Understanding travel mode choice through the lens of COVID-19: a systematic review of pandemic commuters

ABSTRACT

The COVID-19 pandemic disrupted travel behaviours for very large numbers of people including those who shifted to teleworking and those without the option to work from home. While there is much valuable transport research that has examined the former category, it is still unknown how certain people such as health sector employees and delivery drivers changed their physical commuting in transport contexts that were radically different from those existing normally in urban areas. Based on a systematic review of 36 scientific publications on commuting during pandemic, this study pursues a dual objective. First, by examining the interrelated institutional, physical, and socio-psychological processes that supported or hindered low-carbon transport the study revealed that (A) public transport (PT) reduced service levels and concerns related to COVID were positively associated with substantial shifts away from PT towards car and active travel; (B) this positive association was found to be even stronger in the existence of pre-pandemic habit of car use for commute and strong negative emotions like fear triggered by environmental changes and health risks. Second, by synthesising the key findings from the literature, this study provides significant implications for how mode choice is modelled through the Theory of Planned Behavior and Norm Activation Model. By questioning whether the pandemic commuters had a “normal” set of travel mode alternatives to choose from, the study draws attention to the nuances of mode “choice” versus mode “use” and moves beyond the assumption that commuting always results from individuals making choices. It also argues that the role of (negative) emotions along with the importance of proximity to, or separation from, other bodies on how people commute should be considered in future research. Finally, the crucial role of COVID-19 in changing travel-related norms and the resulting long-term implications for policy interventions require further investigation by future research.

KEYWORDS:

• Sustainable transport
• travel behaviour change
• COVID-19
• essential workers
• pandemic commuting
• mode choice

1. Introduction

With the growing efforts to decarbonise transport, travel behaviour research has highlighted the importance of analysing transport disruptions and subsequent individual traveller responses. Voluntary or involuntary, planned or unplanned, travel disruptions change stable mobility habits and increase attentiveness to alternative solutions (e.g. teleworking) and transport modes, hence, a “window of opportunity” opens up for introducing and encouraging the use of low-carbon transportation alternatives (Schwanen et al., 2012; Verplanken & Roy, 2016; Zarabi et al., 2019). The recent pandemic and government-mandated restrictions on mobility have constituted a set of important involuntary and unplanned life-changing events, which have disrupted mobility behaviours for very large numbers of people (Lee et al., 2021) – at least temporarily (Hensher & Beck, 2022). In this period, teleworking increased and physical commuting reduced on an unprecedented scale. However, certain parts of the population such as health sector employees and delivery drivers had to continue travelling as they did not have the option to work from home (WFH). Since March 2020, travelling to a place of work was defined in various ways as an essential trip and specific employees among those who made these journeys were recognised as key or essential workers. This designation varied in different geographical contexts around the world but mainly included those who worked in sectors such as safety, government, healthcare, food, energy, and transport (Plyushteva, 2022). Additionally, there were individuals who were not classified as key or essential workers but still had to commute to their workplace due to the lack of WFH options. In this study, we collectively refer to both essential and non-essential workers who travelled to work during travel restrictions over the course of the COVID-19 pandemic as “pandemic commuters”.

This study offers a systematic review of the published literature on the commuting mode use of workers (both essential and non-essential) during the COVID-19 pandemic. The objectives are two-fold. First, it evaluates how and to what extent the commuting patterns (or future intentions when studied) of different groups of these workers have been affected by (1) changes to the transport context (e.g. reduced public transit services and road congestion) and (2) their perception, interpretation, and emotional experience of those context changes. Within the 36 papers that met our inclusion criteria special attention has been devoted to two topics: commute mode shifts away from Public Transport (PT) to car driving or active travel, and the (negative) emotions triggered by changes in the environment in which their commuting occurs (e.g. changing health risks, changes in popular discourse, situational uncertainty). Second, by synthesising the findings from the review, we derive multiple implications for future studies of commute mode use. For this purpose, we draw insights from two prominent behaviour theories that have been used widely in travel behaviour research, namely the Theory of Planned Behavior (TPB) (Ajzen, 1991) and the Norm Activation Model (NAM) (Schwartz, 1977). Both theories share an expectancy-value structure that aligns with traditional economic analyses, portraying individuals as rational agents driven by utility-oriented preferences (Gardner & Abraham, 2008). Additionally, these theories are grounded in the assumption of conscious awareness and the perception of action consequences. However, a growing body of research has emphasised the influence of habits, which can lead to actions that occur outside of conscious awareness (Donald et al., 2014). Several reviews on the application of the TPB and/or NAM within the domain of travel behaviour are available, offering valuable insights into the extent to which commuting behaviour can reflect an individual’s intentions or habits (e.g. Si et al., 2019; Yuriev et al., 2020). Specifically, commute intentions are shaped by individuals’ positive or negative attitudes towards their work trip and the different transport modes available, the social pressure (i.e. social norms) and/or personal norms to make a particular choice for commute mode, and perception of the ease or difficulty of executing (i.e. perceived behavioural control (PBC)) a particular commute pattern (e.g. mode, route, time). This study will conclude with a heuristic model informed by the TPB and NAM of how highly disruptive changes in the transport context can differently affect daily travel mode use.

By studying pandemic commuters, we can derive significant implications for future research on commute mode use. The underlying principle is that atypical or extreme situations can provide valuable opportunities for understanding the deeper causes behind a given problem (e.g. here auto-dependency or barriers to low-carbon mode use) and its consequences than one can achieve through usual or “normal” situations where less basic mechanisms are often activated (Flyvbjerg, 2006). In that sense, understanding the complex relationships between the individual, physical, and socio-psychological factors that affected the travel of workers who continued to commute on a regular basis, but in an extremely changed context, can provide important lessons for understanding commute mode use. Finally, the wave of research concerning the mobility impacts of COVID-19 conducted during this period (between January 2020 and 2022) was unique and thus provides an excellent opportunity for analysis of travel mode use during highly disruptive circumstances.

The remainder of this paper is organised into four parts. The second section will summarise the scoping review approach, explain how it has been adapted for the purpose of this study, and describe how the adapted method has been employed. The third section will summarise the findings from the systematic review of pandemic commuters travel to/from work. The fourth section will synthesise the findings into a heuristic model of travel mode use and provide theoretical implications for understanding the traditional theories of mode choice modelling. Finally, the conclusion section will present the study’s main results and contributions and discuss priorities for future research deriving from the reported work.

2. Methodology

An adapted version of the PRISMA 2020 method for scoping reviews (Page et al., 2021) has been deployed to conduct and report the review of relevant publications in peer-reviewed, English-language academic journals across diverse academic disciplines published in 2020–2022. Scoping reviews differ from conventional methods of literature review in that they include a robust research method that reduces bias by being transparent and accurate while providing a complete overview of the literature according to the research aims and objectives.

2.1. Search strategy

A search for pertinent articles was conducted with the assistance of a library scientist in September 2022 using five different databases: Web of Science, Scopus, PubMed, Google Scholar, and Transportation Research International Documentation (TRID). Based on the main research topic, “Influence of COVID-19 on the travel behavior¹ of workers”, the research protocol consisted of variations of three sets of keywords. These included travel behaviour (e.g. “means of transport” OR “commuting behavior”) AND COVID-19 (e.g. “pandemic” OR “coronavirus”) AND worker (e.g. “employee” OR “personnel”) which were searched for in the articles’ title, abstract, and keywords. A complete description of search terms and Boolean combinations is provided in the Appendix.

