Optimizing urban mobility: A data-driven approach to strategic Mobility Hub placement
ONGOING | Equitable Use, Access, and Impacts | Land Use and Urban Planning | Systems Analysis and Optimization
Transportation Technologies:
Automated Vehicles, Business Models, Carsharing, Demand-Responsive Transit & Microtransit, Freight/Goods Movement Options, Heavy Duty Applications of Automated Vehicles, Micromobility, Mobility-as-a-service, On-Demand Delivery Services, Passenger Mobility Options, Ridehail/Transportation Network Companies, Universal Basic Mobility, Vehicle TechnologiesImpacts:
Education and Workforce, Energy and Environment, Health, Land Use, Municipal Budgets, Safety, Social Equity, Transportation Systems Operations (and Efficiency)Problem Statement
Cities would need to facilitate a multi-modal mobility platform, which provides travelers with seamless access to a range of emerging mobility options, such as fixed-route or flex-route public transit, micro-transit, ride-sharing, car rentals, bike-sharing, scooters, moped, and walking routes. Those options altogether have potential to improve accessibility to essential resources regarding employment, health care and food. 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 accessibility, sustainability and efficiency for underserved populations.Outcomes & Deliverables
– Open-source codes of multimodal network construction and multimodal routing uploaded to Github for public download
– Open-source codes optimizing mobility hubs for general networks uploaded to Github for public download
– Two conference presentations
– Two journal publications
– Policy brief detailing recommendations for policymakers
Researchers
Sean Z Qian
Professor
Carnegie Mellon University
Professor
Carnegie Mellon University
Jiaqi Ma
Associate Professor
UCLA
Associate Professor
UCLA