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
Link To Document :
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