research report

Barriers to Reducing the Carbon Footprint of Transportation Part 2: Investigating Evolving Travel Behaviors in the Post-Pandemic Period in California

Publication Date

May 1, 2024

Author(s)

Basar Ozbilen, Siddhartha Gulhare, Keita Makino, Aurojeet Jena, Xiatian Iogansen, Patrick Loa, Yongsung Lee, Giovanni Circella

Abstract

During the early months of the pandemic, stay-at-home orders and concerns about infection catalyzed a shift toward online activities, such as remote work and e-shopping, resulting in a significant decrease in conventional travel. However, as the effects of the pandemic diminished, the pandemic-induced online activities began to subside, and conventional travel started to rebound. To understand evolving travel-related activities spurred by the COVID-19 pandemic, researchers at ITS-Davis conducted four waves of mobility surveys in California between Spring 2020 and Fall 2023. Key findings from the analysis of these data reveal that remote work and a combination of remote work and physical commuting (i.e., hybrid work) emerge as an enduring outcome of the pandemic. The pandemic accelerated the rise of e-shopping, both for grocery and non-grocery purchases, with findings demonstrating the critical influence of socio-demographic factors, including age, gender, and income, on e-shopping adoption and frequency. The findings show that socio-demographic factors such as work status, income level, and work arrangements are associated with household vehicle ownership changes and individual vehicle miles traveled (VMT). In particular, an increase in commute frequency reduces the likelihood of vehicle shedding (i.e., getting rid of a vehicle), while amplifying the likelihood of vehicle acquisition. In the meantime, remote workers exhibit lower commuting VMT but higher non-commuting VMT compared to hybrid workers. The findings demonstrate a similarity between the percentage of respondents who used public transit, bikes, e-bikes, and e-scooters for commuting and non-commuting trips to some degree between 2019 and 2023.

research report

Updated Fuel Portfolio Scenario Modeling to Inform 2024 Low Carbon Fuel Standard Rulemaking

Publication Date

November 1, 2023

Author(s)

Colin Murphy, Jin Wook Ro, Qian Wang

Abstract

The Low Carbon Fuel Standard (LCFS) plays a critical role in California’s efforts to reduce greenhouse gas (GHG) and air pollutant emissions from transportation. The LCFS incentivizes the use of fuels with lower life cycle GHG emissions by using a credit market mechanism to provide incentives for low-carbon fuels, using revenue generated by charges applied to high-carbon ones. Maintaining an approximate balance between LCFS credit and deficit supplies helps support a stable LCFS credit price and the broader transition to low-carbon transportation. The Fuel Portfolio Scenario Model, presented here, evaluates bottom-up fuel supply and LCFS compliance to inform LCFS policy decisions. We considered two key fuel demand scenarios: (1) the Low Carbon Transportation scenario, reflecting the expected transition to low-carbon transportation in California over the next 15 years, and (2) the Driving to Zero scenario, featuring a significantly higher consumption of petroleum gasoline. In both scenarios, 2030 LCFS targets around 30% resulting in a near-balance between credits and deficits, with some banked credits remaining. Several additional scenarios were modeled to explore the impact of target trajectory timing, alternate post-2030 targets, greater biofuel use, and other parameters. This fuel portfolio scenario modeling work can meaningfully inform policy development.

blog

Making Policy in the Absence of Certainty: Biofuels and Land Use Change

Publication Date

October 26, 2023

Author(s)

Colin Murphy

op-ed

Opinion: How a California Climate Win Could End up Destroying Rainforests — and What to do About it

Publication Date

March 14, 2024

Author(s)

Colin Murphy, Dan Sperling

other

Story Map: Examining Spatial Disparities in Electric Vehicle Charging Station Placements Using Machine Learning

published journal article

Examining Spatial Disparities in Electric Vehicle Charging Station Placements Using Machine Learning

