Behavior Science Methods for Active Transportation Demand Management

Transportation sustainability is one of the most widely discussed topics by transportation agencies across the U.S. as the transportation sector generates the largest share of greenhouse gas emissions, accounting for 27% of 2020 greenhouse gas emissions. Effective measures should be taken in the transportation industry to reduce carbon dioxide emissions the vulnerability of the system and the people served by these systems to climatic change. Past literature, however, rarely examined how to enhance the efficiency of behavior change tactics and improve equity while reducing costs by assessing people's perceptions, motivations, and irrationality in their decision-making processes. The findings of this dissertation could facilitate the development of a human-centered and cost-effective framework for behavior change that state DOTs can utilize to establish demand management plans. By utilizing cutting-edge behavior measurement tools, including online surveys and field experiments, this dissertation aims to investigate the relationship between individual and contextual factors and the behavior to switch modes of transportation. The target audience of the transportation demand management programs, their perceived barriers to active transportation (i.e., any self-propelled, human-powered mode of transportation, such as walking or bicycling) and shared transportation (i.e., transportation services and resources that are shared among users), as well as the dynamic impact of their cognitive biases on mode shift, were examined in-depth using behavior science methodologies. Advanced statistical and machine learning algorithms were utilized to interpret the findings. This dissertation's outcomes include a comprehensive framework for travel behavior investigation, demographic and geological characterization of the target audiences, and a deeper understanding of how mental and physical biases keep people from switching to more sustainable modes of transportation. This work contrib