Abstract
Method of Predicting Passenger Flow in Scenic Areas Considering Multisource Traffic Data
Highlights
Passenger flow prediction in scenic areas plays a key role in their development
Accurate passenger flow prediction facilitates relevant departments to carry out safety risk management and control and improve the safety management of scenic areas.[1]
We propose a method of predicting passenger flow in a scenic area that is based on a hybrid neural network (HNN) model combining a convolutional neural network (CNN) and long short-term memory (LSTM) with the support of multisource traffic data
Summary
Passenger flow prediction in scenic areas plays a key role in their development. Accurate passenger flow prediction facilitates relevant departments to carry out safety risk management and control and improve the safety management of scenic areas.[1]With the continuous improvement of computer performance, the world has entered the era of big data, and the mining of tourism data has transitioned from the collection and analysis of ISSN 0914-4935 © MYU K.K. https://myukk.org/Sensors and Materials, Vol 32, No 11 (2020)sparse samples to multisource big-data comprehensive analysis. Passenger flow prediction in scenic areas plays a key role in their development. Accurate passenger flow prediction facilitates relevant departments to carry out safety risk management and control and improve the safety management of scenic areas.[1]. The existing research, which ignores the impact of the spatiotemporal distribution of public transport passenger flow on the passenger flow inside a scenic area, is mostly based on the single-source monitoring data of passenger flow, mainly relying on the manual investigation or installation of fixed information collection equipment for data acquisition. The related research of scholars has mainly focused on the large-scale passenger flow distribution and passenger behavior pattern mining, while there have been few studies on passenger flow in scenic areas using traffic passenger flow data. Some scholars have used the data obtained by bus card swiping in London and used passenger portraits to explore the patterns of resident public interchange and travel.[7]
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