An Intelligent Customization Framework for Tourist Trip Design Problems

In the era of the experience economy, “customized tours” and “self-guided tours” have become mainstream. This paper proposes an end-to-end framework for solving the tourist trip design problems (TTDP) using deep reinforcement learning (DRL) and data analysis. The proposed approach considers heterogeneous tourist preferences, customized requirements, and stochastic traffic times in real applications. With various heuristics methods, our approach is scalable without retraining for every new problem instance, which can automatically adapt the solution when the problem constraint changes slightly. We aim to provide websites or users with software tools that make it easier to solve TTDP, promoting the development of smart tourism and customized tourism

Recommendation system to determine suitable and viable hiking routes: a prototype application in Sierra de las Nieves Nature Reserve (southern Spain)

This paper describes a system for recommending hiking routes to help manage hiking activities in a protected area. The system proposes various routes, based on five criteria that maximize some aspects of hikers’ requirements (by analyzing the viability and difficulty of the trails) and also those of protected areas managers (by proposals to relieve congestion in areas already used for hiking and to promote awareness of new ones, as a contribution to environmental education). The recommendation system uses network analysis, multi-criteria decision analysis and geographic information system by free software tools: PgRouting, PostgreSQL and PostGIS. This system has been tested in Sierra de las Nieves Nature Reserve (Andalusia, Spain). Of the 182 routes obtained by the system, 62 (34%) are considered viable for hikers in Sierra de las Nieves, taking into account the type of user most likely to visit this protected area. Most routes have a high difficulty level, which is coherent with the mountainous character of the protected area.This study is a contribution to the Research Projects SEJ2007-67690, funded by the Spanish Ministry of Science and Innovation, and P07HUM-03049, funded by the Ministry of Innovation, Science and Enterprise of the Andalusian Regional Government. This work was carried out within the framework of research group HUM 776 (Geographic Analysis) of the Andalusian Regional Government

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

都市における街歩きを促進する複合現実ナビゲーションインタフェースの設計

早大学位記番号:新9248博士（工学）早稲田大

From Automobiles to Alternatives: Applying Attitude Theory and Information Technologies to Increase Shuttle Use at Rocky Mountain National Park

This thesis examines potential strategies for increasing voluntary shuttle use at Rocky Mountain National Park (ROMO) and the gateway community of Estes Park, Colorado. The first chapter of this two-part study evaluates the impact of a pilot intelligent transportation system (ITS) on visitor awareness and use of shuttles during the summer of 2011. Two forms of ITS, dynamic message signs (DMS) and highway advisory radio (HAR), were evaluated. Specifically, the ITS was meant to influence day-visitors to park at a new park-and-ride lot just east of Estes Park where they could then board a connector shuttle and transfer to any of four shuttle routes servicing the town and park. Surveys were administered onboard the park-and-ride shuttle (N = 68) and at two locations in downtown Estes Park (N = 490). Our analysis revealed that the DMS contributed to increased awareness of the shuttles. However, the HAR did not contribute substantially to awareness or use of the visitor shuttles. Our analysis offers additional recommendations for increasing voluntary shuttle use, such as providing direct routes between the park-and-ride and popular park attractions. The results of this study demonstrate the utility of ITS as a transportation management tool in a national park setting, but also highlight the importance of selecting appropriate technologies that meet the needs of park visitors. The second chapter explores strategies for optimizing the use of ITS by applying the theory of planned behavior (Ajzen, 1991) to identify the beliefs that inform choice of travel mode among ROMO and Estes Park visitors. Using results of a mail survey (N = 222), the theory of planned behavior was applied to the prediction of intention and use of visitor shuttles. Perceived behavioral control was found to have a significant influence on intention to use shuttles. Past experience with park shuttles was tested as an additional predictor of behavior and shown to significantly improve the prediction of shuttle use. Past experience with public transit was also added to the model, but with no significant contribution, thereby demonstrating the inherent difference between travel behaviors in everyday settings as opposed to recreation settings. These results were then coupled with segmentation analysis to identify unique segments of visitors. The segments were statistically similar in terms of demographic characteristics, yet heterogeneous in their attitudes, subjective norms, and perceived control regarding shuttle use. Of the three segments identified, Bus Backers were found to hold the most positive beliefs about shuttles and Potential Mode-shifters were identified as the segment offering the most potential for mode change due to their neutral attitudes and beliefs. Strategies were identified to maintain and improve use of shuttles among these segments. Our study broadens the application of segmentation analysis to transportation in a park setting and demonstrates its important contribution

Geo Data Science for Tourism

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.

都市の持続可能性に向けた旅行行動と知的移動データ統合に関する包括的研究

過去数十年にわたり世界中で都市の持続可能性がトレンドとなり研究対象となっている．人々は，非効率な天然資源の消費や社会経済活動による環境破壊など，地球環境に有害な活動を行い，これには都市計画や交通計画を始め，多くの分野が密接に関係している．現在では，これらを解決する新技術の開発や応用が広範囲な研究分野で日々取り組まれている．本研究では観光に関する問題を，交通と都市の研究の観点からさまざまなビックデータを使用し，持続可能な都市開発を目標とした具体的な解決策を示した．本研究では都市や地域の持続可能性に資するデータの活用方法として，Wi-Fiパケットセンサーを使用した旅行者にとって魅力的な観光目的地マネジメントに関する研究，およびETCプローブデータを使用した旅行時間の信頼性の観測における天候の影響に関する分析を組み合わせて示した．本論文では，都市の移動性の認知に対して以下に示す3つの研究から，特徴的な結果と有効な分析手法を確立した．1）Wi-Fiパッケージセンシング調査を使用した，広域観光エリアでの周遊パターンのマイニングベースの関連法則の調査，2）Wi-Fi追跡データでの大規模な観光地の持続可能な開発に向けた魅力的な目的地の抽出，3）ETC2.0プローブデータを使用して，様々な道路タイプを考慮した旅行の信頼性に対する降雪の影響の評価．以上の研究から，複数視点の考察を積み重ね，包括的な評価と提案を行い，いくつかの重要な結果が得られた．この論文の貢献は，より良い社会への問題解決への糸口となり，今後の政策立案者にとって有意義な内容となるだろう．According to sustainability, the trend is spreading out around the world for past decades. There are many area subjects involved, such as city planning, transportation planning, and so on, because people realized human activities harmful to the environment by consuming natural resources with less efficiency process or damage environment by social and economic movements. Currently, emerging technologies considered for the proactive procedure in extensive study areas regarding new technology application and knowledge based. In term of transport and urban study, including tourism concerns, we used intelligent data from deferent sources to be demonstrating the possible solutions which involve sustainable urban development concept. In this study, as a method of utilizing data that contributes to the sustainability of cities and regions, consideration of attractive destination management for tourists by using wireless probe data, and the weather impact on travel time reliability observation by using electronic toll collection probe data, it represented as combination experiments throughout comprehensive study. This dissertation addressed three contribution studies to the composed acknowledgment of urban mobility, and it obtained the intelligent data and specific method of research-based. It consists of; 1) an association rule mining-based exploration of travel patterns in wide tourism areas using a Wi-Fi package sensing survey, 2) Attractive destinations mining towards massive tourism area sustainable development on Wi-Fi tracking data, and 3) Assessment of the impact of snowfall on travel reliability considering different road types using ETC2.0 probe data. Hence, a stack of varying viewpoints researches provided a comprehensive review and suggestion throughout significant results. The contribution of this dissertation could be an advantage substance for strategy and policies planner to recognize alternative solutions leading to a better society.室蘭工業大学 (Muroran Institute of Technology)博士（工学