Abstract

Electric vehicles (EVs) are an emerging mode of transportation that has the potential to reshape the transportation sector by significantly reducing carbon emissions thereby promoting a cleaner environment and pushing the boundaries of climate progress. Nevertheless, there remain significant hurdles to the widespread adoption of electric vehicles in the United States ranging from the high cost of EVs to the inequitable placement of EV charging stations (EVCS). A deeper understanding of the underlying complex interactions of social, economic, and demographic factors that may lead to such emerging disparities in EVCS placements is, therefore, necessary to mitigate accessibility issues and improve EV usage among people of all ages and abilities. In this study, we develop a machine learning framework to examine spatial disparities in EVCS placements by using a predictive approach. We first identify the essential socioeconomic factors that may contribute to spatial disparities in EVCS access. Second, using these factors along with ground truth data from existing EVCS placements we predict future ECVS density at multiple spatial scales using machine learning algorithms and compare their predictive accuracy to identify the most optimal spatial resolution for our predictions. Finally, we compare the most accurately predicted EVCS placement density with a spatial inequity indicator to quantify how equitably these placements would be for Orange County, California. Our method achieved the highest predictive accuracy (94.9%) of EVCS placement density at a spatial resolution of 3 km using Random Forests. Our results indicate that a total of 11.04% of predicted EVCS placements in Orange County will lie within a high spatial inequity zone – indicating populations with the lowest accessibility may require greater investments in EVCS placements. 69.52% of the study area experience moderate accessibility issues and the remaining 19.11% face the least accessibility issues w.r.t EV charging stations. Within the least accessible areas, 7.8% of the area will require a low density of predicted EVCS placements, 3.4% will require a medium density of predicted EVCS placements and 0.55% will require a high density of EVCS placements. The moderately accessible areas would require the highest placements of EVCS but mostly with low-density placements covering 54.42% of the area. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all.

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.

preprint journal article

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

preprint journal article

A Comparison of Time-use for Telecommuters, Potential Telecommuters, and Commuters during the COVID-19 Pandemic

Abstract

Throughout the ongoing COVID-19 pandemic, changes in daily activity-travel routines and time-use behavior, including the widespread adoption of telecommuting, have been manifold. This study considers how telecommuters have responded to the changes in activity-travel scheduling and time allocation. In particular, the research team considers how workers utilized time during the pandemic by comparing workers who telecommuted with workers who continued to commute. Commuters were segmented into those who worked in telecommutable jobs (potential telecommuters) and those who did not (commuters). Our empirical analysis suggested that telecommuters exhibited distinct activity participation and time use patterns from the commuter groups. It also supported the basic hypothesis that telecommuters were more engaged with in-home versus out-of-home activity compared to potential telecommuters and commuters. In terms of activity time use, telecommuters spent less time on work activities but more time on caring for household members, household chores, eating, socializing, and recreation activities than their counterparts. During weekdays, a majority of telecommuters did not travel and in general this group made fewer trips per day compared to the other two groups. Compared to telecommuters, potential telecommuters made more trips on both weekdays and weekends while non-telecommutable workers made more trips only on weekdays. The findings of this study provide initial insights on time use and the associated activity-travel behavior of both telecommuter and commuter groups during the pandemic.

published journal article

Electric Vehicles in Urban Delivery Fleets: How Far can They Go?

Abstract

The goal of this study is to provide insights into the expected role of medium-duty electric vehicles (EVs) in urban delivery fleets and to analyze the effectiveness of EV subsidies on EV fleet penetration and tailpipe emissions. To meet this goal, we propose a modeling framework that determines the minimum-cost fleet size and fleet mix (of EVs and conventional vehicles) and vehicle routes for a profit-maximizing delivery company. Second, we conduct extensive analyses using this modeling framework and Southern California network data; we vary the EV driving range, per-mile cost of EVs, demand rate, service region size/structure, driver working hours, and network travel times. We find that the optimal fleet mix nearly always includes EVs and conventional vehicles. Moreover, we find that EV subsidies have limited effectiveness with current EV batteries and service regions designed around conventional vehicles. Hence, improving EV battery technology is critical to electrifying urban delivery fleets.