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Jin, Ling

Publications and source records attributed to Jin, Ling.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Source-Resolved Inversion of Elemental Carbon Emissions in California Using Log-Space Bayesian Inference

Elemental carbon (EC), operationally quantified by thermal-optical analysis, is widely used as a proxy for black carbon (BC) relevant to short-term climate forcing and public health. Current EC emission inventories remain highly uncertain, with persistent discrepancies between bottom-up and top-down estimates. In this study, we develop a source-resolved, log-space Bayesian inversion framework applied to estimate California’s statewide EC emissions in 2019. By integrating surface EC measurements from the EPA’s Air Quality System network with high-resolution source contributions simulated by a chemical transport model, we identify a one-third underestimation in the existing statewide EC inventory, requiring an increase of the total from a prior of 8.58 [5.49–13.75] Gg year–1 to a posterior estimate of 12.78 [10.71–15.37] Gg year–1. This discrepancy is primarily driven by substantial underestimations in the power and industrial and off-road mobile sectors. Furthermore, population-weighted exposure analysis reveals a marked sectoral divergence between emission mass and health burden: off-road mobile sources dominate both emissions and exposure, accounting for 31% of statewide exposure, while residential wood combustion contributes 26% of total exposure despite comprising only 19% of total emissions, due to its source proximity to population. These findings underscore the need to update sector-specific EC speciation profiles and demonstrate that mitigation strategies targeting off-road mobile sources and residential wood combustion are critical for reducing EC-related health impacts in California.

Zhang, Jie↗
DOE OSTI2025

Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life

Agent-based models (ABMs) in transportation modeling simulate activity and travel decisions at the disaggregate level of households and individuals. To do this, ABMs require detailed and realistic information on agents’ socioeconomic and demographic characteristics. Various synthetic population generators have been proposed to address this need. However, most of those currently in practice are cross-sectional in nature and do not account for the dynamics within households and individuals as they progress through life events over time. This is a major shortcoming, as literature has shown that transportation decisions are affected by the transition between and co-occurrence of life cycle events. While some demographic evolution simulators have been proposed to address this issue, they are developed using cross-sectional data and capture only a small set of life cycle events and their interdependence. Addressing these drawbacks, we propose a demographic microsimulator (DEMOS) that captures the “continuum of life” by considering a range of household- and individual-level life cycle events. DEMOS is developed using the Panel Survey of Income Dynamics, one of the world’s longest-running longitudinal surveys. The DEMOS submodels consider key life cycle events that are influenced by agents’ demographic variables. DEMOS is applied to evolve the population of the San Francisco Bay Area over a 9-year horizon. Results demonstrate how DEMOS generates life trajectories and how DEMOS outputs match the observed demographic trends. DEMOS is expected to enable longitudinal analysis in the context of ABMs and expand ABMs analyses relating to dynamic processes such as household-level vehicle transactions.

Demographic evolution↗
DOE OSTI2025

Distribution Feeder Characteristics and Their Resiliency to Natural Hazards

This paper introduces a method and approach for initially screening actions to improve the reliability of distribution systems exposed to three natural hazards (wildlife, weather, and vegetation). The method is well-suited to needs and capabilities of smaller utilities because it relies on readily available information and does not depend on simulations involving detailed engineering models. The method involves first aggregating feeders into like types and then correlating them, separately by hazard, to standard measures of reliability, namely, System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI), and the individual constituents of these measures. The correlations provide both new insights into the efficacy of accepted reliability management practices while and re-confirm long-understood, accepted practices. Importantly, the method does not seek to replace the need for detailed engineering analysis. Instead, in view of the significant costs involved in conducting these more involved analyses, the method is intended to help smaller utilities prioritize their more limited resources to maximize the efficacy of actions they take to improve reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Evaluating the Impacts of Autonomous Electric Vehicles Adoption on Vehicle Miles Traveled and CO2 Emissions

