A General Scheme for Deterministic Microscopic Traffic Models. Part II: Empirical Verifications

Bo Yang (), Jiwei Yoon () and Christopher Monterola ()
Additional contact information
Bo Yang: Complex Systems Group, Institute of High Performance Computing, A*STAR
Jiwei Yoon: UC Berkeley, Department of Materials Science and Engineering
Christopher Monterola: Complex Systems Group, Institute of High Performance Computing, A*STAR

A chapter in Traffic and Granular Flow '15, 2016, pp 459-466 from Springer

Abstract: Abstract WeYang, Bo describe the techniquesYoon, Jiwei for the extractionMonterola, Christopher of the microscopic empirical data from high frame-rate videos of the traffic flows in Singapore. Such data include accelerations, velocities, headways and approach velocities. Following the discussions and the proposal from A General Scheme for Deterministic Microscopic Traffic Models. Part I: Theoretical Construction, we aggregate the collected microscopic empirical data by the proper sampling and the averaging of the unimportant factors influencing the driving behaviours, and present some of the tentative results in mapping the master model for the deterministic microscopic traffic models.

Keywords: Traffic Flow; Traffic Model; Driving Behaviour; Traffic System; Theoretical Construction (search for similar items in EconPapers)
Date: 2016
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:sprchp:978-3-319-33482-0_58

Ordering information: This item can be ordered from
http://www.springer.com/9783319334820

DOI: 10.1007/978-3-319-33482-0_58

Access Statistics for this chapter

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