2.2. Study selection and inclusion/exclusion criteria

The selection process was performed in different steps: (1) search query design (explained above); (2) screening based on title, abstract and keywords; and (3) study selection based on full text. In the identification step, 17 additional records were identified through citation tracking and recommendations among the authors. However, upon further investigation, we discovered that all 17 of these records were already included among the initial set of 1885 papers. Consequently, these duplicates were removed during the elimination process. While we anticipated this outcome from the outset, we opted for a transparent approach and provided this level of detail to ensure that no papers were inadvertently overlooked. Step Two was conducted in two rounds to efficiently manage the large number of papers identified in the initial search. In the first round, a quick review of the abstracts, titles, and keywords allowed us to exclude papers that were clearly irrelevant to our research question (e.g. transport of blood cells, (bone) joint mobility, healthcare employees travelling for non-work purposes). This reduced the number of papers for the second round which involved a more detailed evaluation of the remaining papers to determine their relevance based on specific inclusion and exclusion criteria. Additionally, the papers were classified based on the predominant commute mode shifts discussed (e.g. car use, active transport, or both) during the screening process for easier data overview. These classifications were for organisational purposes and did not necessarily influence the review and analysis sections of the paper. Citations collected from the five databases mentioned above were managed using Endnote 20.4. Figure 1 illustrates a flowchart of the selection process.

Figure 1. Flowchart of the article selection process.

The authors agreed upon the inclusion and exclusion criteria to make sure that only the most relevant publications were collected (Table 1). With respect to the criteria, first, included articles were expected to be focused on travel behaviour. More specifically, we were interested in travel to/from work (i.e. commuting) behaviour of workers during the pandemic. While our initial focus was on key workers’ commuting, the literature review revealed challenges in precisely delineating key workers as a distinct segment within the commuting population. Consequently, our findings disproportionally pertain to key workers but also extend to other individuals who did not have an essential job but had to commute while some were able or forced to stay at home. Therefore, we use the term “pandemic commuters” as a broader category that encompasses both essential/frontline/key workers and non-essential non-teleworkers (see Appendix for the complete search query). Regarding the research design or socio-demographic characteristics of the studies population, no restrictions were set. From the 1885 identified records, after removing the duplicates, screening for pertinent titles, abstracts, and full texts, 36 publications that matched the eligibility criteria were selected. Further details about the included studies can be found in Table 2.

Table 1. Eligibility criteria to selection of publications.

Selection criteria	Inclusion criteria	Exclusion criteria
Methodology	Empirical (quantitative and qualitative), Theoretical, Conceptual	None
Quality criterion	Indexed, peer-reviewed academic journals	Books, professional journals, grey literature
Language of publication	English	Other languages than English
Studied behaviour	Commute to/from work including the choice of travel mode, and/or route, and/or time during the COVID-19 pandemic	Non-work travel behaviour (changes) because of the pandemic (e.g. grocery shopping and social or recreational activities) Working from home (telecommuting) Non-urban transport (e.g. Tourism travel behaviour, air travel, etc.) Travel-related attitudes, feelings, and emotions without considering the traveller’s choices of mode, route, and/or time Inequalities associated with work trips (e.g. concerning socio-demographic characteristics of travellers) without any study of the travel characteristics such as mode, route, or time
Studied context	All types of job including non-essential and essential, critical, frontline, or key, workers (e.g. healthcare professionals, delivery jobs, employees of the PT sector, etc.)	Commute travels of cross-border or migrant workers Commute behaviour changes as a result of life-changing events (e.g. workplace relocation) other than the COVID-19 pandemic Analysis of telecommuting and reductions in number of commute trips merely from the standpoint of traffic control, carbon emission reduction, and transformation of urban spaces without regards for individuals travel choices
Unit of analysis	Individual (adults/children)	Organisational

Table 2. Characteristics of included studies and overview of the results.

