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Methodology for Generating Individualised Trajectories from Experiments Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Traffic Flow Optimisation at Sags by Controlling the Acceleration of Some Vehicles

Authors : Bernat Goñi-Ros, Victor L. Knoop, Kenichi Kitahama, Bart van Arem, Serge P. Hoogendoorn

Published in: Traffic and Granular Flow '15

Publisher: Springer International Publishing

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Abstract

Sags are bottlenecks in freeway networks. Nowadays, there is a growing interest in the development of traffic management measures for sags based on the use of in-car systems. This contribution determines the movements that individual (equipped) vehicles should make in order to minimise congestion. Specifically, we optimise the accelerations of some selected vehicles as they move along a one-lane freeway stretch with a sag, setting as objective the minimisation of total travel time. The optimisation results highlight the relevance of two traffic management strategies: (a) motivating drivers to accelerate fast along sags; and (b) limiting the inflow to sags. Also, they suggest ways to apply these strategies in practice by regulating the acceleration of vehicles equipped with in-car systems. These results prove the usefulness of the proposed method as a tool for control measure development.

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previous chapter Scaling from Circuit Experiment to Real Traffic Based on Optimal Velocity Model
next chapter Impact of Synchronised Flow in Oversaturated City Traffic on Energy Efficiency of Conventional and Electrical Vehicles
Footnotes
1
The effects of the supporting strategy have been identified by comparing the scenarios in which some or all controlled vehicles use this strategy with corresponding virtual scenarios in which the supporting strategy was excluded from the solution.
 
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Metadata
Title
Traffic Flow Optimisation at Sags by Controlling the Acceleration of Some Vehicles
Authors
Bernat Goñi-Ros
Victor L. Knoop
Kenichi Kitahama
Bart van Arem
Serge P. Hoogendoorn
Copyright Year
2016
Book
Traffic and Granular Flow '15

Print ISBN: 978-3-319-33481-3

Electronic ISBN: 978-3-319-33482-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-33482-0_67

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