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Although almost half of US pleasure travellers are visiting friends and relatives (VFR), this market was marginalised and forgotten until the late 1980s. This study was designed to analyse sociodemographic and trip characteristic (‘tripographic’) differences between VFR and non-VFR travellers and between single- and multi-destination VFR travellers. Using data from a national travel survey, significant differences were found in the socio-demographic and trip characteristics of VFR and non-VFR travellers. Approximately 9 per cent of VFR travellers had multiple destinations, and multidestination VFRs had different characteristics than single-destination VFRs. Several important recommendations are provided for marketers interested in pursuing VFRs as a niche market.

Feature extraction and classification is a vital part in motor imagery-based brain-computer interface (BCI) system. Traditional deep learning (DL) methods usually perform better with more labeled training samples. Unfortunately, the labeled samples are usually scarce for electroencephalography (EEG) data, while unlabeled samples are available in large quantity and easy to collect. In addition, traditional DL algorithms are notoriously time-consuming for the training process. To address these issues, a novel method of hierarchical semi-supervised extreme learning machine (HSS-ELM) is proposed in this paper and applied for motor imagery (MI) task classification. Firstly, the deep architecture of hierarchical ELM (H-ELM) approach is employed for feature learning automatically, and then these new high-level features are classified using the semi-supervised ELM (SS-ELM) algorithm which can exploit the information from both labeled and unlabeled data. Extensive experiments were conducted on some benchmark datasets and EEG datasets to evaluate the effectiveness of the proposed method. Compared with several state-of-the-art methods, including SVM, ELM, SAE, H-ELM, and SS-ELM, our HSS-ELM method can achieve better classification accuracy, a mean kappa value of 0.7945 and 0.5701 across all subjects in the training and evaluation sessions of BCI Competition IV Dataset 2a, respectively. Finally, it comes to the conclusion that the proposed method has achieved superior performance for feature extraction and classification of EEG signals. Graphical abstract The schematic of the proposed HSS-ELM algorithm.

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China’s domestic tourism market makes up more than 90 per cent of the country’s tourist traffic, and contributes more than 70 per cent of total tourism receipts. This study proposed a demand model that examined the relationship between the annual expenditure of urban domestic travellers and per capita GDP. It was found that the demand theory developed in market economies was applicaable in a transit economy like China. Income elasticity of domestic tourism in China’s urban areas was determined to be 0.30. The model also recognised the positive effect of the country’s special economic zones on the domestic demand. Underlying reasons for the study’s findings are discussed. The model can be used to forecast domestic demand from Chinese urban centres. Implications of the study include the suggestion that the demand measurement of expenditure is more appropriate than person trips in the Chinese context and from the perspective of destination marketing.

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