Active transportation, human-powered transportation modes such as walking and bicycling, not only reduces carbon footprint from the transportation sector but also pro-mote health living by offering opportunities for people to build physical activity into their daily routine. To encourage active transportation through urban planning and public campaigns, it is of significance importance to infer factors that substantially influence commuters in their transportation mode choice pro-cess. This necessitates a flexible and repeatable tool that can evaluate how a policy is perceived by individual commuters,and convert their decisions into macro level understanding. This paper introduces one such effort that is specifically designed for studies of transportation mode choices in metropolitan areas. It provides results from a high-resolution data driven simulation based on high performance computing implementation of the agent-based model framework for home-to-work commute trips.The framework uses a graph-partition based technique that can leverage the interaction structure of agents within a geographic proximity and can boost the simulation execution time. Further,based on a flexible design, it can run ABM with different levels of computing resources–from multi core workstations toHPC grid. The framework has been tested in Titan Cray XK7supercomputer of the Oak Ridge Leadership Computing Facility.