UC Santa Barbara

Human beings have a fundamental need for social interaction according to Maslow’s hierarchy of needs. Human interaction contributes to individual psychological development and well-being, and enhances social cohesion more broadly. Among others, two scientific fields that are interested in human interaction in space and time progressed in separate paths but emerged from similar inspirations and aim at similar end goals. The first is activity analysis with a focus on developing models of activity participation and travel behavior using travel surveys. These are commonly referred to as activity-based approaches in travel demand analysis. The second is the study of human movement in space and time using geometric relationships that evolve over time and space derived from the observations of moving individuals, a tradition followed mostly by geographic information scientists. Travel behavior researchers recognize human interaction as one of the most important determinants affecting people's daily time allocation and travel. However, inter-individual interactions, particularly inter-household ones, in travel behavior research have received less attention while these interactions contribute to a significant amount of travel. Traditional travel surveys are usually limited in sample size and observation period due to expensive costs. They also often lack sufficient information regarding inter-household interactions. Besides human interactions when individuals engage in activities at specific locations, it is also crucial to consider potential interactions in shared spaces, such as when their activity spaces or paths intersect during movement. In recent years, the increased availability of diverse and fine-grained movement data offers an unprecedented opportunity to study human movement and interaction at an unparalleled level of fine granularity. However, there is a shortage of advanced computational methods to trace and understand human interactions present in fine-grained and large-volume movement data. It also remains unclear to what extent the knowledge gained from individual movement data can contribute to our understanding of collective behaviors across different socio-geographical contexts. The primary goal of this dissertation is to develop and evaluate new computational methods for identifying and quantifying spatiotemporal patterns of human daily activities and interactions, using both conventional travel diaries and massive movement data of individuals. By doing so, this dissertation explores important questions to understand heterogeneity in people’s daily activity-travel patterns and interactions across different socio-geographical spaces.