Engineering topics

Young, Stanley

Publications and source records attributed to Young, Stanley.

8 records

DOE OSTI2024

A Model for Optimally Allocating Curbside Space Among Competing Uses

The emergence of various new forms of urban mobility services in recent years is leading to new pressures on curbside space. Municipalities, the entities typically responsible for managing the curbside, are in many instances handling these growing pressures by reallocating portions of the curbside away from traditional uses (such as metered and residential parking) in favor of uses such as ridehailing, scooter and bike-share corrals. As yet, however, such actions are being undertaken on an ad-hoc basis, due to the rapidly growing complexity of the curbside and the lack of standard analytical approaches. This lack of analytical capability is due to the traditional focus of transportation network modeling being focused predominantly on the interaction of supply and demand on links and nodes, with limited focus on link edges (the curbside). In this paper we address this research need by proposing a framework for modeling inter-modal competition for curbside space, inspired by the classical Bid-Rent Model of urban land use, intended to support curb managers to move towards maximizing the aspects of economic welfare that relate to curb access. In the bi-level model, choices made by the curbside manager impact travelers’ mode choices, and vice versa. We then present a simple numerical case study to demonstrate the properties of the proposed model, showing its tractability, flexibility, and intuitive sensitivity to systematic variation in inputs. The framework demonstrates the type of adaptive and evolving approach needed to maximize benefits from increasingly dynamic curb management strategies. The paper concludes with a brief discussion of future research needs to advance this line of inquiry.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri

Legacy fixed route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEV) are an innovative technological solution that can help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, Missouri using responses from a community survey (n=244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed route transit, and transportation network companies (TNC) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly (20%), lower income (27%), and households with limited access to private vehicles (34%)). ODT reduced total travel time by 32% compared to fixed route transit, produced 2.4 - 4.3 times less greenhouse gas emissions per passenger mile (compared to transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10-12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Inaugural Technologist in Communities Scanning Tour: Automated and Electrified On-Demand Mobility (Follow-On Summary Report: April 24-25, 2023) [Slides]

In 2023, the Technologist in Communities (TIC) program hosted its inaugural scanning tour across the American Southwest, with a goal of investigating the region's leading public mobility systems and their integration with on-demand public mobility services. The scanning tour concept was inspired by and named after a U.S. Department of Transportation Federal Highway Administration practice of hosting "scanning tours," both domestically and abroad, to explore innovative transportation practices throughout the 1990s and early 2000s. Guided by the idea that "seeing is believing," scanning tours created opportunities for government and industry representatives to collectively experience new technologies and best practices firsthand. In 2023, the National Renewable Energy Laboratory (NREL) followed suit by leading the inaugural TIC scanning tour. TIC, part of the Technology Integration program within the U.S. Department of Energy's (DOE's) Vehicle Technologies Office, supports positive energy and mobility outcomes in communities across rural to urban contexts. TIC enables technical experts to work directly with leaders and stakeholders in communities and assist them in implementing promising new technologies. This first TIC scanning tour invited NREL, DOE, and industry experts to survey innovative public mobility practices and their integration into public transit systems across the Southwest. The tour's goals included examining the state of innovation in public mobility in cities, communities, and airports; facilitating collaboration between entities; and sparking potential new partnerships. The following report summarizes key findings from the scanning tour.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Linking transportation agent-based model ($\mathrm{ABM}$) outputs with micro-urban social types ($\mathrm{MUSTs}$) via typology transfer for improved community relevance

The human relationship with transportation is shaped by social, economic, demographic, and urban form variables, or socio-spatial factors. The spatial dynamics of these are key to generating and interpreting outputs of transportation models that are most relevant for a community and the diverse mobility needs of its members. Here we present a typology transfer framework, grounded in socio-spatial dynamics shaping people's mobility, to take transportation-themed regional mobility model outcomes, in this case from two agent-based models (ABMs), and extrapolate them to other cities, with less time and resource intensity than new ABM development. The typology transfer process first identifies micro-urban social types (MUSTs) using socio-spatial factors, then defines city types based on spatial patterns of MUSTs to assess across which cities transfer results are likely to best hold. Lastly, a typology transfer multiplier matrix extrapolates a given variable, in our case the Mobility Energy Productivity (MEP) metric, to another city. The full process demonstration uses ABM results from Chicago (POLARIS model) and San Francisco (BEAM model), applying them to New York City. We discuss how MEP or other outputs can be appropriately estimated and used for integrated, human-centered mobility analysis. Key findings include that this MUST framework of user-defined dependent and independent variables allows tailoring ABM results and interpretations to specific community needs and data availability. Findings clarify that positive outcomes can be targeted towards user groups, based on sociospatial characteristics, using a typology approach, such as inclusive access to mobility choices, transportation affordability, and greater efficiency in resource use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Roadmap to Automated Mobility Systems: Informing the Planning of a Sustainable, Resilient Transportation Ecosystem for Dallas/Fort Worth International Airport

The National Renewable Energy Laboratory (NREL) developed this report to provide Dallas/Fort Worth International Airport (DFW) assistance toward a vision that considers the maturation and proliferation of mobility automation, electrification, and infrastructure integrated with Internet of Things (IoT) technologies as they present themselves on the path to 2035. In planning ongoing infrastructure investments, DFW has goals to accommodate and leverage these enabling technologies toward greater sustainability and enhanced traveler and employee experiences, ensuring that infrastructure investments are fully utilized into the future. The objective of this document is to assist DFW to anticipate and envision future airport access by travelers, employees, and goods by examining existing needs and exploring opportunities enabled by technology that informs longer-term infrastructure planning. The vast infrastructure of DFW, which includes buildings, roadways, and other physical structures, as well as growing digital and energy network infrastructures, requires long-term planning and strategy to fully leverage technology advancement and avoid abandoning assets due to functional obsolescence.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

The Automated Mobility District Implementation Catalog, 2nd Edition: Safe and Efficient Automated Vehicle Fleet Operations for Public Mobility

This second volume of the Automated Mobility District (AMD) Implementation Catalog series has been prepared in three parts to assist readers in their review and understanding of the technical information herein. The material may be more easily ingested if each of the three parts is read and then contemplated for its ramifications to the specific interest of the reader before continuing to the next part. Further, each part has been prepared with the intent that it could be read independently from the others. Part 1 is Progress of Automated Vehicle R&D for Deployments in Passenger Service; Part 2 is 10 Early Deployment Sites as Prototypes of AMD Implementation; Part 3 is Five Cardinal Principles for AMD Implementation.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

A Roadmap Toward a Sustainable Aviation Ecosystem

This report outlines a holistic view of pathways to a sustainable aviation ecosystem, focusing on low-net-carbon aircraft energy carriers (fuels), airport ecosystems (airports and bases), and developments in sustainable aircraft components (aircraft). Taking this holistic ecosystem perspective, we identify critical components that contribute to sustainable energy solutions necessary to achieve deep decarbonization of the aviation industry, as well as the integrated energy system interfaces that must be comprehensively understood, planned for, and realized. Further, we outline necessary R&D needs to achieve sustainability across the aviation ecosystem, including advancing breakthrough innovations to rapidly achieve scalable solutions, with attention to their cross-sectoral dependencies and implications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