Autonomous electric vehicles (AEVs) can potentially revolutionize the transportation landscape, offering a safer, contact-free, easily accessible, and more eco-friendly mode of travel. Prior to the market uptake of AEVs, it is critical to understand the consumer segments that are most likely to adopt these vehicles. Beyond market adoption, it is also important to quantify the impact of AEVs on broader transportation systems and the environment, such as impacts on the annual vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. In this pilot study, using survey data, a statistical model correlating AEV adoption intention and socioeconomic and built environment attributes was estimated, and a sensitivity analysis was conducted to understand the importance of factors impacting AEV adoption. We found that the market segments range from early adopters who are wealthy, technologically savvy, and relatively young to non-adopters who are more cautious to new technologies. This is followed by a synthetic population microsimulation of market penetration for the San Francisco Bay Area. With five household vehicle replacement scenarios, we assessed the annual VMT and tailpipe carbon dioxide (CO2) emissions change associated with vehicle replacement. It is found that adopting AEVs can potentially reduce more than 5 megatons of CO2 yearly, which is approximately 30% of the total CO2 emitted by internal combustion engine (ICE) cars in the region.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗
DOE OSTI2024

Automobile and Technology Lifecycle-Based Assignment (ATLAS) v2.0.12

ATLAS is a comprehensive vehicle transaction and technology adoption microsimulator. ATLAS evolves the fleet mix of individual households by simulating the transaction (vehicle addition, disposal, and replacement) and choice (vehicle type, vintage, powertrain, and tenure) decisions in response to the co-evolving demographics, land use, and vehicle technology simulations. Different from the existing vehicle models that are either static or aggregated (e.g. stock model), ATLAS is fully disaggregated and dynamic following a sequential and circumstantial decision-making trajectory. This fine-grained approach not only enhances the realism of the simulation but also provides a nuanced understanding of the dynamics inherent in vehicle fleet evolution. ATLAS outputs are fully compatible with subsequent agent-based transportation modeling system and can enable distributional effect analysis regarding the fleet turnover among heterogeneous populations. ATLAS expands the typical new sale focused vehicle choice modeling to including used vehicle transactions that are of increasing interests to understanding the vehicle adoption behavior among lower income households.

Jin, Ling↗
DOE OSTI2024

Technology progress and clean vehicle policies on fleet turnover and equity: insights from household vehicle fleet micro-simulations with $\text{ATLAS}$

This paper documents the design and application of ATLAS (Automobile and Technology Lifecycle-Based ASsignment), a comprehensive household vehicle transaction and technology adoption micro-simulator in the San Francisco Bay Area. ATLAS evolves the fleet mix of individual households by simulating the vehicle transaction and choice decisions in response to co-evolving demographics, land use, and vehicle technology simulations. While most existing literature has focused on the aggregate clean vehicle uptake, this paper differentiates distributional effects and decomposes the underlying mechanisms across heterogeneous sub-populations of households. Using scenarios and sensitivity simulations that vary vehicle technology and policy assumptions, we find that Zero Emission Vehicles (ZEVs) penetrate into higher income groups at a faster rate than into lower income groups, which is intuitive and aligns with expectations. Interestingly, the relative income disparity in ZEV ownership shrinks over time across all scenarios, with a ZEV mandate coupled with declining battery cost leading to the greatest reduction in disparity of ZEV ownership by 2050. Federal, state, and local financial incentives influence the redistribution of ZEV uptake across income groups and contribute to narrowing income disparity. Vehicle transaction frequency and new versus used market dynamics are found to be important factors contributing to the income disparity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Gender Gaps in Mode Usage, Vehicle Ownership, and Spatial Mobility When Entering Parenthood: A Life Course Perspective

