A Route Recommendation System for Sightseeing with Network Optimization and Conditional Probability

Takashi Hasuike; Hideki Katagiri; Hiroe Tsubaki; Hiroshi Tsuda

doi:10.1109/SMC.2015.467

A Route Recommendation System for Sightseeing with Network Optimization and Conditional Probability

Takashi Hasuike, Hideki Katagiri, Hiroe Tsubaki, Hiroshi Tsuda

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

This paper proposes a route recommendation system for sightseeing based on a network optimization problem. Traveling and sightseeing times are randomly changed dependent on current traffic and congestion conditions, and hence, Time-Expanded Network (TEN) to contain a copy to the set of nodes in the underlying static network for each discrete time step is introduced. In addition, in order to select the next sightseeing site, conditional probabilities are introduced, which are calculated by current conditions, statistical and Web data. Our proposed model is formulated as a nonlinear and discrete optimization problem, and it is hard to solve it directly and efficiently. Therefore, an efficient algorithm is also developed based on dynamic programming and transformation of the main problem into the recursive equation.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2672-2677
Number of pages	6
ISBN (Print)	9781479986965
DOIs	https://doi.org/10.1109/SMC.2015.467
Publication status	Published - 2016 Jan 12
Event	IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong Duration: 2015 Oct 9 → 2015 Oct 12

Other

Other	IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Country/Territory	Hong Kong
City	Kowloon Tong
Period	15/10/9 → 15/10/12

Keywords

conditional probabliy
Dynamic programming
mathematical programming
sightseeing route recommendation
Time-Expanded Network (TEN)

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Energy Engineering and Power Technology
Information Systems and Management
Control and Systems Engineering

Access to Document

10.1109/SMC.2015.467

Cite this

Hasuike, T., Katagiri, H., Tsubaki, H., & Tsuda, H. (2016). A Route Recommendation System for Sightseeing with Network Optimization and Conditional Probability. In Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 (pp. 2672-2677). Article 7379599 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2015.467

A Route Recommendation System for Sightseeing with Network Optimization and Conditional Probability. / Hasuike, Takashi; Katagiri, Hideki; Tsubaki, Hiroe et al.
Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2672-2677 7379599.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hasuike, T, Katagiri, H, Tsubaki, H & Tsuda, H 2016, A Route Recommendation System for Sightseeing with Network Optimization and Conditional Probability. in Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015., 7379599, Institute of Electrical and Electronics Engineers Inc., pp. 2672-2677, IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, Kowloon Tong, Hong Kong, 15/10/9. https://doi.org/10.1109/SMC.2015.467

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abstract = "This paper proposes a route recommendation system for sightseeing based on a network optimization problem. Traveling and sightseeing times are randomly changed dependent on current traffic and congestion conditions, and hence, Time-Expanded Network (TEN) to contain a copy to the set of nodes in the underlying static network for each discrete time step is introduced. In addition, in order to select the next sightseeing site, conditional probabilities are introduced, which are calculated by current conditions, statistical and Web data. Our proposed model is formulated as a nonlinear and discrete optimization problem, and it is hard to solve it directly and efficiently. Therefore, an efficient algorithm is also developed based on dynamic programming and transformation of the main problem into the recursive equation.",

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AB - This paper proposes a route recommendation system for sightseeing based on a network optimization problem. Traveling and sightseeing times are randomly changed dependent on current traffic and congestion conditions, and hence, Time-Expanded Network (TEN) to contain a copy to the set of nodes in the underlying static network for each discrete time step is introduced. In addition, in order to select the next sightseeing site, conditional probabilities are introduced, which are calculated by current conditions, statistical and Web data. Our proposed model is formulated as a nonlinear and discrete optimization problem, and it is hard to solve it directly and efficiently. Therefore, an efficient algorithm is also developed based on dynamic programming and transformation of the main problem into the recursive equation.

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A Route Recommendation System for Sightseeing with Network Optimization and Conditional Probability

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Keywords

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