Study	Methodology	Key variables investigated	Before-pandemic	During-pandemic	Post-pandemic	Main findings
Geography
1. Abdullah et al. (2020)	One online survey, descriptive and regression analysis (Social media) N = 1203	Trip purpose, mode choice, distance travelled, frequency of trips, underlying factors for modal choice, socio-demographic	For primary trips: Car: 32% PT: 36% Active: 12% (Walking: 8%)	Time (of survey completion) May 2020 (lockdown)	Not available	Infection-related factors (social distancing, cleanliness, passenger’s face mask) became more important than travel time savings, comfort, and cost
Country Various countries around the world				For primary trips: Car: 39% PT: 13% Active: 20% (Walking: 15%)
2. Amin and Adah (2022)	One online survey, Regression analysis (Social media) N = 393	Factors affecting riding public buses for commute, buses’ crowdedness, socio-demographic	Not available	Time Oct. to Nov. 2020 (lockdown)	Not available	Perceived risk of infection; Operational effects on transfers, and keyworkers’ willingness to share ride significantly reduced their use of public buses; Low-income people were most trip makers and adversely affected by the pandemic
Country Abuja and Lagos, Nigeria				Car owner keyworkers used their car, keyworkers with no access to car used buses but avoided crowded ones while young, single, low-income females were forced to use public buses
3. Angell and Potoglou (2022)	One online survey, July 2020, (after UK’s 1st wave of infections & deaths) (Social media) N = 211 (for before and future intentions) N = 40 (during)	Travel frequency, mode choice, departure times to/from work, mode-specific attitudes, adaptive capacity (possibility of WFH)	Car commute: 57% PT commute: 29% Active commute: 14% (Cycling: 11%) Majority of car users being captive and majority of non-car users being choice users	Time March to May 2020 (lockdown)	Expected mode choices: Car: 59% PT: 18% Active: 23% Expected increase/decrease in mode use: Car use: 2.3% Active: 9% Public: −11.4%	Main reasons for not using PT: Government’s guidelines to avoid PT use + individual’s own high perceived risk of infection + adaptive capacity by WFH; Active travel loyalty; Car users’ intention to switch to active modes post-pandemic; Longer travel time with PT than car, PT and cycling infrastructure inadequate and unsafe
Country Wales and Cardiff, UK				Car was the most preferred commute mode Majority of respondents worked from home (high adaptive capacity) A fall in traffic volume
4. Anwari et al. (2021)	One online survey, regression analysis (Social media) N = 572	Travel mode and frequency (before and during), socio-economic	Mode choice: PT: 37% Car: 11% Non-motorized: 17% Walking: 11% Frequency: Telecommute: 2% 3–6 time weekly: 35%	Time May to June 2020 (lockdown)	Not available	The pandemic resulted in significant variations in mode preferences but small variations in trip frequencies; Inadequate ICT in rural and suburban areas prevented work trip reductions in these places; Higher income people increased their car reliance while low-income people remained PT users especially for long distances; Men at higher risk of infection vs women due to higher travel frequencies for commute
Country Bangladesh				Mode choice: PT: 19% Car: 25% Non-motorized: 21% Walking: 19% Frequency: Telecommute: 24% 3–6 time weekly: 14%
5. Assoumou Ella (2021)	Covid mobility report developed by Google and government data	Mode choice based on gender and occupation category	Use of PT: Women: 60% Men: 40%	Time A comparison between Jan. & Feb. 2020 as baseline and March to May 2020 (lockdown)	Not available	Women at higher risk of infection vs men due to higher travel frequencies for commute (and family responsibilities) using PT + women’s higher engagement in essential occupations resulted in major gender inequalities
Country Belgium				Not available
6. Balbontin et al. (2022)	One online survey, Mixed Logit Model (Online sampling across Australia but the study focused on two regions) N = 650	Modal choice, possibility of WFH, workplace location, travel-related attitudes, socio-demographic	Car commute: 62% PT commute: 30% Active commute: 8%	Time Sept. to Oct. 2020 (no lockdown and relatively minor restrictions on workplaces and public gatherings)	Not available	The bio-security concern with using PT is a key driver of WFH and choosing to commute by private car especially for: (1) those who worked in CBD areas because of travelling in high-density PT setting pre-COVID; (2) those who had to queue longer than prior to COVID because of the virus transmission risk (∼10% less likely to take PT compared to those who didn’t have to queue) (waiting time could be a proxy for reliability)
Country New South Wales and Queensland, Australia				Car commute: 68% PT commute: 20% Active commute: 12% Of those who used PT before pandemic 27% shifted to car, 11% shifted to active modes, and the rest remained on using PT
7. Basu and Ferreira (2021)	One online survey over six months, 10 interviews, statistical analysis N = 2200	Travel mode choice and perception, car purchasing intentions, socio-demographic	Average mode share from 2014 to 18: Car commuters: 78% Non-car commuters: 22%	Time April (lockdown) to October 2020	60% of all respondents (car and non-car) expressed confidence that people will go back to using transit Due to negative perception about PT over 25% of zero-car households intended to purchase a car within one year	Negative perception of mass transit being “unclean and crowded” existed among car commuters; The Transport Authority provided real-time crowding information for busy bus routes in support of low-income transit-dependent essential workers
Country Boston, USA				At the onset of the pandemic an 85% drop in mass transit ridership was observed and flowed by a very slow recovery Anticipation of reduction in vehicle price led to a strong possibility of non-car-commuters to purchase a car and shift from PT to car for commute
8. Borkowski et al. (2021)	**Computer-assisted telephone interview survey questionnaire, (Verified sampling), regression analysis N = 1069	Modal split, travel time, fear of infection, socio-demographic	Not available	Time March to April 2020 (lockdown)	Not available	Significant decrease in commute time was observed due to decreased use of PT during the lockdown period; Those who did not have access to a car preferred to telecommute; The higher the level of fear of being infected, the higher travel time decrease
Country Poland				Travel time decreased especially after the government mandated restrictions – certain essential jobs such as defence personnel experienced increased travel time
9. Bria et al. (2021)	One online survey of formal sector employees, descriptive analysis N = 430	Travel mode, perception of health protocols in PT, socio-demographic	Not available	Time Not available	Not available	Even though they perceived health protocols to be inadequate, PT remain a preference among low-income people but not among high-income commuters who perceived PT’s health protocols as non-optimal; Car ownership and age were positively related to less use of PT
Country Jakarta, Indonesia				Health protocols such as social distancing and hand washing/sanitising in PT and station areas increase people’s interest in using PT again during the pandemic
10. Campisi, Basbas, et al. (2022) and Campisi, Tesoriere, et al. (2022)	One online survey, descriptive statistics (Social media) N = 700	Correlation of psychological aspects (i.e. fear, anxiety, and stress) with socio-demographic variables and modal choice habits	Walking for commute: more than 30%	Time Oct. to Nov. 2020 (one month after lockdown)	Intentions for walking for commute: close to 40% 50% believe that telecommute has lowered the frequency of walking which can lead to habit of walking less for commute (compared to pre-pandemic) in the future	Telework resulted in a smaller number of trips weekly but larger amount of distance travelled because employees were more willing to commute farther considering they did not commute as often; Commuters experience high levels of fear, stress, and anxiety. Despite these emotions, many people continued using PT because of having no other option, financial incentive from the government, or unfavourable environment for driving.
Country 9 cities in Sicily, Italy				Walking for commute: more than 40% 90% of respondents consider walking as a stress reliever
11. Caulfield et al. (2021)	One online survey partly included a set of open-ended questions for students and staff to explore travel intentions when the university reopened, descriptive statistics and cluster analysis N = 2653	Modal choice, choice of working location, socio-demographic	Car commute: 5% PT commute: 69% Active commute: 26% (Cycling: 10%)	Time June to July 2020 (university was mainly closed and social distancing was obligatory)	Car commute: 16% PT commute: 26% Active commute: 55% (Cycling: 28%) E-scooter and other: 3%	Due to inadequate PT during peak hours and lack of enforcement for health guidelines during COVID, feelings about chances of contracting COVID in PT were significantly higher compared to when walking and cycling for commute; But surprisingly, participants didn’t believe that car commuting will save them from contracting COVID; Majority of participants acknowledged that widening of pedestrian facilities and the increase in safe and segregated cycling facility would certainly lead them toward the use of the modes for commute.
Country Dublin, Ireland				Students mainly walked and staff mainly cycled to university if they needed to travel. However, due to narrow footpaths and larger volume of people, some respondents felt impossible to follow social distancing rules
