UC Irvine

In this research, we propose a series of models designed to take advantage of the availability of data--both structured and unstructured--from a variety of sources ranging including passive data, questionnaires, and social media data to analyze underlying patterns and trends in travel and activity behavior, and to provide results that support enhancements both in transportation planning and the application of programming to support such efforts.

First, we introduce a framework for automatically inferring the travel modes and trip purposes of human movement when tracked by a GPS device. We utilize a multiple changepoints algorithm to divide trajectories into segments using only speed data. Then, Random Forest is used to classify segments into moving and not-moving types. For moving segments, travel mode (car, bus, train, walk, and bike) is predicted. Next, multiple machine learning algorithms are employed, validated, and tested to identify the most suitable model for inferring trip purposes. The overall accuracy for prediction is over 80% on the testing set, both with and without data on socio-demographic variables. The model also predicts "shop" trips with an accuracy of 92.1%, while its accuracy for "go home" and "studying" trips reaches 100%.

Additionally, we utilize the classification results in the first stage of research to compare households' travel patterns from before a new light rail transit line began service to two periods of time after service began. Our results indicate that, although the average of activity duration varies significantly over days of week and waves, the random effect of these two factors on activity duration was minor; time of day contributed over one third of the total variance in the duration.

Finally, the dissertation demonstrates uses of Twitter data as a potentially important data source to understand comments, criticisms, and responses about light rail in Los Angeles. The results of information flow analysis, sentiment estimation, topic modeling, and its application can be useful for exploring trends among commuters and how their sentiment changed according to the light rail line they used, the time of day, and the day of the week.