Key Techniques for Traffic Information Acquisition Sensor Networks

Li, Zhiguo

Key Techniques for Traffic Information Acquisition Sensor Networks

Li, Zhiguo

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Publication Type:: Thesis
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Li, Zhiguo
dc.date.accessioned	2020-05-08T01:52:09Z
dc.date.available	2020-05-08T01:52:09Z
dc.date.issued	2019
dc.identifier.uri	http://hdl.handle.net/10453/140563
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	Road traffic information acquisition technologies have the capability to provide important information for intelligent transportation systems (ITS) by employing sensor networks, especially for detecting the road network information in dots, sections or large-scale areas. Sensor network plays a vital role in acquiring road traffic information of ITS. By exploiting spatial-temporal models, traffic flow models or correlation models, the traffic information of road sections and networks can be derived from the traffic data of some key points in the road for the temporal and spatial correlation. Furthermore, because of the constraints of space-time correlation, project investment and construction cost, the investigation of traffic information acquisition by employing sensor network technologies has become an important research direction of ITS. As a result, the investigation of the theories, techniques, sensors, and methodologies of traffic information acquisition sensor network (TIASN) has been a significant research topic. Based on specific requirements on real time, accuracy and completeness for traffic information acquisition, this thesis has focused on the following key challenges: (1) new algorithm to acquire traffic flow based on multi-functional geomagnetic sensor; (2) efficient optimization methods for TIASN; (3) efficient calibration method for Inertial Measurement Unit (IMU); (4) effective testing method for urban rail transit sectional passenger flow. In this dissertation, motivated by the above challenges, a thorough investigation is presented on a novel multi-parameter sensing method of traffic information by using a multi-function geomagnetic sensor (MFGS). Furthermore, in order to improve the efficiency of IMU based traffic monitoring, the calibration and its associated experimental design schemes are developed for the two-key tri-axial sensors in an IMU, i.e. tri-axial magnetometers and tri-axial accelerometers. At the end, we study the short-term prediction methods of sectional passenger flow and selects Back-Propagation (BP) neural network combined with the characteristics of sectional passenger flow itself.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/140563/2/02whole.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	Key Techniques for Traffic Information Acquisition Sensor Networks	en_AU
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Road traffic information acquisition technologies have the capability to provide important information for intelligent transportation systems (ITS) by employing sensor networks, especially for detecting the road network information in dots, sections or large-scale areas. Sensor network plays a vital role in acquiring road traffic information of ITS. By exploiting spatial-temporal models, traffic flow models or correlation models, the traffic information of road sections and networks can be derived from the traffic data of some key points in the road for the temporal and spatial correlation. Furthermore, because of the constraints of space-time correlation, project investment and construction cost, the investigation of traffic information acquisition by employing sensor network technologies has become an important research direction of ITS. As a result, the investigation of the theories, techniques, sensors, and methodologies of traffic information acquisition sensor network (TIASN) has been a significant research topic. Based on specific requirements on real time, accuracy and completeness for traffic information acquisition, this thesis has focused on the following key challenges: (1) new algorithm to acquire traffic flow based on multi-functional geomagnetic sensor; (2) efficient optimization methods for TIASN; (3) efficient calibration method for Inertial Measurement Unit (IMU); (4) effective testing method for urban rail transit sectional passenger flow. In this dissertation, motivated by the above challenges, a thorough investigation is presented on a novel multi-parameter sensing method of traffic information by using a multi-function geomagnetic sensor (MFGS). Furthermore, in order to improve the efficiency of IMU based traffic monitoring, the calibration and its associated experimental design schemes are developed for the two-key tri-axial sensors in an IMU, i.e. tri-axial magnetometers and tri-axial accelerometers. At the end, we study the short-term prediction methods of sectional passenger flow and selects Back-Propagation (BP) neural network combined with the characteristics of sectional passenger flow itself.

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