The Role That Connected and Automated Vehicles Can Play In Re-Organizing Traffic Flow: Work Zones and Emergency Services

Zou, Yun

The Role That Connected and Automated Vehicles Can Play In Re-Organizing Traffic Flow: Work Zones and Emergency Services

Zou, Yun

Permalink

Publication Type:: Thesis
Issue Date:: 2020

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download contents and abstractAdobe PDF (214.26 kB)

Adobe PDF

Download thesisAdobe PDF (17.64 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Zou, Yun
dc.date.accessioned	2020-08-31T04:39:22Z
dc.date.available	2020-08-31T04:39:22Z
dc.date.issued	2020
dc.identifier.uri	http://hdl.handle.net/10453/142435
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	The extensive progresses in computer science and communication technology in recent decades facilitate the development of the connected and automated vehicles (CAV). Since the emergence of the concept, the commercialization of CAV has been looked forward to providing an effective tool to the regulation of the freeway re-organizing traffic flow who normally initiate the evolvement of the congestion. To analyse the benefits of the CAV on traffic dispersion, the re-organizing traffic in the work zone and the incident-affected zone (under emergency services) were adopted as two cases of non-recurrent congestion, and the microscopic simulations were conducted on the basis of various car-following models and lane-change models. Furthermore, collaborative instances were added to the traditional traffic dynamic models to emulate the motions of the CAV. Trajectories data extracted from NGSIM open-access database were applied to calibrate the Bayes-classifier-based lane-change prediction model in order to better emulate the human drivers’ lane-change decision and to assist the CAV’s collaborations. With the increasing percentage of the CAV, the traffic congestion on the aforementioned bottlenecks were significantly mitigated. While CAV are proved to be capable of facilitate the cooperative lane-changes, they were also trained to refuse the lane-change request if there would be great impact on the target lanes. Although the lane-changes would inevitably impact the target lanes owing to the increasing densities and the disturbances during the lane-change motions, the simulation results showed that CAV are capable of minimizing the negative effects for the entire traffic system’s perspective.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/142435/2/02whole.pdf
dc.rights	au.edu.uts.lib/ppc
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.title	The Role That Connected and Automated Vehicles Can Play In Re-Organizing Traffic Flow: Work Zones and Emergency Services	en_AU
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The extensive progresses in computer science and communication technology in recent decades facilitate the development of the connected and automated vehicles (CAV). Since the emergence of the concept, the commercialization of CAV has been looked forward to providing an effective tool to the regulation of the freeway re-organizing traffic flow who normally initiate the evolvement of the congestion. To analyse the benefits of the CAV on traffic dispersion, the re-organizing traffic in the work zone and the incident-affected zone (under emergency services) were adopted as two cases of non-recurrent congestion, and the microscopic simulations were conducted on the basis of various car-following models and lane-change models. Furthermore, collaborative instances were added to the traditional traffic dynamic models to emulate the motions of the CAV. Trajectories data extracted from NGSIM open-access database were applied to calibrate the Bayes-classifier-based lane-change prediction model in order to better emulate the human drivers’ lane-change decision and to assist the CAV’s collaborations. With the increasing percentage of the CAV, the traffic congestion on the aforementioned bottlenecks were significantly mitigated. While CAV are proved to be capable of facilitate the cooperative lane-changes, they were also trained to refuse the lane-change request if there would be great impact on the target lanes. Although the lane-changes would inevitably impact the target lanes owing to the increasing densities and the disturbances during the lane-change motions, the simulation results showed that CAV are capable of minimizing the negative effects for the entire traffic system’s perspective.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/142435