12. Chen et al. (2021)	One online survey N = 172	Modal choice, Ability and preferences for WFH	Drive/carpool/car share: 38% PT commute: 46% Active commute: 11%	Time Oct. to Nov. 2020 (university was mainly closed and social distancing was obligatory)	Drive/carpool/car share: 47% PT commute: 35% Active commute: 13%	Increased intention for car use and active modes, and reduced attractiveness of PT due to perceived risk of COVID transmission; Participants indicated even higher preference for car use after COVID compared to during the pandemic because of reduced overall commuting by telecommute; Continued use of masks and social distancing on transit was mentioned to help increase PT use the most
Country Vancouver, Canada				Drive/carpool/car share: 45% PT commute: 35% Active commute: 15%
13. Cusack (2021)	One online survey of essential workers in Philadelphia who actively commuted to work, regression analysis (Social media) N = 213	Modal choice, Individual, social, environmental factors associated with active commuting during COVD	Commute mode of half of the respondents who changed their mode during the pandemic: PT: 73% Driving: 10% Bicycling: 8% Walking: 4%	Time June to Aug. 2020	Not available	Safety, cleanliness and limiting potential exposure to COVID-19 plus changes to public transit schedules were mentioned as main reason for changing commute mode; Reduced congestion and road closures led to safer bicycling, while for drivers, fewer cars led to easier parking; Lower odds of bicycle commuting among non-whites.
Country Philadelphia, Pennsylvania, USA				Commute mode of all the respondents: PT: 19% Driving: 29% Bicycling: 31% Walking: 20%
14. DeWeese et al. (2022)	One online survey asking for three time periods in 2020 (Jan., April, Sept.), descriptive statistics N = 1580	Commuting patterns (weekly trips and mode), occupation status	Commute in January Car: 9% Sustainable modes: 85%	Time April 2020 (Lockdown)	Not available	Car-only commuting mode share almost tripled at the start of the pandemic (April) but then slightly changed to a combination of car and PT during the summer when perceived risk of infection somewhat decreased.
Country Montreal, Canada				Commute in April: Car: 26% Sustainable modes: 70% Commute in Sept.: Car: 20% Sustainable modes: 75%
15. Dias et al. (2021)	One online survey, descriptive and regression analysis (Social media) N = 435	Travel mode and frequency, reasons for behaviour change and importance of COVID in travel decisions, socio-demographics	Not available	Time March to April 2021 (pandemic’s 3rd wave, restrictions in effect in some regions)	Not available	Cleanliness, infection concerns, social distance and wearing masks were more important for females, car owners, married respondents and essential workers than others; Car owners were more concerned about pandemic-related factors when making travel choice (placing higher priority on health, family health and comfort over the economy); Essential workers three times more likely than the general population to make a work trip during the pandemic; Government and private employees more likely to commute
Country Malaysia				Commute modes: Car: 85% PT: 5% Motorcycle: 6% 18% reduction in travel for work as the primary travel purpose
16. Teixeira et al. (2022)	One online survey, descriptive statistics and regression analysis (Social media) N = 215	Travel mode, perception of safety, socio-demographics	Commute mode: Car: 11% PT: 36% BSS: 22% Walking: 20% 43% used BSS for commute	Time Sept. to Oct. 2020 (no lockdown, start of the second wave)	Not available	The perceived safety of both PT and cycling facility was decreased but that of the cycling was in a much smaller scale; 58% decrease in safety perception of using PT and 11% increase in that of BSS; New users of the BSS had much more positive perception regarding the safety than former users; Walking was found to be the safest commute mode
Country Lisbon, Portugal				Commute mode: Car: 15% PT: 22% BSS: 27% Walking: 23% 39% used BSS for commute 23% decreased or quit the GIRA to avoid COVID infection 58% decreased or quit because the trip purpose no longer existed 69% of those who joined
17. Garcia et al. (2022)	One online survey, descriptive statistics, qualitative analysis of one open-ended question about a “better normal” (Social media) N = 255	Travel behaviour and attitudes towards Environmentally Sustainable Transport (EST)	Average commute mode for different frequencies of trip to work per week: Car: 62% PT: 38%	Time Aug. 2020 (lockdown, PT not operating and non-essential movements were restricted)	Considering purchase of a car within one year n = 26: Related to COVID: 65% Not related to COVID: 35%	Participants showed concerns regarding PT availability (75%), safety (69%), travel time (68%), and possibility of COVID infection (65%); Where PT is not as safe as before, participants aware of EST concern about using/purchasing electric cars.
Country Philippines				Stated preference for car and private modes use: 47% PT use but with social distancing and personal preventive measures: 33% (Captive PT users: 7%) Active modes: 20% (cycling: 14%)
18. Harrington and Hadjiconstantinou (2022)	One online survey concerning before, during and after COVID choices, open-ended question on underlying reasons for travel choice (Social media) N = 725	Modal choice, travel purpose and frequency, socio-demographics	Commute mode: Car: 72% PT: 28%	Time May to June 2020 (lockdown)	Of the car commuters after restrictions are lifted: Continue car: 82% Switch to PT: 0% Switch to active modes: 11% Of the PT users: Continue PT: 49% Switch to car: 18% Switch to active modes: 21%	Two barriers of switching to active modes: (1) Perceived behavioural controls (e.g. long commute distance, unsafe cycling infrastructure, weather conditions); (2) Lifestyle (e.g. job requirements and family responsibilities) Motivators of switching to active modes: provision to support cycling (showing facility) and intrinsic, personal, environmental benefits.
Country Bristol, UK				Of the car commuters when restrictions were in place: Continue car: 50% Switched to active modes: 12% Switched to PT: 0% Of the PT users: Continued PT: 3% Switched to car: 11% Switched to active modes: 32%
19. Irawan et al. (2022)	One online survey, structural equation modelling (Social media) N = 1062	Travel mode and frequency, attitudes, descriptive norms, use of ICT, socio-demographics	Commute mode: Motorcycle: 60% PT: 25% Active modes + motorcycle-based ride hailing: 10%	Time March to April 2020 (no lockdown yet)	Not available	There was an inverse relationship between frequency of travel and beliefs about how serious of a problem COVID is. Socio-demographics didn’t affect activity-travel behaviour but directly affected ICT use; Only income was significantly positively associated with ability to telecommute; Understanding of the risk involves learning curve of people to adapt to the pandemic situation 90% of the respondents perceived the pandemic to be very dangerous and extremely dangerous 87% significantly changed their activity-travel behaviour
Country Several provinces across Indonesia				Commute mode: Motorcycle: 34% Not travelling: 40% PT: 20% Active modes + motorcycle-based ride hailing: 5%
20. Jamal et al. (2022)	Semi-structured interviews, thematic analysis (Social media) N = 20	Commuter’s preferences and dilemmas of transport choice post-pandemic	Not available	Time July and Aug. 2020 (partial lockdown – essential services open, transit run at 50% capacity)	Not available	Commuting was a trade-off between health risk, affordability, and availability of suitable modes so commuters did not have the ease and flexibility to switch to their preferred safer mode
Country Dhaka, Bangladesh				Not available
21. Javadinasr et al. (2022)	Two-wave panel survey data, descriptive statistics (Social media) N = 2973	Pandemic-induced changes and underlying reasons of commute mode choice, travel attitudes, socio-demographics	Commute mode: Car: 72% Transit: 11% Walking: 3% Not commute: 11%	Time April to Oct. 2020 (Wave 1) Nov. 2020 to May 2021 (Wave 2)	Expected post-pandemic commute mode: Car: 66% Transit: 8% Walking: 2% Not commute: 19%	Although the transit share increased by 31% from wave 1 to wave 2, the post-pandemic expectations are significantly (32%) lower than the pre-pandemic period; Similar trend was also observed for walking; The number one concern for using less PT post-covid than pre-covid was the “I no longer feel safe/comfortable sharing space with strangers” by being selected by 73% and 63% of the respondents in wave 1 and wave 2, respectively; The second main reason was WFH.
Country Different states across USA				20% increase in having the option of WFH Wave 1 commute modes: Car: 40% Transit: 3.5% Walking: 1.6% Not commute: 40% Wave 2 commute modes: Car: 46% Transit: 4.6% Walking: 1.8% Not commute: 38%
22. Kamelifar et al. (2022)	One online survey, regression analysis N = 603	Travel patterns and perceptions of neighbourhood before and during the pandemic	Commute mode: Car: 41% PT: 16% Active modes: 16% (Walking: 14%) Taxi: 17% Organisational service: 7%	Time June and July 2021	Not available	During the pandemic, most variables that influenced walking for commute lost their significance as people were more likely to choose other modes or prefer not to commute due to WFH; Among the variables, perceived distance to job/university, commute distance, neighbourhood types, and shortcut routes remained significant, possibly because of their crucial impact on utilitarian walking.
Country Tabriz, Iran				Commute mode: Car: 47% PT: 7% Active modes: 20% (Walking: 18%) Taxi: 20% Organisational service: 7%
23. Kar et al. (2022)	Used data from O-D travel flow (travel time and frequency between each origin and destination)	Travel patterns based on socioeconomic status (SES) (based on income and ethic)	Low trip frequency and localised short-distance trips for low income Moderate and high SES groups’ travel pattern were diffused with higher number of long-distance trips	Time During the lockdown in 2020	Not available	Disparities in travel among low, medium, and high SES clusters that existed before the pandemic were exacerbated during the lockdown; While low SES clusters experienced more diffused and long-distanced commute trips, high SES clusters travelled mostly for recreational and non-work purposes with short-distance trips.
