About

Konstadinos Goulias is a professor at the Department of Geography at UC Santa Barbara. The provided page text does not include specific details about his research focus, background, or key contributions. Therefore, no further biographical information is available from the given data.

Research topics

• Computer Science
• Sociology
• Machine Learning
• Artificial Intelligence
• Business
• Environmental health
• Genetics
• Statistics
• Psychology
• Mathematics
• Evolutionary biology
• Engineering
• Marketing
• Biology
• Telecommunications
• Transport engineering
• Cartography
• Data science
• Medicine
• Geography

Selected publications

• Cross-sectoral synergies for household energy savings: the role of electric vehicles, solar photovoltaics, and remote work

  Transportation Letters · 2026-01-28

  article
  PublisherDOI

• Dissimilarities in the COVID-19 pandemic reshaping of time use and travel in metropolitan and nonmetropolitan resident behavior from 2019 to 2023

  Cities · 2026-02-09

  articleOpen accessSenior author

  Research has shown that COVID-19 has impacted both short- and long-term aspects of individuals' daily schedules, yet its effects on metropolitan versus nonmetropolitan residents remain underexplored. Leveraging American Time Use Survey data from 2019 to 2023, this study applies diversity indices, sequence analysis, and statistical methods to investigate the pandemic's differential influence on metropolitan and nonmetropolitan daily routines, focusing on time allocation and travel behaviors. The results reveal a significant reduction in out-of-home activities and travel during the pandemic in both metropolitan and nonmetropolitan areas, followed by a partial recovery after vaccination; however, out-of-home activity levels remained below pre-pandemic levels, with this gap being more pronounced in metropolitan regions. Although the total number of road users declined, traffic congestion did not necessarily ease, as the travel population became less evenly distributed during a day by 2023 in both regions. In metropolitan areas, morning and midday peak hours dispersed, while evening travel remained substantially high. Moreover, seven distinct daily time allocation patterns including travel were identified over the past five years, with their frequency in the population evolving differently between metropolitan and nonmetropolitan residents. Approximately 10 million metropolitan residents transitioned to working from home in 2023. These findings underscore the importance of policies supporting electromobility and decentralized energy production. Continued trend analysis with larger samples is needed, as post-vaccination behaviors have yet to reach stability. • Out-of-home activities and travel dropped during the pandemic, with a partial recovery post-vaccination. • Despite fewer travelers, traffic congestion did not necessarily improve due to less balanced travel by 2023. • In metropolitan areas, morning and midday peak hours dispersed, while evening travel remained substantially high. • Seven daily time-use patterns emerged, evolving differently in metro and nonmetro populations. • Results highlight the need for policies promoting electromobility and decentralized energy production.

  PublisherDOI

• List of contributors

  Elsevier eBooks · 2026-01-01

  book-chapter
  PublisherDOI

• Statewide comparison of origin-destination matrices between California travel model and Twitter

  Elsevier eBooks · 2026-01-01

  book-chapterSenior author
  PublisherDOI

• Are past ownership experience and satisfaction major determinants of endorsement and future demand for zero emission vehicle technology when accounting for vehicle characteristics?

  Research in Transportation Economics · 2025-03-05

  articleOpen accessSenior author

  Considering the worldwide impacts of climate change, it is crucial to embrace sustainable transport alternatives in order to reduce the emission of greenhouse gases. This study seeks to probe the impact factors of future vehicle choices and recommendations to other potential users. Specifically, clustering is first done based on vehicle attributes to group users' future vehicle intentions. Then a weighted multinomial logistic model (MNL) is developed to study the impact factors of people's future vehicle demand. Following that, three distinct models are evaluated to identify factors influencing consumer willingness to recommend three different zero-emission vehicles (ZEVs) listed by the California Air Resources Board, namely plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), and hydrogen fuel cell electric vehicles (FCEVs), with past experiences (reflected by post-purchase satisfaction in this study) serving as mediators. Finally, the relationship between past experiences and future vehicle demand is discussed. Future vehicle choices are classified into four groups that based on fuel type, body size, vehicle addition or replacement, and desire for new or used automobiles. The results indicate that consumers who have experienced sustainable vehicles are more likely to continue to select them in the future. In terms of the impact factors of ZEV satisfaction and recommendation, PHEV owners are concerned about the costs associated with gasoline and electricity consumption at home. BEV users consider not just all of the aforementioned but also battery range and the availability of public charging stations. FCEV users value the convenience of refueling their vehicles.

