Integrating Probe Vehicles and Stationary Detector Data to Construct Accurate Cumulative Curves to Study Bottlenecks
Date of Award
Master of Science (MS)
Civil & Environmental Engineering
R. Michael Robinson
Call Number for Print
Special Collections LD4331.E542 A58 2012
Advancements in sensors technologies have given researchers and practitioners access to an immense amount of traffic data from multiple types of sensors. However, because of the diversity of the data types, developing a relationship between the different data sources can be challenging. To further complicate the issue, the presence of error or noise makes it difficult to infer any reliable conclusions from the data. To develop a relationship between different data sources, a new methodology is proposed in this thesis for fusing stationary detector data and probe vehicle data to construct an accurate cumulative curve. To remove or to reduce any error in the stationary detector data (which can be found in the form of bias or white noise), the fusion of data was performed by minimizing the difference between the arrival curve (determined from a stationary detector) and the arrival times of the probe vehicles. A nonlinear optimization tool was used to correct for the unknown bias. To demonstrate the application of the methodology, sample data were generated from VISSIM, a microscopic traffic simulation. VISSIM was used to create a link from which stationary and probe vehicle data were generated. The sensor data from the simulation were artificially contaminated with bias and white (Gaussian) noise to reflect error that can be present in real-world detectors. To analyze the effects of error and other potentially known or unknown parameters, a factorial design was performed. The analyses were performed based on data from five different VISSIM runs. Within each VISSIM run, thirty random replications were generated for each different level of noise. The analysis of the result of the fact01ial design indicates that the proposed methodology was able to fuse the stationary and probe vehicle data effectively. The proposed methodology was then applied to field data collected from the Hampton Roads Bridge-Tunnel (HRBT) corridor. However, due to lack of detectors on the ramps along the corridor and gross error in the traffic data, the cumulative curve for the HRBT could not be constructed.
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Anuar, Khairul A..
"Integrating Probe Vehicles and Stationary Detector Data to Construct Accurate Cumulative Curves to Study Bottlenecks"
(2012). Master of Science (MS), Thesis, Civil & Environmental Engineering, Old Dominion University, DOI: 10.25777/09r4-6e63