Browsing by Author "Lu, Eric Hsueh-Chan"

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An Efficient Framework for Multirequest Route Planning in Urban Environments
(2017-04-01)
Lu, Eric Hsueh-Chan
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Chen, Huan-Sheng
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Tseng, Vincent S.
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資訊工程學系
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Department of Computer Science
In recent years, research on location-based services has received a lot of interest, in both industry and academia, due to a wide range of potential applications. Among them, one of the active topic areas is the constraint-based route planning on a point-of-interest (POI) network. Most of the previous studies on this topic primarily consider the geographic properties of the POIs in planning a route. However, we consider that the reason that a user visits a POI is that it provides some services that the user needs. In particular, in urban environments, a POI may provide various kinds of services. Hence, the user's requests should be considered. In this paper, we address a novel problem, which is called multirequest route planning, and propose a novel framework to efficiently plan a route for serving multiple user-specified requests. The framework consists of two major modules: planning module, in which four approaches with pruning and caching strategies are proposed for planning a preliminary route, and refinementmodule, in which two refinement mechanisms are proposed for further enhancing the quality of the route. To our best knowledge, this is the first work on route planning that considers multiple services provided by a POI and multiple requests specified by a user, simultaneously. Finally, we perform an extensive experimental evaluation based on three real-world POI data sets and deliver excellent performance.
Integrating tourist packages and tourist attractions for personalized trip planning based on travel constraints
(2016-10)
Lu, Eric Hsueh-Chan
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Fang, Shih-Hsin
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Tseng, Vincent S.
;
資訊工程學系
;
Department of Computer Science
In recent years, with the upgrading of mobile positioning and the popularity of smart devices, location related research gets a lot of attentions. One of popular issues is the trip planning problem. Although many related scientific or technical literature have been proposed, most of them focused only on tourist attraction recommendation or arrangement meeting some user demands. In fact, to grasp the huge tourism opportunities, more and more tour operators design tourist packages and provide to users. Generally, tourist packages have many advantages such as cheaper ticket price and higher transportation convenience. However, researches on trip planning combining tourist packages have not been mentioned in the past studies. In this research, we present a new approach named Package-Attraction-based Trip Planner (PAT-Planner) to simultaneously combine tourist packages and tourist attractions for personalized trip planning satisfying users\' travel constraints. In PAT-Planner, we first based on user preferences and temporal characteristics to design a Score Inference Model for respectively measuring the score of a tourist package or tourist attraction. Then, we develop the Hybrid Trip-Mine algorithm meeting user travel constraints for personalized trip planning. Besides, we further propose two improvement strategies, namely Score Estimation and Score Bound Tightening, based on Hybrid Trip-Mine to speed up the trip planning efficiency. As far as we know, our study is the first attempt to simultaneously combine tourist packages and tourist attractions on trip planning problem. Through a series of experimental evaluations and case studies using the collected Gowalla datasets, PAT-Planner demonstrates excellent planning effects.