A high resolution agent-based model to support walk-bicycle infrastructure investment decisions: A case study with New York City

Active transportation modes-walk and bicycle-are central for low carbon transport, healthy living, and complete streets initiative. Building a community with amenable walk and bicycle facilities asks for smart planning and investments. It is critical to investigate the impact of infrastructure building or expansion on the overall walk and bicycle mode usage prior to making investment choices utilizing public tax money. This research developed a high performance agent-based model to support investment decisions that allows to assess the impact of changes in walk-bike infrastructures at a fine spatial resolution (e.g., block group level). We built the agent-based model (ABM) in Repast-HPC platform and calibrated the model using Simultaneous Perturbation Stochastic Simulation (SPSA) technique. The ABM utilizes data from a synthetic population simulator that generates agents with corresponding socio-demographic characteristics, and integrates facility attributes regarding walking and bicycling such as sidewalk width and total length bike lane into the mode choice decision making process. Moreover, the ABM accounts for the effect of social interactions among agents who share identical home and work geographic locations. Finally, GIS-based maps are developed at block group resolution that allows examining the effect of walk-bike infrastructure related investments. The results from New York City case study indicate that infrastructure investments such as widening sidewalk and increasing bike lane network can positively influence the active transportation mode choices. Also, the impact varies with geographic locations-different boroughs of New York City will have different impacts. Our ABM simulation results also indicate that social promotions foucsing on active transportation can positively reinforce the impacts of infrastructure changes.