To Meet or Not to Meet: Finding the Shortest Paths in Road Networks

Finding the shortest path in road networks becomes one of important issues in location based services (LBS). The problem of finding the optimal meeting point for a group of users has also been well studied in existing works. In this paper, we investigate a new problem for two users. Each user has hi...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 30; no. 4; pp. 772 - 785
Main Authors: Weihuang Huang, Yikai Zhang, Zechao Shang, Yu, Jeffrey Xu
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation algorithms
Bridges
Companies
Enumeration
Location based services
meeting requirement
Production planning
Pruning
Roads
Shortest path problem
Shortest paths
Shortest-path problems
Steiner trees
Uncertainty
Weight
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:Finding the shortest path in road networks becomes one of important issues in location based services (LBS). The problem of finding the optimal meeting point for a group of users has also been well studied in existing works. In this paper, we investigate a new problem for two users. Each user has his/her own source and destination. However, whether to meet before going to their destinations is with some uncertainty. We model it as minimum path pair (MPP) query, which consists of two pairs of source and destination and a user-specified weight α to balance the two different needs. The result is a pair of paths connecting the two sources and destinations respectively, with minimal overall cost of the two paths and the shortest route between them. To solve MPP queries, we devise algorithms by enumerating node pairs. We adopt a location-based pruning strategy to reduce the number of node pairs for enumeration. An efficient algorithm based on point-to-point shortest path calculation is proposed to further improve query efficiency. We also give two fast approximate algorithms with approximation bounds. Extensive experiments are conducted to show the effectiveness and efficiency of our methods.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2017.2777851