  PublisherDOI

• The Influence of the COVID-19 Pandemic on Human Daily Mobility and Time Allocation

  2025-05-30

  book-chapter

  This chapter explores the enduring effects of the COVID-19 pandemic on individuals’ daily mobility and travel behavior, utilizing data from the American Time Use Survey (ATUS) from 2019 to 2022, which encompasses both the prepandemic period and the postvaccination phase. The study introduces network-like patterns of daily trips and destinations, referred to as motifs, and examines activity and travel durations and frequencies. Additionally, a fragmentation indicator is employed to assess whether COVID-19 has caused lasting changes to daily routines and behavior. The findings reveal that (1) the stay-at-home motif has the highest prevalence, with Americans favoring motifs involving fewer destinations; (2) participation in outdoor activities and travel decreased during the pandemic and had not returned to prepandemic levels by 2022; and (3) although daily patterns vary across the years, they consistently include mixed days, home discretionary days, and typical work days. Notably, home work days were absent before COVID-19, while discretionary days only appeared in 2019 and 2021.

  PublisherDOI

• Does Past Experience with Sustainability-Supporting Vehicles Influence Future Purchases and Recommendations?

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Choice context

  Edward Elgar Publishing eBooks · 2024-06-12 · 1 citations

  book-chapter1st authorCorresponding

  This chapter provides a review of key aspects in framing of decision making problems that are often encountered in behavioral models used to build simulation model systems. Each of these behavioral models aims at capturing how people address one or more decision problems (e.g., where to live, what car to buy, where to go on vacation, how much time to spend with family). For each of these decision problems, context is important - but the elicitation methods employed also play important roles in understanding decision making. In this chapter, behavioral choice odelled is viewed in terms of the decision making context, framing, and elicitation or measurement methods used from a wider perspective. A few illustrative problems are examined in detail using sources from the literature and personal communications with key research authors to identify relevant decision makers (e.g., a household, a worker, a firm), behaviors and/or actions that are considered most relevant (e.g., go shopping with friends and use the bus), social/cultural and physical contexts of each decision making problem (e.g., belonging to a club), and the alternatives (assumed or elicited) considered by the odelled decision makers. The chapter concludes with a review of the elicitation methods that may be used to obtain information about context in choice behavior.

  PublisherDOI

• Integrated Energy Savings: How Do Electric Vehicles, Solar Photovoltaics, and Work-from-Home Transform Household Electricity Costs?

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Year-to-year time allocation and spatial structure of Americans’ daily schedules from 2019 to 2022 and a detailed analysis of the stay-at-home all-day patterns

  Transportation Research Part A Policy and Practice · 2024-10-03 · 1 citations

  articleOpen accessSenior author

  While numerous studies have examined the effects of COVID-19 on our lives, few of them take into account the simultaneous changes in people’s daily routines from both time allocation and spatial movement perspectives. Based on the American Time Use Survey, this study proposes a novel methodology that combines sequence analysis and labeled motifs to probe the evolution of individuals’ time allocation and mobility movements from the pre-COVID period to the post-vaccination period using activity-travel sequences and network-like daily combinations of destinations and trips called motifs. Additionally, the relationship between socio-demographic characteristics and the time allocation and spatial movement of people’s daily schedules are investigated using a multinomial logit model and binary logit models. The results show that: (1) each ATUS year (from 2019 to 2022) contains mixed days, work days, and leisure days; (2) most trips decreased and increased proportionally from 2019 to 2022 and have not returned to pre-pandemic levels; (3) the stay-at-home motif shows the highest percentage and Americans tend to follow motifs with fewer destinations; and (4) personal and household characteristics influence people’s time allocation and spatial movements differently at different stages of the pandemic outbreak. Our analysis can assist in predicting travel time to reduce traffic congestion and also the timing of energy consumption to avoid energy demand spikes.

  PublisherDOI

Recent grants

• Doctoral Dissertation Research: Travel Determinants and Multi-scale Transferability of National Activity Patterns to Create Synthetic Populations

  NSF · $6k · 2008–2009

Frequent coauthors

• Ram M. Pendyala

  41 shared

• Adam W. Davis

  University of California, Davis

  31 shared

• Seo Youn Yoon

  29 shared

• Srinath Ravulaparthy

  University of California, Santa Barbara

  25 shared

• Ryuichi Kitamura

  25 shared

• Jae Hyun Lee

  Kyung Hee University

  24 shared

• Elizabeth C. McBride

  University of California, Santa Barbara

  22 shared

• Jun Ma

  Nanjing University of Science and Technology

  21 shared

Labs

• Konstadinos Goulias's LabPI

  1-2 sentence research focus

Education

• Other

  University of Calabria

  1986

• Other

  University of Michigan Ann Arbor

  1987

• Ph.D.

  University of California Davis

  1991