Country Columbus, Ohio, USA				Fewer WFH opportunities for low SES people forced them to travel during the pandemic The pandemic made the travel patterns more segregated and localised for high SES people due to better accessibility and job flexibility
24. Kroesen et al. (2023)	**Longitudinal online survey, statistical analysis (Verified sampling) N = 14760	Train use frequency, WFH frequency, attitudes toward train use, attitudes toward WFH	Not available	Time April, June, Sept., and Dec. 2020	Not available	There is a bidirectional relationship between train use and the attitudes towards train use. Participants with a stronger fear of infection in one wave were more likely to avoid train in a subsequent wave. Also, the more participants used train in one wave the more they became less fearful in the next wave.
Country The Netherlands				Not available
25. Liao (2021)	Open-data (Google Mobility) and governmental data sources, regression analysis	Travel frequency and socioeconomic status	Not available	Time January to February (baseline) February to December 2020	Not available	Higher social vulnerability and lack of insurance is associated with smaller reductions in commuting trips; Vulnerable and disadvantaged populations had disproportionately borne adverse impacts during the pandemic
Country Pacific Northwest region of USA (Washington, Oregon, Idaho, and Alaska)				Unlike other studies areas with higher racial diversity showed higher reduction in commute trips due to various programmes that support minorities at the local level Large household size was associated with reductions in work trips due to the need to stay home for childcare
26. Olde Kalter et al. (2021)	One online survey and smartphone-based GPS-data of before and during the pandemic, regression analysis N = 1515	Travel behaviour during COVID and intentions to change commuting patterns after that, socio-demographics	Travel mode for all trips: Car: 58% PT: 4% Active modes: 37% (cycling: 21%)	Time January to February (baseline)	Intentions to use car for commuting after the pandemic decreased mainly due to WFH opportunities (mostly for office workers) Employees with the same travel days expect an increase in car use (+1%) and active modes (+6%), while PT will be used less frequently (− 10%)	The pandemic forced people into new behaviour and left them with no time for re-considering existing habits; Higher education positively related to WFH
Country The Netherlands				Office workers and teaching staff were more likely to increase the amount of time for WFH Car and PT were used less frequently while active transport increased
27. Phandanouvong et al. (2021)	One online survey, Google Form, regression analysis (Social media) N = 217	Travel behaviour of commuters for key destinations (workplace, market, hospital) during the pandemic	Not available	Time March to April 2020 (public bus was suspended) university jobs were under stay-at-home order	Not available	Men were more likely than women to commute by motorcycle, followed by car and active modes (higher likelihood of women to stay home); Higher educated individuals were more likely to go to work than to stay at home; They were most likely to travel by active modes, followed by car and motorcycle because of their high income (close residential access to workplace and car ownership); Low income and low level of education were associated with staying at home.
Country Cambodia and Laos, South Asia				Commute mode: Car: 22% Bus/van: 2% Motorcycle: 33% Active modes: 7% Telecommute: 36%
28. Plyushteva (2022)	Public engagement project (interview), qualitative analysis N = 70	Challenges of essential workers regarding their pandemic commuting	Not available	Time March and June 2021	Not available	Discussed financial and emotional implications related to the commuting during the pandemic.
Country UK				Not available
29. Przybylowski et al. (2021)	One online survey, descriptive analysis N = 302	Travel behaviours before and during the pandemic	PT use: 57% used everyday 24% used few times a week 19% used less regularly	Time May and June 2020	Willingness of passengers to resume using PT after the pandemic depends majorly on their feeling of comfort and safety during the pandemic not socio-demographic, travel frequency, place of residence, and pre-pandemic comfort and safety	Among the various reasons affecting the comfort and safety of use of PT during the pandemic these were: number of passengers and their behaviour, tidiness, air-conditioning, fear of becoming infected and fear of insufficient disinfection.
Country Gdansk, Poland				42% switched from PT to car Reasons for not using PT or using it less often: Telecommute: 49% Fear of infection: 40%
30. Schaefer et al. (2021)	One online survey, regression analysis N = 4359	Factors affecting mode choice during the pandemic, mainly fear, socioeconomic	Not available	Time 15th to 30th June 2020 (restrictions were lifted)	Not available	Fear had a strong negative effect on PT use; Fear played a mediating role masking the effect of socioeconomics on PT use; When fear was used as a dependent variable, women showed a higher level of fear of infection compared to men and reduced PT more; Income was positively associated with car use and less use of PT, but cycling was independent of socio-demographic factors and more linked to environmental awareness of the users.
Country Hanover region, Germany				Not available
31. Sy et al. (2021)	Geographic data (126 zip code tabulation areas with a subway station) and subway data	Subway use and socioeconomic factors	Mean subway use was over 25 million swipes per week	Time January 4th to April 11^th, 2020 (Lockdown started in March)	Not available	Essential work is what drives subway use in areas with lower socioeconomic residents (e.g. low-income, non-white and/or Hispanic/Latino); High income zip code areas had the greatest decrease in mobility and subway use
Country New York, USA				70% decrease in subway use (less than 8 million weekly)
32. Szczepanek and Kruszyna (2022)	One online survey, descriptive analysis N = 815	Travel patterns of a university employees before, during and after COVID, distance, PT standards	Commute mode: Car: 33.5% PT: 33.5% Active: 33%	Time June and July 2021 (restrictions were lifted)	Car: 39% PT: 26% Active modes: 35%	The positive association between PT standards (e.g. density, speed, number of transfers, duration of intervals) and the share of transport modes were evident and stronger than the pandemic itself
Country Wroclaw, Poland				Car: 49% PT: 18% Active modes: 33%
33. Tan and Ma (2021)	One online survey, regression analysis N = 559	Use of rail transit during COVID, socio-demographics and perceptions of travel and COVID	Commute mode: Car: 25% Bus/subway: 31% Bicycle/electric cars: 29% Walking: 9% Taxi/taxi-hailing: 4%	Time Not available	Not available	The likelihood of commuting by rail transit was negatively related to the following factors: perception of risk of infection in PT, being self-employed, having used non-PT modes before COVID, long home-subway walking distance; Old habits of using PT positively influenced rail transit during pandemic
Country China				57% showed to have high or very high levels of anxiety from COVID Among those who didn’t choose rail transit: Car: 40% Bicycle/electric cars: 36% Walking: 21% Taxi/taxi-hailing: 3%
34. Teixeira and Lopes (2020)	Data from NY’s BSS’s trip history (start and end time of trip and stations used), Regression analysis	Transit and bike ridership changes during the pandemic	Not available	Time 18 to 24th of March 2020	Not available	Bike sharing system (BSS) proved to be more resilient than subway with a less ridership decrease (71% vs 90%) and a 6-min increase in its trips’ average duration
Country New York, USA				33% increase in BSS ridership on the day that subway use started to fall Critical workforce membership programme which was launched to encourage essential workers ridership (free trips for a month and increased disinfection of the bicycles and stations) was not successful on its first week of adoption
35. Thomas et al. (2021)	One online survey, regression analysis (Social media) N = 787	Travel patterns and attitudes towards car use, car sharing, and PT	Regular PT users: Australia: 36% New Zealand:14% High positive attitudes towards PT	Time July, August 2020(After restrictions were removed) August, September 2020(Lockdown)	Moderate positive attitudes towards PT Slight increased intention to use PT	When asked to think about post-COVID restrictions, respondents showed a more positive attitude toward PT compared to during the pandemic, but still lower than pre-COVID situation; Regular users of PT had higher levels of positive attitudes towards PT before, during and post pandemic compared to non-regular users
Country Australia and New Zealand				Low positive attitudes towards PT
36. Kim and Kwan (2021)	Mobility data from mobile phone signals (7-month longitudinal study)	Travel behaviour changes by considering various social, spatial, policy, and political factors	Not available	Time March to June 2020 (Wave 1) June to Sept. 2020 (Wave 2)	Not available	Poor people tend to continue commuting during COVID because they largely consist of essential workers such as employees of grocery stores and food processing factories; Mobility restrictions were not as effective as wave 1 in wave 2 even though COVID became more severe
Country 2639 counties in USA				V-shaped mobility trend: mobility declined from March to April but rapidly got back to the pre-COVID level from April to June. Despite the existence of travel restrictions and the pandemic becoming more sever, there was insignificant mobility decline in wave 2

