A quantitative approach to the behavioural analysis of drivers in highways using particle filtering
A quantitative approach to the behavioural analysis of drivers in highways using particle filtering
The analysis of driving behaviour is a challenging task in the transport field that has numerous applications, ranging from highway design to micro-simulation and the development of advanced driver assistance systems. There has been evidence suggesting changes in the driving behaviour in response to changes in traffic conditions, and this is known as adaptive driving behaviour. Identifying these changes and the conditions under which they happen, and describing them in a systematic way, contributes greatly to the accuracy of micro-simulation, and more importantly to the understanding of the traffic flow, and therefore paves the way for introducing further improvements with respect to the efficiency of the transport network. In this paper adaptive driving behaviour is linked to changes in the parameters of a given car-following model. These changes are tracked using a dynamic system identification method, called particle filtering. Subsequently, the dynamic parameter estimates are further processed to identify critical points where significant changes in the system take place.
78-96
Mamouei, Mohammad
c055354b-9653-44bf-9f0c-8e00bb72ddd2
Kaparias, Ioannis
e7767c57-7ac8-48f2-a4c6-6e3cb546a0b7
Halikias, George
bfb80bdc-cf71-4651-96fc-24ff60c8412f
2016
Mamouei, Mohammad
c055354b-9653-44bf-9f0c-8e00bb72ddd2
Kaparias, Ioannis
e7767c57-7ac8-48f2-a4c6-6e3cb546a0b7
Halikias, George
bfb80bdc-cf71-4651-96fc-24ff60c8412f
Mamouei, Mohammad, Kaparias, Ioannis and Halikias, George
(2016)
A quantitative approach to the behavioural analysis of drivers in highways using particle filtering.
[in special issue: Universities' Transport Study Group UK Annual Conference 2015]
Transportation Planning and Technology, 39 (1), .
(doi:10.1080/03081060.2015.1108084).
Abstract
The analysis of driving behaviour is a challenging task in the transport field that has numerous applications, ranging from highway design to micro-simulation and the development of advanced driver assistance systems. There has been evidence suggesting changes in the driving behaviour in response to changes in traffic conditions, and this is known as adaptive driving behaviour. Identifying these changes and the conditions under which they happen, and describing them in a systematic way, contributes greatly to the accuracy of micro-simulation, and more importantly to the understanding of the traffic flow, and therefore paves the way for introducing further improvements with respect to the efficiency of the transport network. In this paper adaptive driving behaviour is linked to changes in the parameters of a given car-following model. These changes are tracked using a dynamic system identification method, called particle filtering. Subsequently, the dynamic parameter estimates are further processed to identify critical points where significant changes in the system take place.
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Accepted/In Press date: 9 October 2015
e-pub ahead of print date: 1 December 2015
Published date: 2016
Organisations:
Transportation Group
Identifiers
Local EPrints ID: 402340
URI: http://eprints.soton.ac.uk/id/eprint/402340
ISSN: 1029-0354
PURE UUID: 7f602506-5b76-4d51-810a-b14c66acfafa
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Date deposited: 07 Nov 2016 16:17
Last modified: 15 Mar 2024 03:57
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Author:
Mohammad Mamouei
Author:
George Halikias
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