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利用水上无人驾驶车辆进行博物馆参观:阿姆斯特丹的案例研究

Employing waterborne autonomous vehicles for museum visits: a case study in Amsterdam

作者:Helena Hang Rong;Wei Tu;Fábio Duarte;Carlo Ratti;

关键词:Waterborne autonomous vehicle,Genetic optimization,Route recommendation,Museum,GIS

DOI:https://doi.org/10.1186/s12544-020-00459-x

发表时间:2020年

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摘要

阿姆斯特丹是一个文化丰富的城市,吸引了数百万游客。 阿姆斯特丹的热门活动包括参观博物馆和乘船游览。 通过战略性地将它们结合起来,本文提出了一种创新的方法,利用水上无人驾驶车辆(WAV)来改善阿姆斯特丹的博物馆参观。 使用包括I Amsterdam卡数据和Instagram标签在内的多源城市数据来揭示博物馆的特点,例如博物馆的线下和线上受欢迎程度以及参观模式。提出了一个多目标模型,通过考虑博物馆特点和旅行体验来优化WAV路线。 在阿姆斯特丹中心地区进行了一项实验,评估了使用WAV的可行性。 通过将WAV与陆地交通进行比较,结果表明WAV可以提升到文化目的地的旅行体验。 展示的创新WAV可以扩展到城市中更多的景点。 这些发现提供了关于在城市旅游中采用人工智能的有用见解。


Abstract

Amsterdam is a culturally rich city attracting millions of tourists. Popular activities in Amsterdam consist of museum visits and boat tours. By strategically combining them, this paper presents an innovative approach using waterborne autonomous vehicles (WAVs) to improve the museum visitation in Amsterdam. Multi-source urban data including I Amsterdam card data and Instagram hashtags are used to reveal museum characteristics such as offline and online popularity of museums and visitation patterns. A multi-objective model is proposed to optimize WAV routes by considering museum characteristics and travel experiences. An experiment in the Amsterdam Central area was conducted to evaluate the viability of employing WAVs. By comparing WAVs with land transportation, the results demonstrate that WAVs can enhance travel experience to cultural destinations. The presented innovative WAVs can be extended to a larger variety of points of interest in cities. These findings provide useful insights on embracing artificial intelligence in urban tourism.