When cities tackle transportation problems, they create simulation models in which travelers move about cities: going to work, dropping children off at school, running errands. Typically these simulations are based on survey data that is expensive, coarse, and infrequently collected. As the pace of transportation innovation accelerates, cities need more accurate, real-time data to effectively inform planning decisions.
By relying on high fidelity data, new approaches to modeling can lead to faster policies and greater consensus. Location-based data can be anonymized to protect consumer privacy and then made useful to urban planners, leading to models that are informed with fresher, cheaper, and more precise data than ever before. If cities can improve data quality, reduce planning time, and extract good ideas from the community, we can create a future in which governments are more nimble, responsive, and effective.