Latest revision as of 00:30, 2 February 2021

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.

Original document

The different versions of the original document can be found in:

• http://openaccess.city.ac.uk/13114/1/Mamouei%20et%20al%20FINAL.pdf

• https://eprints.soton.ac.uk/402640

• https://openaccess.city.ac.uk/id/eprint/13114/1/Mamouei%20et%20al%20FINAL.pdf

• https://openaccess.city.ac.uk/id/eprint/6245/1/4-35.pdf

• http://www.tandfonline.com/doi/pdf/10.1080/03081060.2015.1108084,

http://dx.doi.org/10.1080/03081060.2015.1108084

• https://www.tandfonline.com/doi/full/10.1080/03081060.2015.1108084,

https://eprints.soton.ac.uk/402340,

https://openaccess.city.ac.uk/id/eprint/13114,

https://ideas.repec.org/a/taf/transp/v39y2016i1p78-96.html,

https://trid.trb.org/view.aspx?id=1377512,

https://academic.microsoft.com/#/detail/1919444760

• [ ]