OrientSTS-Spatio Temporal Sequence Searching for Trip Planning

Chunjie Zhou, Pengfei Dai and Zhenxing Zhang
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Chunjie Zhou: School of Information and Electrical Engineering, Ludong University, Yantai, China
Pengfei Dai: Yantai Cloudoer Software Co., Ltd., Yantai, China
Zhenxing Zhang: School of Information and Electrical Engineering, Ludong University, Yantai, China

International Journal of Web Services Research (IJWSR), 2018, vol. 15, issue 2, 21-46

Abstract: For a satisfactory trip planning, the following features are desired: 1) automated suggestion of scenes or attractions; 2) personalized based on the interest and habits of travelers; 3) maximal coverage of sites of interest; and 4) minimal effort such as transporting time on the route. Automated scene suggestion requires collecting massive knowledge about scene sites and their characteristics, and personalized planning requires matching of a traveler profile with knowledge of scenes of interest. As a trip contains a sequence of stops at multiple scenes, the problem of trip planning becomes optimizing a temporal sequence where each stop is weighted. This article presents OrientSTS, a novel spatio-temporal sequence (STS) searching system for optimal personalized trip planning. OrientSTS provides a knowledge base of scenes with their tagged features and season characteristics. By combining personal profiles and scene features, OrientSTS generates a set of weighted scenes for each city for each user. OrientSTS can then retrieve the optimal sequence of scenes in terms of distance, weight, visiting time, and scene features. The authors develop alternative algorithms for searching optimal sequences, with consideration of the weight of each scene, the preference of users, and the travel time constraint. The experiments demonstrate the efficiency of the proposed algorithms based on real datasets from social networks.

Date: 2018
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