3. Findings from the reviews

3.1. Lack of consistent definition

Among the reviewed literature, there is a lack of consistent criteria or definition for identifying pandemic commuters, especially in relation to workers with essential jobs. Some studies provided limited information, making it challenging to determine whether the participants were essential or non-essential workers. However, the majority of pandemic commuters were observed to be younger, low-income individuals from minority ethnic/racial groups who did not own cars and typically held jobs that did not offer the option to work from home (WFH). Examples of such include non-white, Hispanic/Latino in U.S. (Sy et al., 2021), young low-income frequent bus commuters in Indonesia (Bria et al., 2021), and transit-dependent keyworkers in Boston, U.S. (Basu & Ferreira, 2021). Generally, the significance of some socio-economic variables such as income was consistent across the studies whereas for some others context-specific outcomes were observed such as gender, with more female keyworkers in Belgium (Assoumou Ella, 2021) versus more male keyworkers in Bangladesh (Anwari et al., 2021). Concerning the keyworkers’ job categories, limited information is provided by the studies. One study in Belgium (Assoumou Ella, 2021) described that the majority of essential workers were female and held occupations with the highest exposure to infection including health-care professionals (e.g. nurses and medical secretaries), supermarket cashier, childhood educators, and cleaners. Other studies that categorised their participants into professional versus non-professional roles (Angell & Potoglou, 2022) or high-skilled white-collar Information and Communication Technology (ICT)-related versus low-skilled blue-collar jobs (defined as technicians and trades, machine operators/drivers and labourer) (Anwari et al., 2021; Balbontin et al., 2022) found that the latter category had limited capacity to WFH than the former.

3.2. Quadruple selectivity

There is a quadruple selectivity regarding spatial, sampling, behavioural, and temporal aspects in the published literature on pandemic commuters. First, studies from around the world have been included, although they are dominantly from the global North. The majority of the papers come from Europe: Poland and U.K. (three each), the Netherlands (two), Germany, Belgium, Ireland, Italy, and Portugal (one each); North America: U.S.A. (eight) and Canada (two); and Australia (two). However, studies from Asia (eight), the Middle East and Africa (one each) were also retained.

Second, 25 out of 36 studies applied an online sampling method by distributing their questionnaires either through survey companies or social media channels such as Facebook, Twitter, and WhatsApp while targeting a general population of workers belonging to different unknown workplaces, and without verification of sampling accuracy. This method not only reduces the accuracy of screening participants for relevancy but also results in a higher percentage of younger participants with higher education levels due to their active presence in social media (e.g. Dias et al., 2021) as well as an under-representation of extremely low-income households (Schaefer et al., 2021) and minorities (e.g. Basu & Ferreira, 2021).

Third, studies have focused more on pandemic commuters’ mode use than on other aspects of commuting behaviour such as frequency, route, distance, and time of day. There have been many studies on telecommuting by workers in general, but few have looked at how pandemic commuters have changed the frequency with which they travelled to work. Only in one study, among 572 individuals in Bangladesh in May–June 2020, Anwari et al. (2021) found that while certain occupations (e.g. service-holders and businessmen) shifted from trip frequencies of more than 5 times a week to WFH by 10%, day labourers did not have such option especially in rural areas with weaker ICT access.

Fourth, published studies have mostly focused on the first wave, or at least early stages, of the pandemic. At present, there is very little research on commuting in later waves/stages. This is significant due to (1) the difference between the pandemic waves regarding travel restrictions and breaking news and (2) the effects pursuing beyond a given wave. The latter point suggests that, participants who are studied during the second or later waves of the pandemic, are different from those studied during the first wave (i.e. between March and May 2020 in most countries) due to the cognitive, affective, and behavioural mechanisms they involved in over time (van Wee et al., 2019). It is argued that because of being exposed to new knowledge (e.g. about risks of different ways of transmission), experiencing new emotions or engaging in new behaviours, individuals’ mobility conditions in subsequent waves can hardly be considered similar to those in the initial wave. For example, a person comes to believe that PT use increases COVID transmission risks, may then start driving (or walking or cycling) for their commute and come to prefer the new mode. Subsequently, this affective outcome is likely to strengthen the behaviour and/or cognitive beliefs (Kroesen et al., 2023). As a result, the person is no more avoiding PT because of the risks of infection but because of a preference to the new mode.

Among the 3 studies that applied a longitudinal panel method, in a study among thousands of commuters in the Netherlands, Kroesen et al. (2023) discovered that train users with a stronger fear of infection in one pandemic wave tend to use that mode less in a subsequent wave. This study emphasised the bidirectional effect between emotions and behaviour by also showing that higher use of the train led to a decline in the perceived fear of infection. In another study in the U.S., Kim and Kwan (2021) also used mobility data obtained from people’s mobile phone signals in 2639 counties from wave 1 (March-June) and wave 2 (June-September). The authors found that there was very little change in mobility levels in wave 2 compared to the V-shaped (sharp decline and then return to the pre-pandemic) trend in wave 1. This was due to social distancing inertia and quarantine fatigue despite the existence of mobility restrictions and COVID cases climbed. Javadinasr et al. (2022) also targeted nearly 3000 respondents across various U.S. states in wave 1 (April–October 2020) and wave 2 (November 2020–May 2021). The authors not only reported a 31% increase in transit use from wave 1 to wave 2, but also a 10% decline in the share of participants who stated, “I no longer feel safe/comfortable sharing space with strangers” (73% versus 63% in wave 1 and wave 2, respectively). It is not clear what the starting point of that response was, but the trends all point to a reduction in fear over time, though where that will plateau is not yet clear.

3.3. Variations in commute mode shift

At the aggregate level, a significant modal shift from PT to car and active transport use was observed. Studies reported a range of 9% to 90% decline in PT use, and 5% to 42% and 3% to 33% increase in car and active transport use respectively.² The main reasons for changing commute mode can be divided into two: concerns related to COVID-19 (e.g. cleanliness and crowdedness) and reduced service levels due to changes in PT capacity and schedules. The two main factors can also relate to each other as reductions in service levels could result in having to queue longer (Caulfield et al., 2021) or concerns related to crowded bus routes (e.g. when non-essential workers returned to regular commute) (Basu & Ferreira, 2021). One study showed that a greater ease of parking (due to fewer cars) at the workplace was one important reason for increased car commuting while reduced congestion and road closures led to 23% increase in cycling (Cusack, 2021).

