Cooperative Adaptive Cruise Control Vehicles on Highways: Modelling and Traffic Flow Characteristics

Research output: ThesisDissertation (TU Delft)

43 Downloads (Pure)

Abstract

Traffic congestion causes detrimental effects on economy and society in terms of travel time delay, increased vehicle collision risk and increased air pollution. To tackle that, new intelligent transportation systems are being developed. One of these emerging technologies comprise automated driving systems. Adaptive Cruise Control (ACC), enabling a vehicle to follow its leader automatically, is an automated driving system which has been available in the vehicle market. Literature shows that traffic flow performance may not significantly benefit from ACC due to traffic instability and large headways. As an extension of ACC, Cooperative ACC is designed to improve traffic stability and throughput by using Vehicle-to-Vehicle (V2V) communication. Thanks to the anticipation of the downstream traffic, a short following gap can be realized by CACC which is highly expected to considerably increase road capacities. Before CACC vehicle is allowed and promoted in the market, it is crucial for policy makers and road operators to gain insights into the traffic flow impacts of CACC systems. Existing studies show that CACC can increase traffic throughput at high vehicle market penetration. However, the realistic effects of CACC on traffic flow have not been adequately revealed because the behaviour of CACC vehicles has not been realistically modelled. The multiple CACC driving modes, the degraded operation to ACC and the human driver take-over control when it is out of the CACC operational design domain, have not yet been explicitly included in existing CACC impact studies. A scientific gap prevails in modelling the complex CACC behaviour and investigating its actual influence on traffic flow especially at realistic networks with a single bottleneck and interacting bottlenecks.
Original languageEnglish
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • van Arem, B., Supervisor
  • Wang, M., Advisor
Award date3 Dec 2020
Publisher
Print ISBNs978-90-5584-280-3
DOIs
Publication statusPublished - 2020

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