Video driven traffic modelling

Zhou, Hailing, Creighton, Douglas, Wei, Lei, Gao, David Yang and Nahavandi, Saeid 2013, Video driven traffic modelling, in AIM 2013 : Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, IEEE, Piscataway, N.J., pp. 506-511, doi: 10.1109/AIM.2013.6584142.

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Title Video driven traffic modelling
Author(s) Zhou, HailingORCID iD for Zhou, Hailing
Creighton, DouglasORCID iD for Creighton, Douglas
Wei, LeiORCID iD for Wei, Lei
Gao, David Yang
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Conference name Advanced Intelligent Mechatronics. IEEE/ASME International Conference (2013 : Wollongong, New South Wales)
Conference location Wollongong, New South Wales
Conference dates 9-12 Jul. 2013
Title of proceedings AIM 2013 : Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE/ASME International Conference on Advanced Intelligent Mechatronics
Start page 506
End page 511
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary We propose Video Driven Traffic Modelling (VDTM) for accurate simulation of real-world traffic behaviours with detailed information and low-cost model development and maintenance. Computer vision techniques are employed to estimate traffic parameters. These parameters are used to build and update a traffic system model. The model is simulated using the Paramics traffic simulation platform. Based on the simulation techniques, effects of traffic interventions can be evaluated in order to achieve better decision makings for traffic management authorities. In this paper, traffic parameters such as vehicle types, times of starting trips and corresponding origin-destinations are extracted from a video. A road network is manually defined according to the traffic composition in the video, and individual vehicles associated with extracted properties are modelled and simulated within the defined road network using Paramics. VDTM has widespread potential applications in supporting traffic decision-makings. To demonstrate the effectiveness, we apply it in optimizing a traffic signal control system, which adaptively adjusts green times of signals at an intersection to reduce traffic congestion.
ISBN 1467353205
Language eng
DOI 10.1109/AIM.2013.6584142
Field of Research 080110 Simulation and Modelling
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE/ASME
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