Journal of Information Processing
Online ISSN : 1882-6652
ISSN-L : 1882-6652
Roadscape-based Route Recommender System Using Coarse-to-fine Route Search
Koji KawamataKenta Oku
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2019 Volume 27 Pages 392-403


We propose Roadscape-based Route Recommender System (R3), which provides diversified roadscape-based routes. Given starting and destination points, R3 provides four types of roadscape-based routes: rural-, mountainous-, waterside-, and urban-prior routes. To reduce the computational cost, we propose a coarse-to-fine route search approach that consists of a roadscape-based clustering method, roadscape cluster graph, coarse-grained route search, and fine-grained route search. We evaluated the performance of R3 using network data for real roads. The experimental results qualitatively show the validity of the generated roadscape clusters by comparing them with Google satellite maps and Google Street View images. The results also show the validity of the roadscape-based route recommendations. Furthermore, the results show that using a coarse-grained route search can significantly reduce the route search time. Finally, we quantitatively evaluate R3 from the perspective of users. The results show that R3 can appropriately recommend roadscape-based routes for given scenarios.

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© 2019 by the Information Processing Society of Japan
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