NSF Award
2432337
This award will promote the progress of data science in support of the nation’s smart transportation systems through establishing a theoretical foundation for quantifying the value of information from various data sources in a transportation network. Major transportation network operators, such as the California Department of Transportation, depend on good-quality data to operate and manage their systems. Acquiring and managing data can be expensive. A crucial question arises: What data should a network operator acquire to optimize planning and operations? Priorities in data acquisition plans must align with their value for the network operator’s decision-making. Currently, there's no established theoretical framework for developing these plans. This research establishes a unifying theoretical framework for evaluating and optimizing data acquisition in a transportation network. The project will directly benefit society by facilitating effective utilization of information and leading to more sustainable and efficient transportation systems. Interdisciplinary curriculum development supported by the research findings, including modular course materials that can adapt to varying learning needs, will help better prepare and broaden participation of next-generation professionals in the smart transportation innovation ecosystem.<br/><br/>The project introduces novel concepts that quantify the value of data through the lens of robust estimation and decision processes and translates the impact on robustness to sensitivity analysis of optimal planning problems. The project centers on three tasks: Task 1 quantifies how changes in data affect estimates of network performance metrics, which will enable a network operator to identify what data is important and how the importance varies spatially and temporally. Task 2 concentrates on modeling of data acquisition and leads to stochastic optimization models that prescribe the best data acquisition plan in support of the subsequent estimation of performance metrics. Task 3 creates three case studies for the purpose of testing and validating the methods using both real-world and synthetic data.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