3.3.1. Car use during the pandemic

The positive association of COVID concerns and less use of PT was found to be more significant in the case of car ownership and pre-pandemic habit of car use for commute. For instance, in a sample of 725 commuters in Bristol, U.K., in May–June 2020, Harrington and Hadjiconstantinou (2022) indicated that 50% of pre-pandemic car commuters (72% of the sample) continued using their car whereas only 3% of the pre-pandemic PT users (28% of the sample) remained on PT during the pandemic. Among the former group, 82% of employees expressed an intention to continue using cars even after the restrictions are lifted, while only 18% of the latter group did so. Data from 1580 commuters in Montreal, Canada, also revealed that car-only mode share that had almost tripled from 9 to 26 percent in the early pandemic (from January to April 2020) declined to 20% in the later pandemic (September) due to lower perceived risk of infection (DeWeese et al., 2022).

Nine studies found that those who already owned and/or used a car for commute were much more critical of PT and indicated higher negative perception of the mode (e.g. transit being unsafe) compared to regular transit users. In a study among 2200 individuals in Boston, U.S. from April to October 2020, Basu and Ferreira (2021) showed that car commuters (73% of the sample) were quite confident (77% agreement) that a private car possibly reduces the virus transmission risks whereas non-car commuters (27% of the sample) displayed a statistically significant lower level of agreement (69%) with this statement. In another study with 787 participants from Australia and New Zealand in July-September 2020, Thomas et al. (2021) found that non-regular PT users (78% of the sample) were more negative towards PT than their regular counterparts (mean attitude of 2.7 versus 1.9 where 1 = extremely positive and 5 = extremely negative). Data from 430 commuters in Jakarta, Indonesia revealed that car and motorcycle ownership had the strongest influence (after income) on choosing these modes for commute, regardless of the workers’ perception about health protocols in PT (whether inadequate, poorly adequate, or sufficiently adequate) (Bria et al., 2021). These findings highlight the possibility of scepticism among those who do not use a mode (often PT), and hence, develop an inaccurate negative perception of it (Zarabi et al., 2022).

Furthermore, in their Boston study, Basu and Ferreira (2021) found that the negative perception about mass transit increased the intention to purchase a car in 20% of zero-car households, especially active transport commuters. In a follow up interview, ten households that had purchased a car subsequently named the following reasons for their decisions: uncertainty regarding the transit service frequency, lack of trust in safety measures by the transit agency (e.g. overcrowded buses), and fear of other passengers not following the guidelines. A study from August 2020 in the Philippines similarly suggested the pandemic enhanced intentions to purchase a car, by showing that 6% of the 255 participants intended to purchase one within a year (Garcia et al., 2022).

3.3.2. Cycling and walking during the pandemic

People also chose active transport modes to replace PT trips, although cycling and walking witnessed a smaller boom compared to car. Bike sharing systems (BSS), were also demonstrated to be more resilient than subway with a lower decrease in ridership (71% versus 90%) (Teixeira & Lopes, 2020). This study which was conducted in New York, U.S. during March 2020 reported a 33% increase in the BSS ridership on the day that the subway use started to decline. In a study of the staff and students of the University of Dublin, Ireland (n = 2653) in June-July 2020, the preferred mode of commute after reopening of the campus was active modes with 55% of the sample stating preference for cycling (28%) or walking (27%) compared to 26% prior to the pandemic (Caulfield et al., 2021). In this study, in response to an open-ended question, many participants stated that they had previously considered active transport for their commute, but the inadequate PT during the pandemic gave them the final “push” to make that change. In Bristol, U.K., 22% of 725 survey participants who did not use cycling or walking for commute before the pandemic (only car: 72% and PT: 28%) switched to these modes during the lockdown (May-June 2020) and stated intentions to remain active commuters after the restriction are lifted, albeit at a slightly lower level (16%).

Two studies investigated behavioural beliefs related to active transportation. In a study among 700 commuters in Italy, one month after the summer 2020 lockdown, 90% of the respondents considered walking as a stress reliever and more than 40% of essential workers chose to walk for commute several times per week (compared to a 30% before the outbreak) (Campisi, Tesoriere, et al., 2022). The authors observed an absence of walking frequency beyond three times a day during the lockdown period. This was possibly a consequence of teleworking or loss of jobs as well as keyworkers’ modal shift to private car due to feelings of stress associated with the risks of virus transmission while walking for commute. However, the participants believed that the lower walking frequency could negatively influence their mental health. Similar observations were reported in a study on a sample of 213 commuters in Pennsylvania, U.S., in June-August 2020 (Cusack, 2021). Compared to their non-active counterparts, active transport commuters reported greater confidence bicycling in urban areas and the belief that active modes relieve stress and improve mental health.

3.4. Experienced emotions by pandemic commuters

In addition to the modal use and changes discussed above, the objective of this research is to assess how pandemic commuters perceived, interpreted, and emotionally experienced external changes and how this affected their commuting.

Negative emotions were a dominant finding. The majority of the reviewed papers (26 out of 36) referred to the pandemic commuters’ fear due to a perceived risk of infection as a barrier to use PT, especially in the case of a lack of cleanliness, and other passengers’ violating the guidelines (Dias et al., 2021). In a study among 1203 keyworkers in different countries across the world, respondents placed a high priority on COVID-related factors such as “infection concerns”(71%), “safety and security” (66%), “social distance” (64%) “cleanliness” (62%), and “passengers with face masks” (60%) (Abdullah et al., 2020). These concerns led to a 23% decline in PT ridership and 6 and 8 percent rise in car and active transport use, respectively. On the contrary, authors reported that factors such as cost, comfort and travel time saving lost their priority compared to the pre-pandemic situation. Data from a sample of 650 workers in Australia, in September-October 2020, also revealed that bio-security concerns (fear of virus transmission risk) related to crowdedness and long waiting queues of PT decreased the likelihood of commuters choosing mass transit by 12%. In a study from Poland, Przybylowski et al. (2021) found that 42% of the 302 respondents switched from PT to car, 40% of whom highlighted the following reasons for switching from PT: “number of passengers”, “fear of becoming infected”, “fear of insufficient disinfection”, and “fear of other passengers not following the hygienic regime”.

In a sample of 4,359 individuals from Germany’s Hanover in June 2020, Schaefer et al. (2021) explored the link between the fear of infection, commute mode and socio-economic variables. Fear was found to have a significant negative effect on PT use and to be a strong predictor of the increase in bike and car use. Fear was also proved to have a mediator effect in the strong association of gender and PT use as women showed a higher level of fear compared to men and therefore reduced PT more.

In Italy, Campisi, Basbas, et al. (2022) investigated the psycho-attitudinal variables of fear, stress, and anxiety of 700 bus commuters during three periods: pre-pandemic, post-lockdown, and wave two. The dominant emotions experienced by bus commuters were stress (∼90% of respondents) and fear (∼80%) during the pandemic’s post-lockdown and wave two with the former period associated with a slightly higher percentage. The anxiety level which was much lower than fear and stress during the post-lockdown period (∼53%), reached its peak (∼70%) during the second wave. Despite the existence of such negative emotions, people continued to use PT. Various explanations were found including having no other alternatives (captive riders), the provision of monetary incentives for PT by the government, and the unfavourable driving environment during the pandemic (e.g. travel delays due to police checkpoints of private vehicles).

