The detection and localization of traffic congestion for highway traffic systems using hybrid estimation techniques
Abstract
The highway traffic conditions across the United States are in a grim situation caused by daily congestion and daily accidents. The current highway systems are used for daily commuting, transportation of goods and interstate travels. The motorist advocacy group AAA reports that accidents in the United States, cost $164.2 billion each year, with costs derived from medical care, emergency and police services, property damage, lost productivity and quality of life, while congestion costs the nation $67.6 billion each year. About 43,000 people die on the nation's roadway each year. It is then essential that we provide solutions to these problems or at least ways to alleviate the magnitude of their occurrences. So, we first need to come up with ways to quantify traffic data and highway operations in order to channel a path to determine a solution. We then need to find ways to alleviate congestion that will also in turn help in reducing the number of accidents caused by congestion. In order to do that, we propose a method of detecting a congestion onset and its propagation along a highway segment. We have developed a stochastic linear hybrid system model that will be used to represent those congestion scenarios. Using a state-dependent-hybrid estimation algorithm, we estimate the states of the highway that will provide us with the traffic congestion information. The performance of the algorithm is analyzed using the correct detection and identification and false alarm rate indices, time-to-detection as well as the run time for each simulation. We use a set of constructed data that will represent the congestion onset and propagation scenarios. The validation of the algorithm is done using real traffic data obtained from highway I-405 S in California.
Degree
M.S.A.A.
Advisors
Hwang, Purdue University.
Subject Area
Aerospace engineering|Civil engineering
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