This project proposes to explore how micromobility (i.e., bike-share and scooter-share) services are enabling individuals toward a car-light lifestyle. This proposal is divided into three chapters. In the first chapter, using the North American Micromobility Panel (NAMP) survey travel diary data collected from several cities in the US and using machine learning methods for pattern detection and classification, the author is proposing to explore how micromobility is integrated with other modes in the trip-chain and is enabling individuals to perform complex trip chains. In the second chapter, individual-level and trip-based models will be developed to explore the extent to which micromobility is substituting for car, ridehail, and public transit and how different socio-demographic groups and built environment features are associated with this pattern. In the third chapter, the influence of micromobility trips on transit ridership will be modeled using both individual-level data from the NAMP survey and system-level micromobility trip and transit stop level ridership data. Also, a machine learning-based model will be developed to classify transit connection and transit substitution micromobility trips based on the trip trajectory data. The findings from this study can inform the development of future transport policies for influencing individuals towards more sustainable car-light lifestyles.