Artikel

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.

Sprache
Englisch

Erschienen in
Journal: Transportation ; ISSN: 1572-9435 ; Volume: 49 ; Year: 2021 ; Issue: 3 ; Pages: 927-950 ; New York, NY: Springer US

Klassifikation
Handel, Kommunikation, Verkehr
Thema
Automated vehicles
Macroscopic travel demand model
Traffic performance
Perception of time
CoEXist

Ereignis
Geistige Schöpfung
(wer)
Sonnleitner, Jörg
Friedrich, Markus
Richter, Emely
Ereignis
Veröffentlichung
(wer)
Springer US
(wo)
New York, NY
(wann)
2021

DOI
doi:10.1007/s11116-021-10199-z
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

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Objekttyp

  • Artikel

Beteiligte

  • Sonnleitner, Jörg
  • Friedrich, Markus
  • Richter, Emely
  • Springer US

Entstanden

  • 2021

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