Coupling travel characteristics identifying and deep learning for demand forecasting on car-hailing tourists: A case study of Beijing, China

Abstract

Online car-hailing, with its advantages of convenience and efficiency, has quickly become popular among tourists, playing a crucial role in the accessibility of scenic spots. Due to the particularities of tourist travel behaviour and the complexity of travel supply and demand around scenic spots, research on car-hailing tourists is relatively lacking at this stage. Based on multi-source data, this study couples the identifying of travel characteristics, by introducing the concept of service dependency degree, with a Bayesian optimization–long short-term memory–convolutional neural network (BO-LSTM-CNN) method to conduct multi-task online car-hailing demand forecasting. The evaluation of the dependency degree primarily encompasses the establishment of evaluation indices and the application of the entropy weight method and natural breakpoint method. The BO-LSTM-CNN model utilizes Bayesian optimization for hyperparameter tuning, LSTM for temporal variable processing, and CNN for the fusion of multi-source information related to weather, space, and online car-hailing attributes. Extracting online car-hailing tourist travel orders based on spatial–temporal constraints, the proposed methods are applied to 72 scenic spots in Beijing, China. According to their dependency degree, Beijing's scenic spots are categorized into three levels of dependency on online car-hailing services, from high to low. The outstanding forecasting efficacy of the proposed model for various scenic spots is verified through comparison tests with several benchmark models. Consequent to these findings, mobility service improvement strategies are specifically proposed for each class of scenic spots, which can provide valuable insights for the relevant tourism traffic management personnel.