Entry into parenthood is a major disruptive event to travel behavior, and gender gaps in mobility choices are often widened during parenthood. The exact timing of gender gap formation and their long-term effects on different subpopulations are less studied in the literature. Leveraging a longitudinal dataset from the 2018 WholeTraveler Study, this paper examines the effects of parenthood on a diverse set of short- to long-term outcomes related to the three hierarchical domains of mobility biography: mode choice, vehicle ownership, spatial mobility, and career decisions. The progress of the effects is evaluated over a sequential set of parenting stages and differentiated across three subpopulations. We find that individuals classified as “Have-it-alls”, who start their careers, partner up, and have children concurrently and early, significantly increase their car uses two years prior to childbirth (“nesting period”), and they then relocate to less transit-accessible areas and consequently reduce their reliance on public transportation while they have children in the household. In contrast, individuals categorized as “Couples”, who start careers and partnerships early but delay parenthood, and “Singles”, who postpone partnership and parenthood, have less pronounced changes in travel behavior throughout the parenting stages. The cohort-level effects are found to be driven primarily by women, whose career development is on average more negatively impacted by parenting events than men, regardless of their life course trajectory. Early career decisions made by women upon entering parenthood contribute to gender gaps in mid- to longer-term mobility decisions, signifying the importance of early intervention.

Yang, Hung-Chia (ORCID:0000000161439904)↗
DOE OSTI2023

Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life

Agent-based models (ABMs) simulate activity and travel decisions at the disaggregate level of households, and individuals. To do this, ABMs require detailed information pertaining to socioeconomic and demographic characteristics of individuals. Various synthetic population generators (SPGs) have been proposed to address this need. However, most of the SPGs currently in practice are cross-sectional in nature, and do not account for the interrelationships among household's or individual's life progression. This is a major shortcoming of SPGs as literature has shown that transportation decisions are impacted by lifecycle events that unfold over a span of time. While some demographic evolution simulators have been proposed to address this shortcoming, they: i) are developed using cross-sectional data, ii) do not capture the full spectrum of lifecycle events and their interdependency. Overcoming these drawbacks, this paper proposes a Demographic Microsimulator (DEMOS) which captures the 'continuum of life' by accounting for a range of household-, and individual-level lifecycle events. DEMOS is developed using the Panel Survey of Income Dynamics, which is one of the world's longest running longitudinal surveys. DEMOS sub-models consider key lifecycle events which are influenced by a host of demographic variables. The whole framework is applied to evolve the population of San Francisco Bay Area over a 9-year horizon. Results indicate that the household and individual evolution are tightly connected, and that the structural framework (i.e., model sequencing) is a key element in capturing the population trend accurately.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2022

What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions

What makes you hold on to that old car? While the vast majority of household vehicles are still powered by conventional internal combustion engines, the progress of adopting emerging vehicle technologies will critically depend on how soon the existing vehicles are transacted out of the household fleet. Leveraging a nationally representative longitudinal data set, the Panel Study of Income Dynamics, this study examines how household decisions to dispose of or replace a given vehicle are: 1) influenced by the vehicle’s attributes, 2) mediated by households’ concurrent socio-demographic and economic attributes, and 3) triggered by key life cycle events. Coupled with a newly developed machine learning interpretation tool, TreeExplainer, we demonstrate an innovative use of machine learning models to augment traditional logit modeling to both generate behavioral insights and improve model performance. We find the two gradient-boosting-based methods, CatBoost and LightGBM, are the best performing machine learning models for this problem. The multinomial logistic model can achieve similar performance levels after its model specification is informed by TreeExplainer. Both machine learning and multinomial logit models suggest that while older vehicles are more likely to be disposed of or replaced than newer ones, such probability decreases as the vehicles serve the family longer. Pickup trucks and sport utility vehicles are less likely to be disposed of or replaced than cars, and leased vehicles are more likely to be transacted than owned vehicles. We find that married families, families with higher education levels, homeowners, and older families tend to keep their vehicles longer. Life events such as childbirth, residential relocation, and change of household composition and income are found to increase vehicle disposal and/or replacement. We provide additional insights on the timing of vehicle replacement or disposal, in particular, the presence of children and childbirth events are more strongly associated with vehicle replacement among younger parents.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Quality Data Essential for Modeling Water Cycles Effectively

The experimental and observational data is the backbone of effective modeling of all aspects of water cycles. As the computer technology used for these data gathering efforts has improved dramatically in the recent decades, the environmental conditions these instruments are subjected to remain pretty much the same. These measuring instruments are exposed to the sun light, wind, rain, snow and ice; and they are immersed in dirt, water and mud. All of these are damaging to the electronics components used for data gathering, storage, and transmission, which creates many different kinds of data quality issues that affect the quality and reliability of modeling and prediction efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States

Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. In this work, we use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

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↗
DOE OSTI2020

Life-Cycle Greenhouse Gas Emissions and Human Health Trade-Offs of Organic Waste Management Strategies

Waste-to-energy systems can play an important role in diverting organic waste from landfills. However, real-world waste management can differ from idealized practices, and emissions driven by microbial communities and complex chemical processes are poorly understood. This study presents a comprehensive life-cycle assessment, using reported and measured data, of competing management alternatives for organic municipal solid waste including landfilling, composting, dry anaerobic digestion (AD) for the production of renewable natural gas (RNG), and dry AD with electricity generation. Landfilling is the most greenhouse gas (GHG)-intensive option, emitting nearly 400 kg CO 2e per tonne of organic waste. Composting raw organics resulted in the lowest GHG emissions, at -41 kg CO 2e per tonne of waste, while upgrading biogas to RNG after dry AD resulted in -36 to -2 kg CO 2e per tonne. Monetizing the results based on social costs of carbon and other air pollutant emissions highlights the importance of ground-level NH3 emissions from composting nitrogen-rich organic waste or post-AD solids. However, better characterization of material-specific NH 3 emissions from landfills and land-application of digestate is essential to fully understand the trade-offs between alternatives.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Smelly-Odor Webtool (Smelly) v1

Smelly is an interactive webtool displaying odor dispersion from various potential waste-to-energy sites across California. It currently displays data for dry anaerobic digestion technology with organic waste intake of 90,000 tons per year. The model addresses meteorological and geographical differences in offsite odor impacts (Jin et al. 2019). In addition to interactive display of odor dispersions, the embedded emission calculator provides two additional functionalities: (1) determine the impact distance for given onsite odor emission rates; and (2) determine allowable odor emission rates at given separation distance between the facility and its downwind communities. Please note that data and model is preliminary at this point and serve as a Proof of Concept. Additional data collection, modeling and research is needed for final application.

Jin, Ling↗
DOE OSTI2019

High-density thermal sensitivity maps of the human body

‘Personal comfort systems’ and thermally active clothing are able to warm and cool individual building occupants by transferring heat directly to and from their body surfaces. Such systems would ideally target local body surfaces with high temperature sensitivities. Such sensitivities have not been quantified in detail before. Here we report local thermal sensations and sensitivities for 318 local skin spots distributed over one side of the body, measured on a large number of subjects. Skin temperature changes were induced with a thermal probe 14 mm in diameter, and subjective thermal sensations were surveyed after 10 s. Our neutral base temperature was 31 °C and the spot stimulus was ±5 °C. Cool and warm sensitivities are seen to vary widely by body part. The foot, lower leg and upper chest are much less sensitive than average; in comparison, the cheek, neck back, and seat area are 2–3 times as sensitive to both cooling and warming stimuli. Every body part exhibits stronger sensitivity to cooling (1.3–1.6 times stronger) than to warming. Inter-personal differences and regional variance within body parts were observed to be 2–3 times greater than potential sex differences. These high-density thermal sensitivity maps with appended dataset provide the most comprehensive distributions of cold and warm sensitivity across the human body.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Paths to Sustainable Distributed Generation through 2050: Matching Local Waste Biomass Resources with Grid, Industrial, and Community Needs

This project identifies and evaluates current and future organic waste fuel types and matches these wastes with local grid, industrial, building power, and thermal energy needs. The team developed data on organic waste streams and thermal energy needs, and then constructed a techno-economic analysis model that can process the data and compute viable production of electricity, waste heat, biogas, and/or biomethane for a range of energy price points. Scenarios generated by the model include allocation of local organic waste streams to existing or new conversion infrastructure and are evaluated in a life-cycle assessment model to determine local and global environmental impacts. The project’s webbased tool provides rapid evaluation of potential waste-to-energy sites, including retrofits/expansions of existing facilities or entirely new construction, on the basis of resource availability, potential for waste heat use, and economic and environmental metrics.

09 BIOMASS FUELS↗