published journal article

Meta-analysis of shared micromobility ridership determinants

Abstract

Shared micromobility (SμM)—shared e-scooters and (e-)bikes—offer moderate-speed, space-efficient, and carbon-light mobility, promoting environmental sustainability and healthy travel. SμM benefits and SμM data availability have fueled a growing literature that analyses SμM ridership. We present a meta-analysis of 29 studies that estimate statistical models of zone- or station-based SμM trip counts, including 22 that examine station-based bikeshare systems. The meta-analysis reveals positive elasticities between SμM usage and population density (median elasticity of 0.16), employment density (0.07), median household income (0.33), bus stops (0.12), metro stations (0.17), bike infrastructure (0.09), and nearby station capacity (0.32). In contrast, station elevation has a negative elasticity. These magnitudes can inform SμM providers and transportation planners seeking to plan/design SμM systems to promote environmentally sustainable travel. Additionally, we critique the existing literature’s failure to (i) capture spatial dependencies, and (ii) discuss the practical implications of model parameters. Finally, we identify themes for future research.

Phd Dissertation

To Commute or Not to Commute? Impacts on Commuting of Land Use, Housing Costs, and COVID-19

Publication Date

May 24, 2023

Author(s)

Abstract

Apart from the COVID-19 pandemic, two chronic problems affecting Californians are high housing costs and road congestion. Although high housing costs and the determinants of commuting have separately received a lot of attention from academic researchers, to my knowledge very few papers have analyzed the linkage between them. In this dissertation, I present three essays that will enhance our understanding on the relationship between commuting, land use, housing costs, and the impact of COVID-19 on telecommuting. In all three essays, I use Structural Equation Model (SEM). In my first essay, I propose a framework for understanding the impact of housing costs on commuting time and commuting distance in one worker-households in Los Angeles County, which is the most populous county in the US. After analyzing data from the 2012 California Household Travel Survey (CHTS), I find that households who can afford more expensive neighborhoods have on average a commute 3.1% shorter per additional $100k to their residence median home values. In my second essay, I analyze the commutes of two-worker households to understand some of the trade-offs they need to make, since two-worker households have dual work constraints. My data for this essay come from 2017 National Household Travel Survey (NHTS) respondents who reside in five U.S. MSAs (San Francisco, Los Angeles, Dallas, Houston, and Atlanta). Results show that women do not commute as far as men on average, although their commuting time is not necessarily shorter than men’s, and that the commuting times of men and women are weakly positively correlated. Moreover, households have faster commutes by 14.5% for men and 22.7% for women per additional $1000 to their residence median monthly housing cost. My third essay investigates the impact of the COVID-19 pandemic on telecommuting by analyzing a unique dataset collected at the end of May 2021 by IPSOS via a random survey of California members of KnowledgePanel®. I find that an additional 4.2% of California workers would engage in some level of telecommuting and more educated workers are expecting to telecommute more (0.383* for bachelor’s degree) post-pandemic. Teasing out the impact of high housing costs on commuting is important at a time when concerns about the environmental impacts of transportation have turned reducing vehicle-miles traveled (VMT) into a policy priority. More generally, a better understanding of the determinants of commuting is critical to inform housing and transportation policy, improve the health of commuters, reduce air pollution, and achieve climate goals.

policy brief

What are the Equity Implications of Robo-taxis in terms of Job Accessibility Benefits?

Abstract

After years of research and development, companies are now operating fully driverless shared-use automated vehicle-enabled mobility services (SAMS) or “robo-taxis“ in Arizona and California. SAMS offer several potential benefits to travelers and society including reducing vehicle ownership, parking demand, congestion, crashes, energy consumption, and emissions, as well as increasing roadway capacity, mobility, and accessibility. Moreover, previous research by our team found that SAMS can provide significant job accessibility benefits to workers in California. To better understand the equity implications of the job accessibility benefits from SAMS, we analyzed the distribution of SAMS benefits across different segments of the population (e.g., low- vs. high-income, young vs. old).
To measure the accessibility benefits of SAMS, we use the logsum of a hierarchical work destination and commute mode choice model—a monetary measure of consumer surplus consistent with microeconomic and utility maximization theories. If a new commute mode (e.g., SAMS) is made available to travelers, and that new mode is competitive with existing modes in terms of travel time and travel cost, then the new mode will improve a traveler’s job accessibility. For more information, please see our previous study on measuring the job access benefits of SAMS2.

