Automatic detection of traffic flow parameters for ITS

By: Call Number: AIT Thesis no.RS-05-04 Contributor(s): Material type: SeriesSeries: Asian Institute of Technology. Thesis ; no. RS-05-04Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2005Description: 52 leaves : illSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 2005 Summary: The basic traffic flow parameters, such as vehicle's speed, type and traffic volume, are critical to Intelligent Transportation Systems (ITS). This study focuses on developing a new, flexible and reliable method which is able to collect the real-time traffic parameters. Our method is to convert the 2-D image sequence data to 1-D image sequence. This kind of conversion has made our process easier and faster, and also made us successful in real-time process of vehicle's signal. New algorithms are applied to identify vehicle's signal. The vehicle's travel speed, its type and the traffic volume are also measured. The new algorithms are implemented on a computer with good experimental results.
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20-AIT Publication Asian Institute of Technology Library AIT Publications AIT Thesis no.RS-05-04 (Browse shelf(Opens below)) 1 Available 30050120591283
20-AIT Publication Asian Institute of Technology Library AIT Publications AIT Thesis no.RS-05-04 (Browse shelf(Opens below)) 2 Available 30050120591309
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A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering

Thesis (M.Eng.) - Asian Institute of Technology, 2005

The basic traffic flow parameters, such as vehicle's speed, type and traffic volume, are critical to Intelligent Transportation Systems (ITS). This study focuses on developing a new, flexible and reliable method which is able to collect the real-time traffic parameters. Our method is to convert the 2-D image sequence data to 1-D image sequence. This kind of conversion has made our process easier and faster, and also made us successful in real-time process of vehicle's signal. New algorithms are applied to identify vehicle's signal. The vehicle's travel speed, its type and the traffic volume are also measured. The new algorithms are implemented on a computer with good experimental results.

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