Introduction and Overview of Smart Mobility@Ames and the Smart Urban Integrated Test Environment
Introduction and overview of Smart Mobility@Ames
Smart Mobility↗
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SMART Mobility. Modeling Workflow Development, Implementation, and Results Capstone Report
The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the SMART Mobility Modeling Workflow effort. The SMART Mobility Modeling Workflow was 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, the Workflow yields insights about the influence of new mobility and vehicle technologies at the system level. For information about the other Pillars, please refer to the relevant pillar’s Capstone Report.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
SMART Mobility. Mobility Decision Science Capstone Report
The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Mobility Decision Science Pillar. The Mobility Decision Science Pillar sought to fill gaps in existing knowledge about the human role in the mobility system including travel decision-making and technology adoption in the context of future mobility. The objective was to study how underlying preferences, needs, and contextual factors might constrain or hasten future transportation system scenarios. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SMART Mobility. Advanced Fueling Infrastructure Capstone Report
The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Advanced Fueling Infrastructure Pillar. This Pillar investigated the charging infrastructure needs of electric ride-hailing and car-sharing vehicles, automated shuttle buses, and freight-delivery truck fleets. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar’s Capstone Report.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SMART Mobility. Multi-Modal Freight Capstone Report
The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Multi-Modal Freight Pillar. The Multi Modal Freight Pillar’s objective is to assess the effectiveness of emerging freight movement technologies and understand the impacts of the growing trends in consumer spending and e-commerce on parcel movement considering mobility, energy, and productivity. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SMART Mobility. Connected and Automated Vehicles Capstone Report
The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Connected and Automated Vehicles (CAVs) Pillar. This Pillar investigated the energy, technology, and usage implications of vehicle connectivity and automation and identified efficient CAV solutions. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar’s Capstone Report.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SMART Mobility. Urban Science Capstone Report
The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The Urban Science (US) Pillar focuses on maximum-mobility and minimum-energy opportunities associated with emerging transportation and transportation-related technologies specifically within the urban context. Such technologies, often referred to as automated, connected, efficient (or electrified), and shared (ACES), have the potential to greatly improve mobility and related quality of life in urban areas. Although all the SMART Mobility research pillars share some commonalities, Urban Science strives to model, analyze, and gain insights from the perspective of human settlements (the “city”) as a living organism. This is especially critical as the United States is one of the most urbanized countries, and as more and more of the global population migrates to urban areas.1 The urban mobility system consists of a complex network that reaches well beyond roads and vehicles and includes significant investments in public transit, private mobility services (such as taxis and transportation network companies, or TNCs), significant parking reserves, and curb management practices, not to mention the abundance of emerging on-demand micromobility services for the movement of people and goods such as e-bikes and scooters, which make the urban space a dynamic laboratory for mobility. Urban spaces also concentrate employment, markets, services, and attractions, which are the destinations for most trips. The concentration of human activities and ensuing density also creates the need and emphasis for space efficiency in urban environments, which is often less of a constraint in suburban or rural contexts. This mixture of transportation and mobility infrastructure and practice, combined with global urbanization trends, make urban spaces a critical focus of research for developing energy-efficient mobility systems (EEMS).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic
This article presents the design, implementation, and use cases of the Chattanooga Digital Twin (CTwin) towards the vision for next-generation smart city applications for urban mobility management. CTwin is an end-to-end web-based platform that incorporates various aspects of the decision-making process for optimizing urban transportation systems in Chattanooga, Tennessee, to reduce traffic congestion, incidents, and vehicle fuel consumption. The platform serves as a cyberinfrastructure to collect and integrate multi-domain urban mobility data from various online repositories and Internet of Things (IoT) sensors, covering multiple urban aspects (e.g., traffic, natural hazards, weather, and safety) that are relevant to urban mobility management. The platform enables advanced capabilities for: (a) real-time situational awareness on traffic and infrastructure conditions on highways and urban roads, (b) cyber-physical control for optimizing traffic signal timing, and (c) interactive visual analytics on big urban mobility data and various metrics for traffic prediction and transportation performance evaluation. The platform is designed using a multi-level componentization paradigm and is implemented using modular and adaptive architecture, rendering it as a generalizable and extendable prototype for other urban management applications. We present several use cases to demonstrate CTwin's core capabilities for supporting decision-making in smart urban mobility management.
