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Tourism is vital to national economic growth and fulfilling individuals' spiritual pursuits. However, traditional scenic recommendation algorithms must improve accuracy, susceprecommended route personalizationtion of recommended tour routes, and suboptimal tour value. This study presents a UGC text mining-based travel recommendation method to address these problems. To begin with, the proposed method utilizes text mining techniques to enrich scenic tag data and introduces the recommendation algorithm based on fuzzy label-matching user-item characteristics (FLMC). Additionally, we incorporate scenic attributes and travel time between scenics to identify four key attributes that impact scenic selection in tour routes. Users' subjective preferences for these attributes are calculated, and objective weights derived from information entropy are used to determine combination weights for scenic selection. This ensures a more personalized and optimized tour route. Lastly, the proposed method features a tour route ranking method that selects the highest-scoring route as the recommended option. This approach enhances the overall quality of the recommended tour routes. Through experimentation with 240,194 reviews of 369 scenics in Beijing, in the attraction recommendation scenario, the proposed FLMC algorithm achieves up to 75.40% accuracy, which is higher than the other three compared algorithms (46.48%, 35.90%, and 32.98%). In the travel route recommendation scenario, the proposed method has a route gain value of up to 1.57 and a user experience value of recommended route personalizationd compared to the other three compared algorithms (1.03, 0.9; 0.95, 0.53; 1.12, 0.85). These results highlight the significant potential of the proposed method in improving the personalization of travel recommendations and enhancing tourist experiences. [ABSTRACT FROM AUTHOR]

Air quality plays a vital role in people's health, and air quality forecasting can assist in decision making for government planning and sustainable development. In contrast, it is challenging to multi-step forecast accurately due to its complex and nonlinear caused by both temporal and spatial dimensions. Deep models, with their ability to model strong nonlinearities, have become the primary methods for air quality forecasting. However, because of the lack of mechanism-based analysis, uninterpretability forecasting makes decisions risky, especially when the government makes decisions. This paper proposes an interpretable variational Bayesian deep learning model with information self-screening for PM2.5 forecasting. Firstly, based on factors related to PM2.5 concentration, e.g., temperature, humidity, wind speed, spatial distribution, etc., an interpretable multivariate data screening structure for PM2.5 forecasting was established to catch as much helpful information as possible. Secondly, the self-screening layer was implanted in the deep learning network to optimize the selection of input variables. Further, following implantation of the screening layer, a variational Bayesian gated recurrent unit (GRU) network was constructed to overcome the complex distribution of PM2.5 and achieve accurate multi-step forecasting. The high accuracy of the proposed method is verified by PM2.5 data in Beijing, China, which provides an effective way, with multiple factors for PM2.5 forecasting determined using deep learning technology. [ABSTRACT FROM AUTHOR]

• Novel multi-modal transport recommendation via graph neural networks. • Models travel behaviors with spatial and temporal data integration. • Introduces a contrastive learning framework to reduce bias. • CDHGNN: New de-biased recommendation framework tested in Beijing. • Demonstrates superiority over existing methods using 300,000 user records. Conventional uni-modal transportation recommendation systems focused on single modes of transportation are limited in providing satisfactory solutions since passengers often undertake journeys involving multiple modes. Multi-modal transportation recommendation systems are becoming increasingly popular within navigation applications. However, these systems face challenges from biased raw data, data sparsity and long-tail distribution, as well as complexities in representing large-scale graph structures, which collectively hinder their optimal performance. This study introduces a novel approach for enhancing multi-modal transportation recommendation systems: the C ontrastive D e-biased H eterogeneous G raph N eural N etwork (CDHGNN). By integrating contrastive learning, the model generates augmented samples to mitigate bias and overcome the data-skewing problem. The heterogeneous graph neural network adaptively captures temporal and spatial patterns among users and locations, as well as spatial adjacency and attribute relations, leading to enhanced representations of nodes, and consequently, improved model performance. The proposed method was evaluated using real-world data from over 300,000 users' records in Beijing over two months in 2018. The extensive experiments demonstrate that the approach outperforms four contemporary state-of-the-art methods. The results underscore the potential of the CDHGNN in large-scale city-level problems in practical applications, revealing a promising advancement for multi-modal transportation recommendation systems. [ABSTRACT FROM AUTHOR]

User profiles, considered as one of the fundamental inputs of recommendation systems and customized services, can be rationally applied in the public transport domain to represent passengers' characteristics and behavioral preferences. A user profile of a bus passenger, termed as a bus passenger profile (BPP), is an assortment of labels containing passengers' travel features. This paper proposes a data-driven framework for developing BPP so as to provide guidance on how to create and estimate user profiles for bus passengers based on smart card data. The proposed method comprises three steps. (i) Data preprocessing aimed at extracting key information and preparing passenger profiling. (ii) A tag system aimed at storing the estimated travel features of passengers. (iii) Knowledge graphs aimed at connecting various BPP with semantic edges for practical application of prior knowledge in downstream tasks. The developed framework is implemented in a case study of the Beijing bus system. Deployment of the developed framework has demonstrated that it can satisfactorily develop BPP, while prior knowledge from the BPP-based knowledge graphs can benefit downstream tasks. [ABSTRACT FROM AUTHOR]