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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.

As autonomous vehicles (AVs) and shared mobility gain traction, there’s an urgent need to quantify their land use impacts and reassess zoning requirements. The demand for traditional auto-serving facilities like gas stations, repair shops, and car dealerships is expected to wane, while the need for new types of spaces, such as fleet servicing centers and storage, will rise. This shift also redefines the layout and size of freight logistics areas to accommodate the integration of AV technology. Understanding these dynamics is crucial for urban planners and policymakers as they adapt zoning regulations to meet the evolving needs of urban landscapes. This research aims to provide concrete data on land use changes and identify zoning adjustments that support the transition towards a future where AVs and shared mobility solutions are prevalent, helping cities prepare for a sustainable and efficient transformation in urban land utilization.

Autonomous vehicles, both individual and shared shuttles, offer significant mobility benefits, especially for users who lack use of a personal vehicle. However, there are barriers towards emulating the key parts of human-driven shared ride services, such as efficient rendezvous and good curb location selection. Human drivers solve these problems with limited impact on operational cost and safety, but autonomous vehicles struggle with this poorly defined, yet critical step. We seek to address these barriers by characterizing effective and user-focused rendezvous strategies, thereby helping decisionmakers and service providers improve service, enhance safety, and lower operational cost.