DocumentCode
631818
Title
Video Driven Traffic Modelling
Author
Hailing Zhou ; Creighton, Douglas ; Lei Wei ; Gao, D.Y. ; Nahavandi, S.
Author_Institution
Centre for Intell. Syst. Res. (CISR), Deakin Univ., Geelong, VIC, Australia
fYear
2013
fDate
9-12 July 2013
Firstpage
506
Lastpage
511
Abstract
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.
Keywords
computer vision; decision making; road traffic control; road vehicles; video signal processing; Paramics traffic simulation platform; VDTM; computer vision techniques; green signal time; origin-destinations; real-world traffic behaviour simulation; road network; starting trips; traffic composition; traffic congestion; traffic decision making; traffic intervention effect evaluation; traffic management authorities; traffic parameter estimation; traffic parameter extraction; traffic signal control system optimization; traffic system model; vehicle types; video driven traffic modelling; Biological system modeling; Cameras; Computational modeling; Delays; Roads; Vehicle detection; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Intelligent Mechatronics (AIM), 2013 IEEE/ASME International Conference on
Conference_Location
Wollongong, NSW
ISSN
2159-6247
Print_ISBN
978-1-4673-5319-9
Type
conf
DOI
10.1109/AIM.2013.6584142
Filename
6584142
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