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Transportation agencies and researchers struggle with fragmented, incomplete, or unavailable mobility data, making it difficult to accurately model mobility patterns and predict future transportation demand. While various datasets exist—such as GPS trajectories, public transit records, traffic sensors, and household travel surveys—they are often disconnected, limited in scope, or proprietary, preventing cities from making fully informed planning decisions. These datasets, when properly integrated, have the potential to improve urban planning, transportation optimization, and system operations. This project aims to review available mobility data sources critical for mobility pattern analysis, build a mobility data fusion pipeline by using multiple cross-domain data sources, allowing for detailed synthesis and modeling of urban and rural activities, travel behavior, demand, and trajectories, as well as estimation/generation of network-wide travel patterns. Ultimately, this project will provide a scalable, transferable data fusion framework that agencies can use for demand prediction, transportation planning, and network optimization.
Cities would need to facilitate a multi-modal mobility platform, which provides travelers with a range of flexible mobility options, such as fixed-route or flex-route public transit, micro-transit, ride-sharing, car rentals, bike-sharing, scooters, and walking routes, some of which can be potentially served by automated vehicles. Those options altogether have potential to help residents reach businesses, employment, health care and other essential points of interest. This research acquires mobility service data to understand travel behavior in choosing mobility options, optimize design of such a platform by optimally placing mobility hubs with multiple mobility options, with the ultimate goals of improving system efficiency, increasing ridership, reducing system cost and enhancing travel safety.
AV deployments are rapidly expanding across multiple urban environments, yet current AV operations and planning are often hindered by limited access to standardized, real-time municipal data. Cities produce a wide range of data that could critically inform AV routing and decision-making, including 911 call logs, real-time street closures, construction activities, and emergency response events. However, these data are rarely available in a consistent standardized format suitable for AV consumption. The Data for Autonomous Transportation Awareness (DATA) project aims to close this gap by identifying key municipal data sources, evaluating existing data standards, and developing a scalable, standardized data-sharing framework that can be integrated into AV operational systems. Through stakeholder engagement and data standards analysis, the project will enable AVs to proactively avoid potentially problematic areas (e.g., emergency incidents or active construction zones), thereby reducing conflicts with first responders, improving roadway safety, and optimizing traffic operations. Ultimately, the DATA project will foster replicability, support widespread industry adoption, and ensure that AVs can leverage city data efficiently and consistently, avoiding a fragmented “patchwork” of standards across different regions.
Transit accessibility remains an issue for many residents. Additionally, empty buses run during off-peak or late-night hours and in suburban areas to meet coverage requirements, often resulting in inefficient resource allocation. This project will assess the potential for new mobility services (e.g., AVs and micromobility) to fill gaps in transit service and improve operational efficiency.