The fundamental research question that was addressed with the project is whether a simple, continuously collected GPS sequence can be used to accurately measure human behavior. We applied Hybrid Dynamic Mixed Network (HDMN) modeling techniques to learn behaviors given an extended GPS data stream. This research project was designed to be an important component of a much larger effort. Unfortunately, the promised funding from a commercial sponsor for the larger project did not materialize, and so we did not have the resources to deploy a prototype personal travel assistant system. Work focused on developing the HDMN model. The learning and inference steps using the HDMN model were much slower than would be acceptable in an operational Personal Travel Assistant (PTA) system. We conducted research into alternate formulations that would improve convergence, handle noisy data more robustly and reduce the need for human intervention. This report describes how this project’s results fit into the larger research context, details the work done for this UCTC grant, and outlines future directions of research based on the findings of this project.

I arrived here at the UC Transportation Center just nine months ago. A former lawyer and aspiring writer, I had only a layman's knowledge of transportation systems, mostly based on my personal experiences. 

Growing up in Hilo, Hawaii, I thought traffic jams meant having to circle the parking lot twice to find a space. No one worried about ozone or took cars in for smog checks. Every desirable destination - shopping malls, movie theaters, beaches, even downtown - was within a few minutes' drive.