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Cited by 29Transportation and Mobility InnovationsTransportation Planning and OptimizationTraffic control and management

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Simulation of automated transport offers for the city of Brunswick

Open access
Cyganski, Rita, Heinrichs, Matthias, von Schmidt, Antje, Krajzewicz, Daniel·2018·Procedia Computer Science
DOI 10.1016/j.procs.2018.04.083
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Abstract

The introduction of automated vehicles in the transport system is widely expected to have significant impact on traffic flow and safety, mobility behavior, car ownership and modal usage. Not only the replacement of conventional passenger cars by automated ones but also other forms of mobility offers are being discussed, such as the introduction of vehicle-on-demand fleets. When investigating the effects of such systems, the changes in the perceived travel time have to be regarded. Within this paper, the effects of introducing automated vehicles as well as vehicle-on-demand and shared vehicle-on-demand offers are presented. Eight different simulation settings with different fleet sizes and shares of private automated vehicles were evaluated using an agent-based demand model. The factors for value of time were obtained from a stated preference user survey. The results show only minor changes in the modal split and the amount of rides for the city of Brunswick due to the relatively small travel distances prevailing.

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Crossref · 10.1016/j.procs.2018.04.083

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Statistics

Figures

Fig. 1 a) Size and age constitution of Brunswick population for the years 2010 and 2030; b) population density within the simulated area.
Fig. 1 a) Size and age constitution of Brunswick population for the years 2010 and 2030; b) population density within the simulated area.CC-BY-NC-ND · From the PDF
Fig. 2 Mode share distribution within the city of Brunswick for the scenarios, a) all inhabitants b) inhabitants without a car in the household
Fig. 2 Mode share distribution within the city of Brunswick for the scenarios, a) all inhabitants b) inhabitants without a car in the householdCC-BY-NC-ND · From the PDF
Fig. 3 Comparison of the average travel distances of different transport modes for the simulated settings.
Fig. 3 Comparison of the average travel distances of different transport modes for the simulated settings.CC-BY-NC-ND · From the PDF
Fig. 4 a) Person kilometers travelled by car as driver; b) Person kilometers travelled of VOD vehicles.
Fig. 4 a) Person kilometers travelled by car as driver; b) Person kilometers travelled of VOD vehicles.CC-BY-NC-ND · From the PDF
Image 5
CC-BY-NC-ND · From the PDF

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Citations

Cites

Show all 1 references

Research works printed but not linked: 9

  1. Trommer, 2016, Autonomous Driving - The Impact of Vehicle Automation on Mobility Behaviour
  2. Fagnant, DJ, Kockelman KM.The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. In: Transportation Research Board 93th Annual Meeting. No. 15-1962. 2014. · DOI · Paper page
  3. Meyer, 2017, Autonomous vehicles: The next jump in accessibilities?, Research in Transportation Economics, 62, 80, 10.1016/j.retrec.2017.03.005 · DOI · Paper page
  4. Bischoff, 2015, Simulation of City-wide Replacement of Private Cars with Autonomous Taxis in Berlin, Procedia Computer Science, Vol.83, 2016, pp. 237-244.7. Fagnant, DJ, Kockelman, KM. Dynamic ride-sharing and optimal fleet sizing for a system of shared autonomous vehicles in Austin, Texas. In: Transportation Research Board 94th Annual Meeting. No., 15-1962
  5. Bienzeisler, 2017, Verkehrliche Auswirkungen einer autonomen Carsharingflotte, ATZ-Automobiltechnische Zeitschrift, 119, 66, 10.1007/s35148-017-0064-2 · DOI
  6. Kolarova, V, Steck, F, Cyganski, R, Trommer, S. Estimation of value of time for autonomous driving using revealed and stated preference method. In: European Transport Conference, 2017. · DOI
  7. Heinrichs M, Krajzewicz D, Cyganski R, von Schmidt A. Introduction of car sharing into existing car fleets in microscopic travel demand modelling. In: Personal and Ubiquitous Computing, 2017. · DOI
  8. Steck F, Kolarova V, Bahamonde-Birke F, Trommer S, Lenz B. How autonomous driving may affect the value of travel time savings for commuting. In: Proceedings of the 97th Annual Meeting of the TRB, 2008.
  9. Zimmer W, Blanck R, Bergmann T, Mottschall M, von Waldenfels R, Cyganski R, Wolfermann A, Winkler C, Heinrichs M, Dünnebeil F, Fehrenbach H, Kämper C, Biemann K, Kräck J, Peter M, Zandonella R, Bertschmann D. Endbericht RENEWBILITY III - Optionen einer Dekarbonisierung des Verkehrssektors. 2016. · DOI

Cited by

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Metadata

Authors
Cyganski, R., Heinrichs, M., von Schmidt, A., & Krajzewicz, D.
Affiliations
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
Corresponding author
Cyganski, Rita
ORCID
Venue
Procedia Computer Science
Publisher
Elsevier BV
Published
2018
Volume
130
Pages
872-879
ISSN
1877-0509 (ISSN-L)
DOI
10.1016/j.procs.2018.04.083
OpenAlex
W2801663809
Cited by
29
References
1
Citations by year
2026: 2 · 2025: 4 · 2024: 5 · 2023: 6 · 2022: 2 · 2021: 4 · 2020: 4 · 2019: 1 · 2018: 1
FWCI
2.56
Type
conference paper
Language
en
Open access
Licence
Locations
  • Procedia Computer Science (publishedVersion, cc-by-nc-nd)
  • elib (German Aerospace Center) (acceptedVersion)
Topics
  • Social Sciences › Transportation › Transportation and Mobility Innovations
  • Social Sciences › Transportation › Transportation Planning and Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
Keywords
autonomous vehicles, modal split, travel time perception, value of time, stated preference, fleet size, car ownership, mobility behavior, Braunschweig

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CoChat. (2026). Citation check: Simulation of automated transport offers for the city of Brunswick. https://app.cochat.ai/papers/10.1016/j.procs.2018.04.083
The paper
Cyganski, R., Heinrichs, M., von Schmidt, A., & Krajzewicz, D. (2018). Simulation of automated transport offers for the city of Brunswick. Procedia Computer Science. https://doi.org/10.1016/j.procs.2018.04.083