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Mobility connects people to opportunity. Whether they are accessing work, healthcare, shopping, education, or leisure activities, people rely on transportation as fundamental to a high quality of life.

Yet for the average American household, transportation costs are second only to housing expenses.[1] Nationally, transportation represents 1/3 of total energy use,[2] and it is a major source of emissions.[3] These factors, combined with new technologies and business models, are rapidly changing the reality of transportation, both for the movement of people and goods. Advanced mobility solutions – from new vehicle powertrains and control systems, to traffic signal control networks in urban areas, to new ride hailing services – have emerged to help people connect more efficiently and affordably to their places of employment and the goods and services they seek. But, how these myriad new technology options interact with and impact transportation at the system level is yet to be fully understood.

What energy implications and opportunities to improve mobility are inherent in these new solutions? Do gains in one area cause trade-offs in another? Where are there synergies between new technologies? The U.S. Department of Energy’s (DOE’s) Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is dedicated to answering those kinds of important questions.

The SMART Mobility Consortium is based upon a fundamental principle: Transportation is a “system of systems,” where many factors, such as vehicle electrification, connectivity, automation, traveler behavior, shared ridership, micro-mobility, freight, and transit, operate simultaneously, creating outcomes not attributable to any one element alone. Through first-of–its-kind modeling and simulation, based on a deep understanding of emerging technologies and studies of consumer impacts, the consortium has been able to shed new light on how these new mobility technologies may impact the transportation system overall.

The SMART Mobility Capstone reports summarize the consortium’s research methods, results, and insights for the following research pillars. This three year effort has shown that the net impact from different technologies are not always as expected and can often be surprising.  This research informs where additional R&D and system level understanding is needed.

Connected and Automated Vehicles (CAVs). Versatile CAV technologies can improve how vehicles interact with one another while enabling more efficiency from the powertrain technologies to which they are applied. For example, researchers developed algorithms that enable individual automated vehicles with intelligent powertrain and speed control to adapt to the road, surrounding vehicles, and green/red traffic light sequencing. When integrated with vehicle-to-infrastructure communication that provides traffic signal information, the algorithms can reduce energy/fuel consumption by up to 20%. The system-of-systems principle figures in prominently, as a particular CAV technology’s net benefits vary significantly depending on the specific scenario and technology application, illustrating the need for a robust and high-fidelity modeling workflow that accurately simulates CAV use and operation.

Mobility Decision Science. People’s transportation choices—including their adoption of advanced mobility solutions—change over their lifetimes as events related to education, career, and family reshape their values and goals. These decisions, like car ownership versus public transit, or e-commerce versus in-store shopping, all affect the larger transportation system. For example, results of one metropolitan study indicated that a reduction in per-mile ride-hail costs can motivate people to stop commuting by car and instead use ride-hailing to access public transit stations, reducing overall traffic congestion. When the cost of ride-hailing drops very low, however, many people choose ride-hail for their entire commute and much of the congestion improvement is negated.

Multi-Modal Freight. Emerging freight movement technologies and continued e-commerce growth are just a couple of the advanced mobility factors poised to impact the efficiency of transferring goods through long-haul, intercity freight movement, and intracity last-mile delivery. For example, while e-commerce is expected to cause a large increase in last-mile delivery of goods, researchers found—after accounting for shopping trip reductions and vehicle technology improvements—an overall reduction in vehicle miles traveled (VMT) and energy use as a result of increased e-commerce.

Urban Science. Whether commuting, running errands, or just passing through, city travelers interact with the infrastructure of the urban setting and base their transportation behavior on physical cues and social norms. Researchers studied how advanced mobility solutions can leverage these linkages between transportation networks and the built environment and enhance access to opportunity. For example, one study indicated that using real-time feedback controls to time traffic signals at networked intersections reduced average travel delays by up to 40% with 100% CAV penetration, compared to existing pre-timed controls.

Advanced Fueling Infrastructure. Energy-efficient future mobility demands new fueling/charging infrastructure. Research into the associated costs, benefits, and requirements indicates that tradeoffs abound—no single solution can satisfy a diverse electric vehicle (EV) market ranging from medium- and heavy-duty trucks to automated ride-hailing fleets. Instead, demand should guide charging network design, with solutions that go beyond simply adding more stations and even include potentially maintaining some slow-charging locations in contrast to switching over all charging stations to faster rates.

SMART Mobility Modeling Workflow. To probe the “system-of-systems” principle, DOE researchers advanced the state-of-the-art in transportation system modeling and simulation with a novel, multi-fidelity end-to-end modeling workflow. Varying inputs include land use, EV charging, passenger and goods movement, system-level control and traveler behavior. Using agent-based transportation models, researchers can quantify the impact of different mobility technologies in various scenarios across numerous metrics, including VMT, congestion, vehicle hours traveled, energy, cost, and greenhouse gases. These metrics can then be used to compute an aggregate Mobility Energy Productivity metric —the measurement of an emerging mobility technology’s system-level potential to more efficiently and affordably connect people and goods to opportunities and needs.

The SMART Mobility Consortium is an initiative of the EERE Vehicle Technologies Office. The consortium includes researchers from Argonne National Laboratory, Idaho National Laboratory, Lawrence Berkeley National Laboratory, the National Renewable Energy Laboratory, and Oak Ridge National Laboratory.



[1] U.S. Department of Labor, Bureau of Labor Statistics, News Release, USDL-19-1593, September 10, 2019.

[2] Transportation Energy Data Book 36th Edition, ORNL, 2017. Table 2.1. U.S. Consumption of Total Energy by End-use Sector, 1973-2017.

[3] U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2018,  EPA 430-R-20-002, April 13, 2020.