Engineering topics

Hwang, Ho-Ling

Publications and source records attributed to Hwang, Ho-Ling.

7 records

DOE OSTI2023

Factors influencing mode choice of adults with travel-limiting disability

Introduction: Despite the plethora of research devoted to analyzing the impact of disability on travel behavior, not enough studies have investigated the varying impact of social and environmental factors on the mode choice of people with disabilities that restrict their ability to use transportation modes efficiently. This research gap can be addressed by investigating the factors influencing the mode choice behavior of people with travel-limiting disabilities, which can inform the development of accessible and sustainable transportation systems. Additionally, such studies can provide insights into the social and economic barriers faced by this population group, which can help policymakers to promote social inclusion and equity. Method: This study utilized a Random Parameters Logit model to identify the individual, trip, and environmental factors that influence mode selection among people with travel-limiting disabilities. Here, using the 2017 National Household Travel Survey data for New York State, which included information on respondents with travel-limiting disabilities, the analysis focused on a sample of 8,016 people. In addition, climate data from the National Oceanic and Atmospheric Administration were integrated as additional explanatory variables in the modeling process. Results: The results revealed that people with disabilities may be inclined to travel longer distances walking in the absence of suitable accommodation facilities for other transportation modes. Furthermore, people were less inclined to walk during summer and winter, indicating a need to consider weather conditions as a significant determinant of mode choice. Moreover, low-income people with disabilities were more likely to rely on public transport or walking. Conclusion: Based on this study’s findings, transportation agencies could design infrastructure and plan for future expansions that is more inclusive and accessible, thus catering to the mobility needs of people with travel-limiting disabilities.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Alternative Fuel Vehicle Usage and Owner Demographics in New York State

With mounting concerns over climate change and the environmental impact of fossil fuels, the United States has witnessed a growing interest in alternative fuel vehicles (AFVs). In 2021, approximately 1.5 million battery EVs (BEVs), 0.8 million plug-in hybrid EVs (PHEVs), and 5.5 million hybrid EVs (HEVs) were registered in the United States. In the state of New York, a total of 51,900 BEVs, 44,600 PHEVs, and 221,600 HEVs were registered in 2021. The current report presents the results of an analysis of AFV adoption patterns in New York State and the rest of the United States based on data from the 2017 National Household Travel Survey (NHTS). Overall, the report reveals the demographics and mobility factors (e.g., household income, homeownership, and trip length) that contribute to the adoption of AFVs. This study provides insights that can inform policy decisions aimed at promoting sustainable transportation solutions.The 2017 NHTS data showed that the percentage of households owning at least one AFV is lower in New York City compared with that in other regions of New York State. From the NHTS samples, of the 25 households that owned at least one BEV in New York State, 15 households (60%) lived within a 5-mile radius, based on the great circle distance, of the nearest charging station, and 23 households (92%) lived within a 10-mile radius of the nearest charging station. Furthermore, among the 40 households in New York State that own at least one PHEV, 48% (19 households) lived within a 5-mile radius of the closest EV charging station, and 83% (33 households) lived within a 10-mile radius of the nearest charging station. The rest of the United States had a higher percentage of households that own at least one AFV compared with that of New York State. A comparison was made between EV adoption levels using NHTS and EValuateNY, which is a tool that gathers statistics on the electric car market in New York State. The estimates obtained from New York State household samples in NHTS were slightly lower than the data provided by EValuateNY. In New York State and the rest of the United States, households with higher incomes tended to have a higher proportion of AFV ownership compared with those with lower incomes. For example, households in New York State earning $\$ $150,000 or more had an approximately 6% share of owning at least one AFV, which was markedly higher than those earning less than $\$ $100,000 (less than 3%). Additionally, homeowners in New York State and the rest of the United States also exhibited a significantly higher share of AFV ownership compared with that of renters. In New York State, households that own at least one AFV tended to travel farther and had longer travel times compared with their counterparts without an AFV. In terms of households with at least one AFV, households with HEVs tended to have more person trips, longer person miles of travel, and more vehicle miles traveled, resulting in longer travel times than that of households with BEVs or PHEVs. Notably, households with AFVs had a slightly lower share of family and personal business trips but a higher share of social and recreational trips compared with households without AFVs. Additionally, households with at least one AFV tended to have a slightly higher share of walking trips than their counterparts without an AFV. However, the comparisons were not statistically significant. These travel patterns observed in New York State were consistent with those observed in other regions of the United States.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Examining Rail Transportation Route of Crude Oil in the United States Using Crowdsourced Social Media Data

Safety issues associated with transporting crude oil by rail have been a concern since the boom of the U.S. domestic shale oil production in 2012. During the last decade, over 300 crude-oil-by-rail incidents have occurred in the United States. Some of them have caused adverse consequences including fire and hazardous materials leakage. However, only limited information on crude-on-rail routes and their associated risks is available to the public. To this end, this study proposed an unconventional way to reconstruct crude-on-rail routes using geotagged photos harvested from the Flickr website. The proposed method linked the geotagged photos of crude oil trains posted online with national railway networks to identify potential railway segments that those crude oil trains were traveling on. Here, a shortest path-based method was applied to infer the complete crude-on-rail routes, by utilizing the confirmed railway segments as well as their directional information. Validation of the inferred routes was performed using a public map and official crude oil incident data. The results suggested that the inferred routes based on geotagged photos had high coverage, with approximately 96% of the documented crude oil incidents aligned with the reconstructed crude-on-rail network. The inferred crude oil train routes were found to pass through several metropolitan areas of high population density, who were exposed to potential risk. These findings could improve situational awareness for policy makers and transportation planners. In addition, with the inferred routes, this study has established a good foundation for future crude oil train risk-analyses along the rail route.

