Vision Zero, a popular initiative aiming to eliminate traffic fatalities and injuries, emphasizes the need for more traffic data analysis to better predict and prevent future accidents. By working with location data streams, cities can understand past mobility and movement patterns, which is crucial in identifying areas that require attention. A cost-effective alternative to traditional methods, this approach uses four types of location data to estimate the impact of a hypothetical car ban along a busy street segment. The analysis aims to find the optimal time for the car ban, identify drivers and pedestrians whose travel would be disrupted, and estimate commercial activity generated from the ban. The study used open municipal data and foot traffic derived from anonymized GPS mobile data to analyze traffic patterns on Mass Ave in Cambridge, Massachusetts. By aggregating and joining different datasets, the analysis revealed insights into mobility patterns, including the impact of a car ban on local residents and businesses. The study also identified commercial micro-regions and quantified spend and mobility patterns, providing estimates of lift in store visits and potential revenue. This approach can be replicated to address other traffic-related issues, offering new solutions to old problems.