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dc.contributor.authorAl-Tarawneh, Mu'ath
dc.description.abstractRecently, adding more lanes becomes less and less feasible, which is no longer an applicable solution for the traffic congestion problem due to the increment of vehicles. Using the existing infrastructure more efficiently with better traffic control and management is the realistic solution. An effective traffic management requires the use of monitoring technologies to extract traffic parameters that describe the characteristics of vehicles and their movement on the road. A three-dimension glass fiber-reinforced polymer packaged fiber Bragg grating sensor (3D GFRP-FBG) is introduced for the traffic monitoring system. The proposed sensor network was installed for validation at the Cold Weather Road Research Facility in Minnesota (MnROAD) facility of Minnesota Department of Transportation (MnDOT) in MN. A vehicle classification system based on the proposed sensor network has been validated. The vehicle classification system uses support vector machine (SVM), Neural Network (NN), and K-Nearest Neighbour (KNN) learning algorithms to classify vehicles into categories ranging from small vehicles to combination trucks. The field-testing results from real traffic show that the developed system can accurately estimate the vehicle classifications with 98.5 % of accuracy. Also, the proposed sensor network has been validated for low-speed and high-speed WIM measurements in flexible pavement. Field testing validated that the longitudinal component of the sensor has a measurement accuracy of 86.3% and 89.5% at 5 mph and 45 mph vehicle speed, respectively. A performed parametric study on the stability of the WIM system shows that the loading position is the most significant parameter affecting the WIM measurements accuracy compared to the vehicle speed and pavement temperature. Also the system shows the capability to estimate the location of the loading position to enhance the system accuracy.en_US
dc.publisherNorth Dakota State Universityen_US
dc.rightsNDSU policy 190.6.2en_US
dc.titleTraffic Monitoring System Using In-Pavement Fiber Bragg Grating Sensorsen_US
dc.typeDissertationen_US
dc.typeVideoen_US
dc.date.accessioned2020-09-18T17:08:35Z
dc.date.available2020-09-18T17:08:35Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/10365/31539
dc.subjectFBG sensoren_US
dc.subjectfiber optic sensoren_US
dc.subjectflexible pavementen_US
dc.subjecttraffic monitoringen_US
dc.subjectvehicle classificationen_US
dc.subjectweigh in motionen_US
dc.rights.urihttps://www.ndsu.edu/fileadmin/policy/190.pdfen_US
ndsu.degreeDoctor of Philosophy (PhD)en_US
ndsu.collegeEngineeringen_US
ndsu.departmentCivil and Environmental Engineeringen_US
ndsu.programCivil Engineeringen_US
ndsu.advisorHuang, Ying


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