Understanding tourism travel behavior by combining revealed preference survey and mobile phone data
本研究提出结合手机数据和问卷调查数据的新方法，分析旅游者的出行方式和路径选择，并量化其对舒适度和环境的关注，为制定旅游交通政策提供依据。
• Hybrid choice model that combines survey and mobile phone data enables precise tourism travel behavior analysis and captures tourists' concerns for comfort and environmental issues. • The trajectory matching method based on the ST-DBSCAN algorithmcan can extract detailed travel modes and routes from mobile phone data. • Elasticity and willingness-to-pay analysis inform effective tourism policies. Tourism transportation is an indispensable element in tourism activities, serving as the premise for the emergence and development of tourism. Understanding the travel modes and path choice behaviors of tourists is the first step toward enhancing tourism transportation. An increasing amount of mobile phone (MP) data containing abundant information has been widely accumulated with the aid of information and communication technology. However, its limitations in capturing the travel modes of tourists and factors affecting their travel behavior (e.g., travel attitudes of travelers) restrict its further application. By contrast, revealed preference (RP) survey data collected through questionnaires include these factors. Nevertheless, from the perspective of dataset size, passive data sources such as MP data provide larger datasets than conventional questionnaire surveys (e.g., RP surveys). Therefore, this study proposes a set of new approaches for estimating the travel modes and path choices of tourists by combining the RP survey and MP data. The joint estimation of the two datasets based on a nested model structure with balanced parameters can adapt to different scales of the two datasets. Furthermore, we investigated tourists’ concerns regarding comfort and environmental protection and constructed a hybrid choice model (HCM) to quantify their impact. The travel process of tourists was more accurately reflected by introducing the stochastic transfer waiting time extracted from the MP data, and the performance of the estimation method was improved. The proposed model, findings, and discussion provide a basis for establishing policy measures, thereby contributing to improving the service quality and modal share of public transportation.