Data and Models Informing Smart Cities

This task supports the Urban Science pillar of the Department of Energy’s SMART Mobility effort through research focused on assessing the landscape of data and models in emerging smart cities. focuses on understanding the energy implications and opportunities of advanced mobility technologies and services, specifically within diverse urban contexts. More specifically, this includes exploring the linkages between transportation networks, emerging technologies and the built environment, as well as identifying the potential to enhance access to economic opportunity. These emerging mobility technologies, often referred to as automated, connected, efficient (or electrified), and shared (*this is including both vehicle and ride-sharing as the enhanced asset utilization, often in the form of app-based mobility-on demand or mobility as a service choices) (ACES), have the potential to greatly improve mobility and related quality of life in urban areas. The Urban Science team has strived to collect and integrate datasets, model, analyze, and gain unique insights from the perspective of human settlements (the ‘city’) as a living organism. Typically, the urban mobility system includes significant investments in, planning for, and adapting to potential future changes for public transit, private mobility services (such as taxis and transportation network companies, or TNCs), parking and curb management practices, emerging on-demand micromobility services for people and goods movement, e-bikes and scooters, all of which makes the urban space a dynamic ‘observatory’ for exploring data, and modeling, to informing energy efficient mobility systems implications of changing mobility technologies and systems. Foundational efforts to date, have included 1) providing a data and modeling resources report to help further integrate between transportation research and practice and with the growing reality that advanced transportation, mobility services, and infrastructure modernization are of increasing interest to cities, 2) examine data and modeling solutions within multiple cities, and 3) down-select on city case studies to work together on testing and scaling of urban science using available data and new primary data collected with inputs/validation from local city and regional partners. A focus is on factors that influence mobility options(e.g., travel time, costs, access to opportunities, and emerging mobility energy productivity metric/s) and associated energy-related impacts (e.g., fuel spent, vehicle miles traveled, costs to households) specific to new urban automated, connected, electric and shared mobility strategies developed and evaluated with partner cities. These cities comprise case examples where knowledge generated and coupled mobility-energy assessments can advance efforts across all 498 U.S. urban areas. These efforts have placed emphasis in exploring advances in urban transportation data and modeling to develop, for analysis purposes, a robust, sophisticated and practical framework supporting the ultimate goal of providing an efficient, safe and sustainable mobility system for passenger and goods movement