published journal article

1,000 HP Electric Drayage Trucks as a Substitute for New Freeway Lanes Construction

Abstract

Electrification of trucking combined with connected technologies promise to cut the cost of freight transportation, reduce its environmental footprint, and make roads safer. If electric trucks are powerful enough to cease behaving as moving bottlenecks, they could also increase the capacity of existing roads and reduce the demand for new road infrastructure, a consequence that has so far been understudied. To explore the potential speed changes of replacing conventional heavy-duty drayage trucks with electric and/or connected trucks, we performed microscopic traffic simulations on a network centered on I-710, the country’s most important economic artery, between the San Pedro Bay Ports and downtown Los Angeles, in Southern California. In addition to a 2012 baseline, we analyzed twelve scenarios for the year 2035, characterized by three levels of road improvements and four types of heavy-duty port trucks (HDPT). Our results show that 1,000 hp electric/hydrogen trucks (eTs) can be a substitute for additional freeway lanes in busy freight corridors. While conventional HDPTs with CACC would only slightly increase network speeds, replacing conventional HDPTs with eTs and improving selected I-710 ramps should be sufficient to absorb the forecasted increases in drayage demand for 2035 without adding a controversial lane to I-710. Our results highlight the importance of accounting for the impacts on the speed of new vehicle technologies in infrastructure planning and suggest shifting funding from building new capacity to financing 1,000 hp connected electric trucks in freight corridors until the market for these vehicles has matured.

published journal article

Telecommuting and Travel during COVID-19: An Exploratory Analysis across Different Population Geographies in the U.S.A.

Abstract

This study explores the impact of the COVID-19 pandemic on telecommuting (working from home) and travel during the first year of the pandemic in the U.S.A. (from March 2020 to March 2021), with a particular focus on examining the variation in impact across different U.S. geographies. We divided 50 U.S. states into several clusters based on their geographic and telecommuting characteristics. Using K-means clustering, we identified four clusters comprising 6 small urban states, 8 large urban states, 18 urban-rural mixed states, and 17 rural states. Combining data from multiple sources, we observed that nearly one-third of the U.S. workforce worked from home during the pandemic, which was six times higher than in the pre-pandemic period, and that these fractions varied across the clusters. More people worked from home in urban states compared with rural states. As well as telecommuting, we examined several activity travel trends across these clusters: reduction in the number of activity visits; changes in the number of trips and vehicle miles traveled; and mode usage. Our analysis showed there was a greater reduction in the number of workplace and nonworkplace visits in urban states compared with rural states. The number of trips in all distance categories decreased except for long-distance trips, which increased during the summer and fall of 2020. The changes in overall mode usage frequency were similar across urban and rural states with a large drop in ride-hailing and transit use. This comprehensive study can provide a better understanding of the regional variation in the impact of the pandemic on telecommuting and travel, which can facilitate informed decision-making.

research report

Investigation of LiDAR Sensing Technology to Improve Freeway Traffic Monitoring

published journal article

Health and equity impacts from electrifying drayage trucks

Abstract

Diesel heavy-duty drayage trucks (HDDTs) serving the Ports of Los Angeles and Long Beach in Southern California are large contributors to regional air pollution, but cost remains an obstacle to replacing them with zero-emission HDDTs. To quantify the health and equity impacts of operating diesel HDDTs, we built a microscopic simulation model of a regional freeway network and quantified their emissions of PM2.5 (particulate matter with a diameter < 2.5 μm) and CO2 in 2012 and 2035, before estimating their contribution to selected health outcomes. We found that 483 premature deaths ($5.59 billion) and 15,468 asthma attacks could be attributed to HDDTs in 2012. Regulations and technological advances could shrink these impacts to 106 premature deaths ($1.31 billion) and 2,142 asthma attacks in 2035 (over 2/3 accruing to disadvantaged communities) despite population growth and a 145 % jump in drayage traffic, but they still justify replacing diesel HDDTs with zero-emission HDDTs by 2035.

