However, the latest optimal sequenced route algorithm TD-OSR [6] cannot be directly applied to this problem, which does not consider the constrained rating score and is not efficient for our problem.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Baseline For Rcosrmentioning

confidence: 99%

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Top k Optimal Sequenced Route Query with POI Preferences

Zhu

Liu

et al. 2022

Data Sci. Eng.

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“…And Ohsawa et al [18] study the OSR query in Euclidean space, and develop the EOSR algorithm based on incremental Euclidean restriction (IER) [19]. Then, Costa et al [7] propose TD-OSR algorithm to find an optimal time-dependent sequenced route (OTDSR) in road network. Yuya Sasaki et al [22] propose the skyline sequenced route (SkySR) query, which is similar to OSR query, the difference is that SkySR query does not strictly obey the passing categories, but adopts the similar categories using a semantic hierarchy.…”

Section: Single User Queriesmentioning

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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“…If the trip route can be optimized by the total travelling time, it is more convenient for the user. Costa et al (2015) proposed a method to optimize the route by the total travelling time, and they also considered changes of traffic conditions. They supposed a condition that the travelling time over an edge was time-dependent in OSR search.…”

Section: Related Workmentioning

confidence: 99%

Safe-Region Generation Methods for Continuous Trip Route Planning Queries

Ohsawa¹,

Htoo²,

Win³

2017

Informatica

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Continuous query is a monitoring query issued by a moving object to keep the query condition satisfied. In the continuous query, the safe-region method is preferable to reduce the load for several requests on the server. A safe-region is a region in which the query result is unchanged, and it is created and sent to the moving object with the query result. The moving object always checks the current position in the region. When it leaves the region, it requests a new result to the server. Safe-region generation methods have been eagerly discussed for simple query types, including kNN, distance range, and RkNN queries. This paper challenges to generate the safe-region for trip route planning queries (TRPQ). This type of query is very time consuming even for snapshot queries, and therefore, there are many restrictions on the safe-region generation methods in existing studies. This paper first investigates the property of the safe-region on TRPQ, and then proposes two types of efficient algorithms, the preceding rival addition (PRA) and the tardy rival addition (TRA) algorithms. The former algorithm runs fast, however, it still requires long processing time when the density of the data object is high. The latter algorithm is very fast independent of the density of data objects, however, the safe-region generated by TRA becomes about 5% larger in the size of generated safe-region. We evaluate the performance through intensive experiments.

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Optimal time-dependent sequenced route queries in road networks

Cited by 12 publications

References 20 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

Safe-Region Generation Methods for Continuous Trip Route Planning Queries

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