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Chapter 3:
Bayesian Inference for Traffic Management
Book: Sensor and Data Fusion for Intelligent Transportation Systems
Author(s): Lawrence A. Klein
Published: 2019
https://doi.org/10.1117/3.2525400.ch3
Abstract
Bayesian inference is widely applied for detection, classification, and identification of objects and events that occur in conjunction with traffic management strategies. The applications include travel-time estimation, automatic incident detection, and decision support. This chapter describes the underlying principles of the technique, the assumptions typically encountered in its applications, and four examples that illustrate its exploitation for traffic management. The examples presented are identification of vehicle type using emission spectra data collected by a sensor; incident detection using an influence diagram and joint sensor report with data from roadway, cell phone, and truck radio reports; truck classification using a multiple-classifier system; and maneuver-based trajectory prediction and criticality assessment through causal Bayesian networks.
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