journal article preprint

Impacts of the COVID-19 Pandemic on Telecommuting and Travel

Abstract

This chapter examines changes in telecommuting and the resulting activity-travel behavior during the COVID-19 pandemic, with a particular focus on California. A geographical approach was taken to “zoom in” to the county level and to major regions in California and to “zoom out” to comparable states (New York, Texas, Florida). Nearly one-third of the domestic workforce worked from home during the pandemic, a rate almost six times higher than the pre-pandemic level. At least one member from 35 percent of U.S. households replaced in-person work with telework; these individuals tended to belong to higher-income, White, and Asian households. Workplace visits have continued to remain below pre-pandemic levels, but visits to non-work locations initially declined but gradually increased over the first nine months of the pandemic. During this period, the total number of trips in all distance categories except long-distance travel decreased considerably. Among the selected states, California experienced a higher reduction in both work and non-workplace visits, and the State’s urban counties had higher reductions in workplace visits than rural counties. The findings of this study provide insights to improve our understanding of the impact of telecommuting on travel behavior during the pandemic

Phd Dissertation

Alternative Fuel Adoption Behavior of Heavy-duty Vehicle Fleets

Abstract

Alternative fuel adoption by heavy-duty vehicle (HDV) fleets can bring substantial benefits to both current local communities and future generations by reducing air pollutants and greenhouse gas emissions. However, the penetration rate of alternative fuel vehicles (AFVs) is still very low in the HDV sector. Revealing HDV fleet operator perspectives towards alternative fuels can serve as the basis for developing effective policies for accelerating the diffusion of these technologies. This dissertation aims to fill a key knowledge gap, where such fleet operator perspectives have rarely been addressed, by exploring alternative fuel adoption behavior of HDV fleets.An initial theoretical framework was first developed based upon existing theories and literature to conceptually understand AFV fleet adoption behavior in organizations. This initial framework consists of a five-stage adoption process as well as two levels of sub-frameworks: at the decision-making unit level and the individual (e.g., vehicle drivers) acceptance level. Next, it was attempted to empirically improve the initial framework by investigating 20 organizations operating HDVs in the State of California via in-depth qualitative interviews and project reports. A total of 29 adoption and 42 non-adoption cases was probed across various alternative fuel technologies, including natural gas, propane, electricity, hydrogen, biodiesel, and renewable diesel options. The qualitative data was analyzed using content and thematic analyses, by which numerous themes and hypotheses were developed to build a theory explaining heavy-duty AFV fleet adoption behavior. Based on these qualitative inferences, a conceptual modelling framework was proposed for estimating demand for heavy-duty AFVs under different policy and technology advancement scenarios. An overall structure along with specific modules and components for this framework are presented. As an ongoing work, a stated preference choice experiment was designed to quantitatively operationalize one of the modules, to estimate AFV choice probabilities. The feasibility of this modelling approach is proposed to be examined in a case study interviewing California drayage fleet operators. Finally, the research findings contribute theoretically and empirically to a better understanding of the demand-side aspects of AFV adoption by HDV fleet operators, particularly in California and in the other US states that follow California’s environmental policies.

book/book chapter

Understanding and Modeling the Impacts of COVID-19 on Freight Trucking Activity

Abstract

Restrictions on travel and in-person commercial activities in many countries (e.g., the United States, China, European countries, etc.) due to the global outbreak and rapid spread of the coronavirus disease 2019 (COVID-19) have severely impacted the global supply chain and subsequently affected freight transportation and logistics. This chapter summarizes the findings from the analysis of truck axle and weight data from existing highway detector infrastructure to investigate the impacts of COVID-19 on freight trucking activity. Three aspects of COVID-19 truck impacts were explored: drayage, long and short-haul movements, and payload characteristics. This analysis revealed disparate impacts of this pandemic on freight trucking activity because of local and foreign policies, supply chain bottlenecks, and dynamic changes in consumer behavior. Due to the ongoing effects of COVID-19, it is not yet possible to distinguish between transient and long-term impacts on freight trucking activity. Nonetheless, a future expansion of the study area and the incorporation of other complementary data sources may provide further insights into the pandemic’s impacts on freight movement.