Rail Passenger Flow Prediction Combining Social Media Data for Rail Passenger

Jiren Shen ()
Additional contact information
Jiren Shen: Beijing Capital Agribusiness and Food Group

A chapter in LISS 2021, 2022, pp 672-681 from Springer

Abstract: Abstract Comprehensive characterization and scientific prediction of urban rail transit passenger flow plays a very important role in the process of urban rail transit planning, construction and management operation. This study combines social media data to characterize urban rail transit passenger flow under the influence of different social events, and achieves a comprehensive characterization of rail transit passenger flow and scientific prediction of passenger flow.

Keywords: Social media; Big data; Rail transit; Passenger flow prediction (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-16-8656-6_59

Ordering information: This item can be ordered from
http://www.springer.com/9789811686566

DOI: 10.1007/978-981-16-8656-6_59

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().