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On traffic disruptions : event detection from visual data and Bayesian congestion games

Author(s)
Liu, Jeffrey,Ph.D.Massachusetts Institute of Technology.
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Other Contributors
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering.
Advisor
Saurabh Amin.
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MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
Road traffic is often subject to random disturbances due to weather, incidents, or special events. Effectively detecting and disseminating information about disturbances is a key goal of modern, "smart" infrastructure. Toward this, this dissertation investigates two related questions. First, how can traffic managers better utilize existing traffic cameras to automatically identify traffic disturbances? Second, how can we model different aspects of information-such as human misperception or ignorance of other's information-and their effects on the travelers' route choices? Part I addresses analyzing unstructured, sequential image data, such as traffic CCTV footage, with a novel, semantics-oriented approach based on natural language and semantic features. The approach extracts structured, human-interpretable "topic signals" from distributions of common object labels, which correspond to physical processes depicted in the footage.
 
Changes and anomalies in these topic signals are used to identify notable events in weather conditions and traffic congestion. This is demonstrated on a new, real-world dataset collected from Boston freeway CCTV footage. In notable event detection, the use of topic signal representation outperforms the use of any individual label signal. Part II addresses game theoretic modeling of informational effects on travelers' route choices. It considers both access and accuracy of information about the network state, as well as the perception of other's information. It introduces the Subjective Bayesian Congestion Game (BCG), which models a broader set of player beliefs than those allowed by the conventional common prior assumption (Objective BCG). This enables modeling of uncertainty about other's information, such as when one population is unaware of information services.
 
Analytical solutions are provided for a stylized configuration of the Subjective BCG, and a numerical solver is provided for more general configurations. Compared to the Objective BCG, the Subjective BCG has qualitatively distinct solutions and costs, indicating that the perception of other's information significantly affects equilibrium route choices.
 
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 123-131).
 
Date issued
2019
URI
https://hdl.handle.net/1721.1/123189
Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Publisher
Massachusetts Institute of Technology
Keywords
Civil and Environmental Engineering.

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