To improve the forecasting accuracy of tourism demand through forecasting model and data sources, this paper takes the social network data as an entry point, and collects the social network data by the web crawler, then quantifies the data based on the sentiment analysis of the BERT model. This paper uses structured variables such as social network data, weather, holidays, etc. to build a tourism demand forecasting model based on Gradient Boosting Regression Trees. At last, take Huang Shan as example, use actual statistics of passenger terminal and social network data to do an empirical analysis of Huang Shan tourism demand forecasting. Compared with the existing model and introduce ablation study to verify the effectiveness of the considered factors. The result shows that the model based on social network data has improved the forecasting accuracy from the existing ones, ablation study shows social network data helps to improve the accuracy of tourism demand forecasting.

This study aims to guide the community life circle to create a green, travel-supportive built environment. It quantitatively analyzes the variations in car usage behavior based on the level of the built environment of the community and objectively reflects the car usage behavior based on the parking space utilization rate (PSUR). Ordinary least squares (OLS) and gradient boosting decision tree (GBDT) models were developed to describe the impact of the built environment on this utilization rate. An empirical analysis of the model was also conducted using the multisource, heterogeneous parking data of commercial parking facilities in the main urban area of Chongqing, China; the data include records of parking survey, points of interest, and road networks. The results showed that the GBDT model had a better fitting degree than the OLS model considering nonlinear effects. In terms of the contribution of community-built environment variables, distance to business center (14.30%), population density (14.20%), and land use mix (12.60%) considerably affect the PSUR, indicating that these variables have an important influence on the use of private cars. All built environment variables have nonlinear relationships, and the threshold effects reflect a complex relationship between the built environment and car usage behavior. This study provides refined suggestions for the spatial design and transformation of the community life circle.

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In this study, gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models were constructed to systematically ascertain the influencing factors and electric vehicle (EV) use action laws from the perspective of travelers. The use intensity of EVs was represented by electric vehicle miles traveled (eVMT); variables such as the charging time, travel preference, and annual income were used to describe the travel characteristics. Seven variables, including distance to the nearest business district, road density, public transport service level, and land use mix were extracted from different dimensions to describe the built environment, explore the influence of the travel behavior mode and built environment on EV use. From the eVMT survey data, points of interest (POI) data, urban road network data, and other heterogeneous data from Chongqing, an empirical analysis of EV usage intensity was conducted. The results indicated that the deviation of the GBDT model (9.62%) was 11.72% lower than that of the OLS model (21.34%). The charging time was the most significant factor influencing the service intensity of EVs (18.37%). The charging pile density (15.24%), EV preference (11.52%), and distance to the nearest business district (10.28%) also exerted a significant influence.