A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control
A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control
Fully automated vehicles could have a significant share of the road network traffic in the near future. Several commercial vehicles with full-range Adaptive Cruise Control (ACC) systems or semi-autonomous functionalities are already available on the market. Many research studies aim at leveraging the potential of automated driving in order to improve the fuel efficiency of vehicles. However, in the vast majority of those, fuel efficiency is isolated to the driving dynamics between a single follower-leader pair, hence overlooking the complex nature of traffic. Consequently fuel efficiency and the efficient use of the roadway capacity are framed as conflicting objectives, leading to fuel-economy control models that adopt highly conservative driving styles.
This formulation of the problem could be seen as a user-optimal approach, where in spite of delivering savings for individual vehicles, there is the side-effect of the deterioration of traffic flow. An important point that is overlooked is that the inefficient use of roadway capacity gives rise to congested traffic and traffic breakdowns, which in return increases energy costs within the system. The optimisation methods used in these studies entail high computational costs and, therefore, impose a strict constraint on the scope of problem.
In this study, the use of car-following models and the limitation of the search space of optimal strategies to the parameter space of these is proposed. The proposed framework enables performing much more comprehensive optimisations and conducting more extensive tests on the collective impacts of fuel-economy driving strategies. The results show that, as conjectured, a “short-sighted” user-optimal approach is unable to deliver overall fuel efficiency. Conversely, a system-optimal formulation for fuel efficient driving is presented, and it is shown that the objectives of fuel efficiency and traffic flow are in fact not only non-conflicting, but also that they could be viewed as one when the global benefits to the network are considered.
27-41
Mamouei, M.H.
c055354b-9653-44bf-9f0c-8e00bb72ddd2
Kaparias, Ioannis
e7767c57-7ac8-48f2-a4c6-6e3cb546a0b7
Halikias, G.
f3675dcf-e69b-48fc-8bc0-2fa8ee7a8d2d
July 2018
Mamouei, M.H.
c055354b-9653-44bf-9f0c-8e00bb72ddd2
Kaparias, Ioannis
e7767c57-7ac8-48f2-a4c6-6e3cb546a0b7
Halikias, G.
f3675dcf-e69b-48fc-8bc0-2fa8ee7a8d2d
Mamouei, M.H., Kaparias, Ioannis and Halikias, G.
(2018)
A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control.
Transportation Research Part C: Emerging Technologies, 92, .
(doi:10.1016/j.trc.2018.02.002).
Abstract
Fully automated vehicles could have a significant share of the road network traffic in the near future. Several commercial vehicles with full-range Adaptive Cruise Control (ACC) systems or semi-autonomous functionalities are already available on the market. Many research studies aim at leveraging the potential of automated driving in order to improve the fuel efficiency of vehicles. However, in the vast majority of those, fuel efficiency is isolated to the driving dynamics between a single follower-leader pair, hence overlooking the complex nature of traffic. Consequently fuel efficiency and the efficient use of the roadway capacity are framed as conflicting objectives, leading to fuel-economy control models that adopt highly conservative driving styles.
This formulation of the problem could be seen as a user-optimal approach, where in spite of delivering savings for individual vehicles, there is the side-effect of the deterioration of traffic flow. An important point that is overlooked is that the inefficient use of roadway capacity gives rise to congested traffic and traffic breakdowns, which in return increases energy costs within the system. The optimisation methods used in these studies entail high computational costs and, therefore, impose a strict constraint on the scope of problem.
In this study, the use of car-following models and the limitation of the search space of optimal strategies to the parameter space of these is proposed. The proposed framework enables performing much more comprehensive optimisations and conducting more extensive tests on the collective impacts of fuel-economy driving strategies. The results show that, as conjectured, a “short-sighted” user-optimal approach is unable to deliver overall fuel efficiency. Conversely, a system-optimal formulation for fuel efficient driving is presented, and it is shown that the objectives of fuel efficiency and traffic flow are in fact not only non-conflicting, but also that they could be viewed as one when the global benefits to the network are considered.
Text
Mamouei et al - TR-C paper (AAC)
- Accepted Manuscript
More information
Accepted/In Press date: 2 February 2018
e-pub ahead of print date: 27 April 2018
Published date: July 2018
Identifiers
Local EPrints ID: 420726
URI: http://eprints.soton.ac.uk/id/eprint/420726
ISSN: 0968-090X
PURE UUID: 70bf5606-fa69-470e-8f8d-09da8344d2eb
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Date deposited: 14 May 2018 16:30
Last modified: 16 Mar 2024 06:37
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Author:
M.H. Mamouei
Author:
G. Halikias
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