A framework for processing connected vehicle data in transportation planning applications
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This thesis presents a framework to process connected vehicle data into a format that is practical for implementation in the transportation planning field. Whereas prior research on connected vehicles has used theoretical models or small data samples for analysis, this study uses the largest public connected vehicle dataset currently available – the Sample Data Environment from the Safety Pilot Model Deployment project out of Ann Arbor, Michigan. This data includes basic safety messages and driving data for 2800 vehicles over two months. An algorithm to process basic safety message data into a trip level dataset is presented. This thesis also includes a process for spatial aggregation of trips into origin and destination zones using a hexagonal grid. These processes are implemented through the combination of a variety of open-source tools including Hadoop and PostgreSQL. Excerpts from the processed data are provided as well as sample analysis applications for the trip and spatial data. Recommendations and guidance are provided on handling the details of such an immense dataset. Since similar future vehicle-to-vehicle communications datasets are likely, it is imperative to develop methods to process and analyze this rich data effectively.