33 ADVANCED PROPULSION SYSTEMS↗
Smart Mobile Sensor Platform Development for Radiological Mapping of Large-scale Areas [Slides]
Abstract not provided.
61 RADIATION PROTECTION AND DOSIMETRY↗
A real-time energy and cost efficient vehicle route assignment neural recommender system
Here, this paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e. using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-k vehicles star ranking system, and (2) engage in more general assignment problems where n vehicles need to be deployed over m (m ≤ n) trips. This new assignment system has been deployed and integrated into the POLARIS. Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium (SMART, 2024).
Energy consumption↗
Micromobility Integrated Transit and Infrastructure for Efficiency (MITIE)
Nearly omnipresent in many cities of all sizes across the United States, micromobility vehicles-e-scooters, manual bicycles, e-bicycles, and larger seated electric scooters-are notably missing from SMART Mobility research. This project aims to expand the spectrum of modes currently being researched within SMART Mobility by exploring micromobility as an important tool toward meeting energy-efficient mobility goals. It expands on findings from SMART Mobility 1.0 that revealed preferences to reduce transportation-related expenses through use of a network of mobility-as-a-service (MaaS) and other shared mobility options, and builds on findings from a 2019 Vehicle Technology Analysis Program (VTAP) funded micromobility project conducted by our team. We will explore multiple facets of micromobility, including behavior and decision-making, the integration of micromobility within transportation infrastructure, energy estimates, and operations. Guiding research questions include: (1) What are the potential energy savings from low, medium, and high market penetration of micromobility (in passenger, multimodal, and freight domains)? (2) Which scenarios for micromobility use and related enablement of increased public transit use should be modeled/considered in the SMART 2.0 Workflow? (3) To what degree can micromobility supplement/complement transit system operations? (4) What are people's preferences towards micromobility? How do preferences vary across various sociodemographic segments? How can this knowledge inform operations? (5) What are optimal strategies to attain high user adoption and shift users toward more energy-efficient mode choices in terms of micromobility operation? How do these strategies affect energy savings, person-miles traveled, lifecycle energy use, and adoption rates? These questions will be addressed through applied research in five project emphasis areas: (1) Energy estimates of micromobility for Workflow scenarios: Expand and refine previous micromobility work to augment the Workflow approaches to modeling urban travel. (2) Multimodal connection with transit: Utilizing Mobility-Energy Productivity (MEP) tools to evaluate multimodal travel patterns enabled by micromobility, including assessing how to reduce barriers of inequity of access to mobility options and destinations. (3) Mode choice, induced demand, and infrastructure: Understanding the mode shift induced through micromobility to inform energy impact analysis. (4) Energy optimization of micromobility operations: Identification of micromobility operations parameters and development of operations scenarios to better understand present-day micromobility operations for integration into the Workflow, in partnership with BEAM and POLARIS modeling teams. (5) Micro-freight: Characterize the current state of micro-freight activities, including energy effects and geospatial analyses, to inform Workflow.
ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
Micromobility Integrated Transit and Infrastructure for Efficiency (MITIE)
Nearly omnipresent in many cities of all sizes across the United States, micromobility vehicles-e-scooters, manual bicycles, e-bicycles, and larger seated electric scooters-are notably missing from SMART Mobility research. This project aims to expand the spectrum of modes currently being researched within SMART Mobility by exploring micromobility as an important tool toward meeting energy-efficient mobility goals. It expands on findings from SMART Mobility 1.0 that revealed preferences to reduce transportation-related expenses through use of a network of mobility-as-a-service (MaaS) and other shared mobility options, and builds on findings from a 2019 Vehicle Technology Analysis Program (VTAP) funded micromobility project conducted by our team. We will explore multiple facets of micromobility, including behavior and decision-making, the integration of micromobility within transportation infrastructure, energy estimates, and operations. Guiding research questions include: 1) what are the potential energy savings from low, medium, and high market penetration of micromobility (in passenger, multimodal, and freight domains)? 2) which scenarios for micromobility use and related enablement of increased public transit use should be modeled/considered in the SMART 2.0 Workflow? 3) to what degree can micromobility supplement/complement transit system operations? 4) what are people's preferences towards micromobility? How do preferences vary across various sociodemographic segments? How can this knowledge inform operations? 5) what are optimal strategies to attain high user adoption and shift users toward more energy-efficient mode choices in terms of micromobility operation? How do these strategies affect energy savings, person-miles traveled, lifecycle energy use, and adoption rates? These questions will be addressed through applied research in five project emphasis areas: 1) energy estimates of micromobility for Workflow scenarios: Expand and refine previous micromobility work to augment the Workflow approaches to modeling urban travel; 2) multimodal connection with transit: Utilizing Mobility-Energy Productivity (MEP) tools to evaluate multimodal travel patterns enabled by micromobility, including assessing how to reduce barriers of inequity of access to mobility options and destinations; 3) mode choice, induced demand, and infrastructure: Understanding the mode shift induced through micromobility to inform energy impact analysis; 4) energy optimization of micromobility operations: Identification of micromobility operations parameters and development of operations scenarios to better understand present-day micromobility operations for integration into the Workflow, in partnership with BEAM and POLARIS modeling teams; 5) micro-freight: Characterize the current state of micro-freight activities, including energy effects and geospatial analyses, to inform Workflow.
ADVANCED PROPULSION SYSTEMS↗
Virtual Community Trains
When access to transit solutions is restricted to parts of a community, a division occurs what leads to disadvantaged socioeconomic conditions that only grows over time. This restriction causes difficulties in accessing needed resources such as medical facilities, employment, healthy food, education, healthy food, and more. To solve this restricted access, Labyrinth Smart Mobility and partners are developing a virtual community train that encourages scalability, energy efficiency, and equitability to all regardless of individual circumstances and limitation. The implementation of virtual community trains allows access to resources previously out of reach. The virtual community train enables one operator to drive a lead vehicle. This lead vehicle would then provide direction to a follow vehicle through vehicle-to-vehicle communication. Labyrinth Smart Mobility in conjunction with partners used various methods and resources to prove market feasibility, technical feasibility, economic need and viability, and more as well as design system requirements and architecture emphasis efficiency and safety. In doing so, Labyrinth Smart Mobility and partners will continue these advancements to further develop the virtual community train.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Unmanned Autonomous Systems (UAS) Traffic Management
This presentation is for the Plenary Session: "Unmanned Traffic Management (UTM) and the Future of Unmanned Systems in Urban Airspace" at the ASCE flagship 2019 International Conference on Transportation and Development, "Engineering Smart Mobility for the Smart City", June 9-12, 2019. The Panel is at 8:00 am on Wednesday, June 12 and moderated by Brent Ingraham (DOD). This presentation supplements Dr. Kopardekar's short introduction on how NASA is developing an Unmanned Traffic Management system and how this UTM will change the future of shared airspace.
Kopardekar, Parimal H.↗
Modeling and Simulation of Automated Mobility Districts
Increasing interest and investment in connected, automated, and electric vehicles (CAEVs), and mobility-as-a-service (MaaS) concepts are paving the way for the next major shift in transportation through automated and shared mobility. The initial excitement towards rapid deployment and adoption of automated vehicles has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geo-fenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities regarding benefits or dis-benefits of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This presentation provide the details of a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as Automated Mobility District (AMD) modeling and simulation toolkit, the proposed decision support tool intends to help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles in geofenced regions.The AMD modeling and simulation task is funded through DOE's SMART Mobility Consortium. This presentation outlines the progress of the AMD modeling and simulation task in FY19, including case studies conducted in Greenville, SC, and Austin, TX.
47 OTHER INSTRUMENTATION↗
Charging Infrastructure Design Trade-offs for a Fleet of Human-Driven and Fully Automated Electric Vehicles in San Francisco
This presentation summarizes the objectives, approach, and findings of a 3-year project led by INL as part of DOE's SMART Mobility Laboratory Consortium. The presentation will be given to the general public via Zoom during the FY20 Vehicle Technologies Office Annual Merit Review on June 3, 2020.
33 ADVANCED PROPULSION SYSTEMS↗