Impact of advanced technologies at the transportation system level on mobility, energy, emissions, cost, EEJ

Research to increase the energy efficiency of the transportation system has traditionally emphasized the importance of vehicles and vehicle components. Today, however, dramatic changes in transportation and communication technologies—from vehicle electrification and automation to shared mobility, e-commerce, and telecommuting—are disrupting the transportation system and increasing the need to understand the systemic energy impacts of human behaviors in the context of so many new choices.

The U.S. Department of Energy (DOE) Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multi-year, multi-laboratory collaborative dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The SMART Mobility Modeling Workflow has been developed to evaluate new transportation technologies such as connectivity, automation, sharing, and electrification through multi-level systems analysis that captures the dynamic interactions between technologies. By integrating multiple models across different levels of fidelity and scale (i.e., individual vehicles to entire metropolitan areas), the Workflow yields insights about the influence of new mobility and vehicle technologies at the system level.

SMART Mobility Workflow

Argonne implementation of the workflow includes multiple tools:

  • POLARIS: An agent-based, mesoscopic transportation system simulation tool used to generate travel demand and traffic flow at the metropolitan area level.
  • Aimsun: A traffic microsimulation tool used to generate traffic flow fundamental diagrams (i.e., the relationships between the speed, flow rate, and density of vehicles on a given transportation link) for different market penetrations of AVs, used as an input to POLARIS.
  • RoadRunner: A multi-vehicle simulation tool used to estimate the impact of new control algorithms at the vehicle and component levels using Autonomie powertrain models and vehicle aerodynamic data.
  • UrbanSim: A microsimulation platform for forecasting the growth and development of metropolitan regions over decade-scale time horizons, including changes to land use, demographics, and employment. Outputs are provided to POLARIS for scenario runs.
  • Electric Vehicle Infrastructure Projection (EVI-Pro): A tool used to determine public charging station location and type based on plug-in electric vehicle (PEV) trip demand generated by POLARIS.
  • Stochastic Vehicle Trip Profile (SVTrip): A tool to synthesize vehicle speed dynamics (i.e., acceleration, deceleration) from detailed POLARIS information (i.e., average vehicle speed per link, road type, potential stop duration) for use by Autonomie to properly estimate vehicle energy consumption.
  • Autonomie: A high-fidelity vehicle simulation tool used to estimate vehicle energy consumption from SVTrip speed traces across vehicle classes, powertrains, and component technologies.
  • MEP: A computation using outputs from UrbanSim, POLARIS, and Autonomie in an aggregate calculation that includes the energy, time, and cost of mobility.

The combination of tools in the SMART Mobility workflow allows for the analysis of complex future mobility and vehicle technology scenarios and to characterize the impacts of those scenarios in fine-grained detail over multiple metrics of interest including congestion, economic impact, accessibility and mobility, emissions and energy use, productivity and environmental justice and equity.