Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
Qili Tang,
Li Yang,
Li Pan
Abstract:The existing scenic spot passenger flow prediction models have poor prediction accuracy and inadequate feature extraction ability. To address these issues, a multi-attentional convolutional bidirectional long short-term memory (MACBL)-based method for predicting tourist flow in tourist scenic locations in a location-based services big data environment is proposed in this study. First, a convolutional neural network is employed to identify local features and reduce the dimension of the input data. Then, a bidir… Show more
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.