Mobility Compass

Discover mobility and transportation research. Find experts, partners, networks.

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The Mobility Compass is an open tool for improving networking and interdisciplinary exchange within mobility and transport research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Seuring, Stefan
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Schakel, W. J.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (11/11 displayed)

  • 2021Impact of radio channel characteristics on the longitudinal behaviour of truck platoons in critical car-following situationscitations
  • 2020A generic multi-scale framework for microscopic traffic simulation part II: Anticipation Reliance as compensation mechanism for potential task overload16citations
  • 2019Using advanced adaptive cruise control systems to reduce congestion at sags: An evaluation based on microscopic traffic simulation54citations
  • 2019Using advanced adaptive cruise control systems to reduce congestion at sags: An evaluation based on microscopic traffic simulation54citations
  • 2018Exploring the effects of perception errors and anticipation strategies on traffic accidents - A simulation study9citations
  • 2017Will Automated Vehicles Negatively Impact Traffic Flow?157citations
  • 2017Will automated vehicles negatively impact traffic flow?157citations
  • 2017Pleasure in using adaptive cruise control: A questionnaire study in The Netherlands14citations
  • 2014Empirical analysis of an in-car speed, headway and lane use Advisory systemcitations
  • 2014Improving traffic flow efficiency by in-car advice on lane, speed, and Headway79citations
  • 2010Effects of cooperative adaptive cruise control on traffic flow stability245citations

Places of action

Chart of shared publication
Sharma, Salil
1 / 18 shared
Knoppers, P.
1 / 3 shared
Verbraeck, A.
2 / 15 shared
Riebl, Raphael
1 / 18 shared
Al-Khanak, Ehab Nabiel
1 / 1 shared
Van Lint, J. W. C.
5 / 51 shared
Calvert, S. C.
4 / 30 shared
Van Arem, B.
4 / 83 shared
Goñi-Ros, B.
1 / 2 shared
Wang, Meng
1 / 46 shared
Knoop, V. L.
2 / 43 shared
Hoogendoorn, S. P.
2 / 117 shared
Papacharalampous, A. E.
1 / 3 shared
Sakata, I.
1 / 2 shared
Sakata, Ichiro
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Goni Ros, B.
1 / 6 shared
Wang, M.
2 / 29 shared
Papacharalampous, Alexandros E.
1 / 3 shared
Lint, J. W. C. Van
1 / 4 shared
Gorter, C. M.
1 / 3 shared
De Winter, J. C. F.
1 / 69 shared
Van Arem, Bart
2 / 383 shared
Netten, B. D.
1 / 7 shared
Chart of publication period
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Co-Authors (by relevance)

  • Sharma, Salil
  • Knoppers, P.
  • Verbraeck, A.
  • Riebl, Raphael
  • Al-Khanak, Ehab Nabiel
  • Van Lint, J. W. C.
  • Calvert, S. C.
  • Van Arem, B.
  • Goñi-Ros, B.
  • Wang, Meng
  • Knoop, V. L.
  • Hoogendoorn, S. P.
  • Papacharalampous, A. E.
  • Sakata, I.
  • Sakata, Ichiro
  • Goni Ros, B.
  • Wang, M.
  • Papacharalampous, Alexandros E.
  • Lint, J. W. C. Van
  • Gorter, C. M.
  • De Winter, J. C. F.
  • Van Arem, Bart
  • Netten, B. D.
OrganizationsLocationPeople

article

Will Automated Vehicles Negatively Impact Traffic Flow?

  • Lint, J. W. C. Van
  • Schakel, W. J.
  • Calvert, S. C.
Abstract

With low-level vehicle automation already available, there is a necessity to estimate its effects on traffic flow, especially if these could be negative. A long gradual transition will occur from manual driving to automated driving, in which many yet unknown traffic flow dynamics will be present. These effects have the potential to increasingly aid or cripple current road networks. In this contribution, we investigate these effects using an empirically calibrated and validated simulation experiment, backed up with findings from literature. We found that low-level automated vehicles in mixed traffic will initially have a small negative effect on traffic flow and road capacities. The experiment further showed that any improvement in traffic flow will only be seen at penetration rates above 70%. Also, the capacity drop appeared to be slightly higher with the presence of low-level automated vehicles. The experiment further investigated the effect of bottleneck severity and truck shares on traffic flow. Improvements to current traffic models are recommended and should include a greater detail and understanding of driver-vehicle interaction, both in conventional and in mixed traffic flow. Further research into behavioural shifts in driving is also recommended due to limited data and knowledge of these dynamics.

Topics
  • simulation
  • data
  • driving
  • driver
  • experiment
  • estimate
  • bottleneck
  • road network
  • truck
  • autonomous vehicle
  • autonomous driving
  • automation
  • vehicle mix
  • traffic model
  • manual

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