42 ENGINEERING↗

DOE OSTI2023

An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City

In recent years, bikesharing systems have become increasingly popular as affordable and sustainable micromobility solutions. Advanced mathematical models such as machine learning are required to generate good forecasts for bikeshare demand. Here, this study proposes a machine learning modeling framework to estimate hourly demand in a large-scale bikesharing system. Two Extreme Gradient Boosting models were developed: one using data from before the COVID-19 pandemic (March 2019 to February 2020) and the other using data from during the pandemic (March 2020 to February 2021). Furthermore, a model interpretation framework based on SHapley Additive exPlanations was implemented. Based on the relative importance of the explanatory variables considered in this study, share of female users and hour of day were the two most important explanatory variables in both models. However, the month variable had higher importance in the pandemic model than in the pre-pandemic model.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

A Comparative Study of Machine Learning Algorithms for Industry-Specific Freight Generation Model

According to Bureau of Transportation Statistics, the U.S. transportation system handled 14,329 million ton-miles of freight per day in 2020. Understanding the generation of these freight shipments is crucial for transportation researchers, planners, and policymakers to design and plan for a more efficient and connected freight transportation system. Traditionally, the freight generation modeling has been based on Ordinary Least Square (OLS) regression, although more advanced Machine Learning (ML) algorithms have been evaluated and proven to have excellent performance in various transportation applications in recent years. Furthermore, one modeling approach applied for one industry might not always be applicable for another as their freight generation logics can be quite different. The objective of this study is to apply and evaluate alternative ML algorithms in the estimation of freight generation for each of 45 industry types. Seven alternative ML algorithms, along with the base OLS regression, were evaluated and compared. In addition, the study considered different combinations of variables in both the original and logarithmic form as well as hyperparameters of those ML algorithms in the model selection for each industry type. The results showed statistically significant improvements in the root mean square error reduction by the alternative ML algorithms over the OLS for over 80% of cases. The study suggests utilizing the alternative ML algorithms can reduce the root mean square error by about 30%, depending on industry types.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Travel Patterns and Characteristics of Elderly Population in New York State: 2017 Update

According to US Census Bureau, the elderly population (individuals 65 years and older) has grown by over a third during the past decade (2010 to 2019), and by 3.2% from 2018 to 2019. It is essential for policymakers and planners to understand transportation issues associated with the elderly to meet their increasing travel demands. These issues include transportation and mobility of the elderly population, factors impacting their travel behavior, and transportation safety. In this study, Oak Ridge National Laboratory was tasked by the New York State Department of Transportation (NYSDOT) to conduct a detailed examination of travel behaviors and identify patterns and trends of its elderly residents. The National Household Travel Survey (NHTS) was used as the primary data source to analyze subjects and address questions such as: Are there differences in traveler demographics between the elderly population and those of younger age groups who live in various New York State (NYS) regions, e.g., New York City (NYC), other urban areas of NYS, or other parts of the country? How do they compare with the population at large? Are there any regional differences (e.g., urban versus rural)? Do any unique travel characteristics or patterns exist within the elderly group? How did these patterns change over time? In addition to the analysis of NHTS data, roadway travel safety concerns associated with elderly travelers were also investigated. Specifically, data on crashes involving the elderly (including drivers, passengers, and pedestrians) as captured in the Fatal Analysis Reporting System database was analyzed to examine elderly drivers and elderly pedestrian travel safety issues in NYS. This study report provides a summary of travel behavior and social-demographic characteristics of NYS elderly residents. These statistics could be used to examine equity issue concerning elderly New Yorkers, as well as to evaluate how well their mobility needs are being met. With a deeper understanding of issues and needs that this special population group is facing, policymakers and transportation planners would be able to make informed decisions on transportation investments and design services that could better address them.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Improving the effectiveness and equity of fuel economy regulations with sales adjustment factors

Larger vehicles, such as sports utility vehicles, consume more energy than cars. Their increasing popularity runs contrary to the goal of fuel economy regulations to reduce fossil fuel consumption and greenhouse gas emissions and can be explained by consumer preference and lower regulation stringency, which is due to footprint, truck classification, and the omission of heterogenous lifetime vehicle distance traveled among vehicle classes. This study shows that, for both the US and China, large vehicles travel more, last longer, and are owned by higher income consumers. This means large vehicles and their high-income owners use more fuel and emit more pollutants than represented by current policy and thus raises both policy effectiveness and energy equity concerns. We propose and estimate Sales Adjustment Factors that weigh fuel economy standards based on vehicle lifetime usage and demonstrate the resultant significant improvements in the effectiveness and equity of fuel economy regulations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