Field Data Based Data Fusion Methodologies to Estimate Dynamic Origin-Destination Demand Matrices from Multiple Sensing and Tracking Technologies

Brief Description of Research Project Recent advances in real-time traffic sensing, including GPS data from probe vehicles, automatic vehicle identification using RFID and Bluetooth sensors, and automatic number plate recognition, provide richer data when combined with traditional O-D estimation techniques. However, the data obtained from these different sensors do not convey similar information on the traffic conditions of the network. This project seeks to develop and test a systematic methodology to integrate the different data sources, also labeled data fusion, to address the O-D estimation problem, leveraging the availability of different types of data with disparate characteristics.

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