Planning for new, shared modes of transit that will rival private vehicles in access and convenience requires a paradigm shift in the planning process. Rather than using traditional methods, we need to capture individual behavior while interacting with the systems in questions. An increasing number of studies show that combining agent-based simulation with activity-based travel demand modeling is a good approach. This approach creates a digital twin of the population of the city, with similar characteristics as their real-world counterparts. These synthetic individuals have activities to perform through the course of the day, and need to make mobility decisions to travel between activity locations. The entire transportation infrastructure of the city is replicated on a virtual platform that simulates real life scenarios. If individual behavior and the governing laws of the digital reality are accurately reproduced, large-scale mobility demand emerges from the bottom-up, reflecting the real-world incidences.