Type of Document	Master's Thesis
Author	Kondagari, Shourie
Author's Email Address	skonda1@lsu.edu
URN	etd-06142006-164510
Title	A Probabilistic Approach for Modeling and Real-Time Filtering of Freeway Detector Data
Degree	Master of Science in Civil Engineering (M.S.C.E.)
Department	Civil & Environmental Engineering
Advisory Committee	Advisor Name Title Sherif Ishak Committee Chair Brian Wolshon Committee Member Chester G. Wilmot Committee Member
Keywords	real-time data filtering loop detectors detector errors artificial neural networks
Date of Defense	2006-05-18
Availability	unrestricted
Abstract Traffic surveillance systems are a key component for providing information on traffic conditions and supporting traffic management functions. A large amount of data is currently collected from inductive loop detector systems in the form of three macroscopic traffic parameters (speed, volume and occupancy). Such information is vital to the successful implementation of transportation data warehouses and decision support systems. The quality of data is, however, affected by erroneous observations that result from malfunctioning or mis-calibration of detectors. The open literature shows that little effort has been made to establish procedures for screening traffic observations in real-time. This study presents a probabilistic approach for modeling and real-time screening of freeway traffic data. The study proposes a simple methodology to capture the probabilistic and dynamic relationships between the three traffic parameters using historical data collected from the I-4 corridor in Orlando, Florida. The developed models are then used to identify the probability that each traffic observation is partially or fully invalid.
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