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Big data products offer a new paradigm to understand and analyze human mobility patterns, a primary interest of long-range transportation planners. However, it remains unclear how widely these datasets are utilized by planners and to what extent they influence decision-making. We present the perspectives of more than 50 planners from MPOs across the United States. While we found a range of use cases, there was also a tendency to focus on a narrow set of applications.Transparency,regulation, andlegitimacyemerged as the primary factors influencing adoption decisions. 

Urban population growth has significantly complicated the management of mobility systems, demanding innovative tools for planning. Generative Crowd-Flow  (GCF) models, which leverage machine learning to simulate urban movement patterns, offer a promising solution but lack sufficient evaluation of their fairness–a critical factor for equitable urban planning. We present an approach to measure and benchmark the fairness of GCF  models by developing a first-of-its-kind set of fairness metrics specifically tailored for this purpose. Using observed flow data, we employ a stochastic biased sampling approach to generate multiple permutations of Origin-Destination  datasets, each demonstrating intentional bias. Our proposed framework allows for the comparison of multiple GCF  models to evaluate how models introduce bias in outputs. Preliminary results indicate a tradeoff between model accuracy and fairness, underscoring the need for careful consideration in the deployment of these technologies. To this end, this study bridges the gap between human mobility literature and fairness in machine learning, with potential to help urban planners and policymakers leverage GCF  models for more equitable urban infrastructure development.