A Deep Spatiotemporal Fuzzy Neural Network for Subway Passenger Flow Prediction With COVID-19 Search Engine Data
IEEE Transactions on Fuzzy Systems
; 31(2):394-406, 2023.
Article
in English
| ProQuest Central | ID: covidwho-2236429
ABSTRACT
Passenger flow prediction is of great significance in the operation and management of subways, especially in reducing energy consumption and improving service quality. Due to the impact of COVID-19, subway passenger flow fluctuates a lot, which makes passenger flow estimation or forecasting a very challenging task. This article mainly carries out two aspects of work to solve the task of subway passenger flow prediction under pandemic. First, this article introduces search engine data as a new data source and provides a systematic method to extract valid quires and search volumes that are closely associated with subway passenger flow under pandemic. Second, this article combines the fuzzy theory and neural network to propose a deep learning architecture called "deep spatiotemporal fuzzy neural network” to deal with the complex spatiotemporal features and uncertain external data of subway passenger flow prediction. Experiments on the actual dataset of the Beijing subway prove the superiority of the model and the effectiveness of search engine data in subway passenger flow forecasting.
Computers--Computer Networks; Public transportation; Pandemics; Search engines; Forecasting; Predictive models; Internet; Data models; Deep learning; fuzzy neural networks (FNNs); intelligent transportation systems; subway passenger flow prediction; Subways; Fuzzy logic; Artificial neural networks; Neural networks; Data search; Passengers; Energy consumption; Coronaviruses; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Prognostic study
/
Systematic review/Meta Analysis
Language:
English
Journal:
IEEE Transactions on Fuzzy Systems
Year:
2023
Document Type:
Article
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