Multi-day and multi-stay travel planning using geo-tagged photos

GEOCROWD '13 Pub Date : 2013-11-05 DOI:10.1145/2534732.2534733

Xun Li

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引用次数: 21

Abstract

By utilizing large amount of crowd volunteered geo-tagged photos, existing research can successfully discover landmarks or attractive areas, mine travel patterns, find classical travel routes and recommend travel destinations or routes for inexperienced tourists. However, few of them focuses on a complicated real-life travel planning problem--planning multi-day and multi-stay (different places of accommodation) travel for tourist. By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results.

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使用地理标记照片进行多日和多住宿旅行计划

通过利用大量人群自愿提供的地理标记照片，现有的研究可以成功地发现地标或有吸引力的地区，挖掘旅行模式，找到经典的旅行路线，并为没有经验的游客推荐旅行目的地或路线。然而，很少有人关注现实生活中一个复杂的旅行规划问题——为游客规划多日、多住宿(不同的住宿地点)的旅行。通过整合数据挖掘和运筹学的新技术，我们开发了一个新的旅行计划系统，可以根据地理标记照片设计多天、多住宿的旅行计划。具体而言，提出了一种改进的迭代局部搜索启发式算法，利用从照片中发现的旅游图模型中的兴趣点(poi)和poi之间的递归权值来寻找多日多宿旅游规划问题的近似最优解。为了证明这种方法的可行性，我们从照片分享网站Panoromia.com上检索了澳大利亚的地理标记照片，为游客设计了多天、多住宿的实验性旅行计划。利用流图技术在不同地理尺度上挖掘的旅行模式对实验结果进行了评价。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

GEOCROWD '13

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0.00%

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