Beyond concerns over infection, various other factors increased negative emotions. These included government-imposed travel restrictions (Campisi, Tesoriere, et al., 2022), deserted transport and urban spaces (Plyushteva, 2022), enduring face covering and continuous hand sanitising while commuting (ibid.), knowledge of new COVID cases (Thomas et al., 2021), coronavirus severity (Amin & Adah, 2022), lack of information about the virus, and situational uncertainty (e.g. from uncertainty around transit service frequency to economic uncertainty) (Basu & Ferreira, 2021).

4. Implications for understanding the use of commute modes

The review of the papers indicates that the pandemic commuting had various health, financial³, and emotional implications for pandemic commuters. These implications differed with respect to their level of influence and how they were dealt with. With pandemic commuters facing the challenges of the new mobility context, they had to adapt and if possible, reconsider their everyday commute patterns. However, the magnitude of this adaption, particularly regarding mode use depends on workers’ “adaptive capacity” which is a function of, among others, their travel-related choice sets which are conditioned by socio-demographic status and psychological characteristics, built environment features including transport infrastructure and geographical context (Rahimi et al., 2019), and the government mandated regulations discussed earlier (Angell & Potoglou, 2022; Kim & Kwan, 2021). Figure 2 illustrates the three main factors and provides examples of the emotional, physical, and financial implications discussed in the reviewed papers.

Figure 2. Factors that influence mode use for pandemic commuting.

Based on synthesis of findings from the reviewed literature, the subsequent sections will elaborate the following lessons for further research on commute mode use: (1) the assumption that all commuting results from individuals making choices is best avoided; (2) the role of (negative) emotions in commuting should not be overlooked; (3) the importance of proximity to, or separation from, other bodies on how people commute should be considered. Finally, we will discuss the implications for the use of the existing travel behaviour theories for understanding commute mode use.

4.1. Commute mode “choice”?

Based on the findings from the review, pandemic commuters primarily belonged to groups whose options were limited in some form or another. This was related to individual factors (female and/or minority racial groups) and/or financial constraints (car-free, low-income households mainly with non-ICT-related jobs) which were further exacerbated by the extreme conditions imposed by the pandemic. Consequently, these individuals did not have the luxury to shift away from PT and were constrained to select from limited alternatives, often compromising their health or taking the risk of losing their job. For instance, a delivery person who lives in a low-density environment and works in a peripheral location like a distribution warehouse and does not own a vehicle is forced to use a bus (or other PT modes) which is only – if ever – operating on an irregular schedule with highly reduced capacity due to government-mandated restrictions during lockdowns. Additionally, the person must confront the fear of infection as they are obligated to opt for in-person versus remote work in the first place, risking job loss otherwise.

These findings raise questions about whether all commuters have a true choice – understood as the ability to do otherwise if all other factors and conditions are the same – when it comes to the transport modes they use. Most contemporary transport research tends to assume so, despite scholars in the past already expressing doubts about this (e.g. Burnett, 1980; Swait & Ben-Akiva, 1987). However, a distinction is in order between the availability of choice alternatives and neurological processes responsible for choice.

The above results for pandemic commuters reinforce the importance of considering the availability of alternatives in travel behaviour research and adapting analysis and frameworks accordingly. This point has direct relevance in the application of the TPB and the NAM, because both assume that individuals have a degree of choice over whether to perform a given behaviour, such as driving a car or using PT for commuting. Neither model directly analyses the choice between two alternatives, say driving versus using PT, but both are still based on the assumption that people can do something or refrain from this if all other factors and conditions are the same. Based on our findings we submit that use of models like the TPB and NAM is best restricted to people who (have the luxury of) choice. Alternatively, these models need to be extended to consider the varying level of choice individuals are able to exercise over commute mode use (as in Section 4.4). Therefore, we carefully differentiate between the terms “mode choice” and “mode use” throughout this paper, using the most appropriate of the two where relevant. Beyond conceptual reasons, considering choice alternatives is vital from a social inequality perspective, as our results indicate that marginalised populations are more likely to have limited access to alternative commute modes.

In the random utility theory (RUT) approach to travel behaviour, the need to specify choice sets correctly has received some attention (Li et al., 2015; Swait & Ben-Akiva, 1987). It is argued that ignoring or inaccurate specification of choice sets, for instance because a certain proportion of the population is captive to a particular mode, can lead to a biased understanding of the associated behaviour. Work along these lines needs to be advanced such that considering choice set generation becomes standard practice in RUT-based analyses of commute mode use. We are similarly sympathetic to work by De Vos (2022) who has recently hypothesised that travellers are more likely to exhibit attitude-congruent travel behaviour if they enjoy large freedom of choice while attitude-incongruous behaviour is more likely if choices are constrained. It remains unclear, however, how freedom of choice can be assessed reliably in future studies.

In the longer term, transportation researchers should also pay greater attention to the neurological processes implicated in commute mode use. Since the first experimental studies showed brain activity to precede the time when human beings (appear to) make a conscious decision (Libet et al., 1983), there has been multi-disciplinary debate on whether human decisions are subjectively “free”, and whether intention is the antecedent of behaviour and choice or rather the “backward-looking” follow-up to the brain signals that initiate behaviour. While unresolved (Delnatte et al., 2023), this debate matters to transportation studies. Not only does it raise questions about the theoretical adequacy of models like the TPB and conventional understandings of travel choice, but also brings questions about the interplay of contextual changes like those during the COVID-19 pandemic with human brain processes: in how far was that interplay different for pandemic commuters than for other workers and why? This particular question can no longer be answered, but research on mode use, brain processes, and the social and spatial context in which these occur is a particularly fertile area for future research (Eagly & Chaiken, 1993; van Wee et al., 2019).

4.2. Treating emotions differently

Our systematic review highlights the powerful role of negative emotions, particularly fear (and related stress and anxiety), in both motivating and inhibiting behaviour change for commute mode use over the course of the pandemic. These negative emotions were linked to the perceived risk of infection and the potential life-threatening consequences associated with exposure to infected air and/or surfaces as well as proximity to other bodies (and in some cases fear of empty PT spaces). The corporal proximity during the COVID-19 pandemic raised serious concerns about the safety of PT as individuals are normally close to each other in such settings and older systems may have poor ventilation, facilitating the spread of infectious diseases (Fadaei, 2021). Additionally, the asymptomatic nature of the coronavirus added to the fear of proximity, as infected individuals can spread the virus unknowingly (Wang et al., 2020). It could take even some weeks before a PT passenger could ascertain whether there had been asymptomatic virus carriers around them. This prolonged exposure to uncertainty and worrisome situations has contributed to a decline in interpersonal trust termed “social scarring” (Fang et al., 2023), particularly in crowded public spaces like PT (Navarrete-Hernandez et al., 2023). Therefore, from the emergence of the COVID-19 onwards, proximity to other bodies that may sneeze, cough, or exhibit signs of illness has become a vital concern among some PT passengers.⁴ As such, these emotions potentially have longer-term consequences in the form of a stronger intention to obtain and use a private vehicle and a reluctance to (continue to) commute by PT.

We interpret this as a need for treating emotions in travel behaviour research in a manner that gives full and equal consideration to their full range, from positive to negative. A range of negative emotions has been considered in studies, including stress during the commute (e.g. Sannasi et al., 2022), fear of crime (e.g. Heinen, 2023; Truong & Currie, 2019) and gender-based violence (e.g. Infante-Vargas & Boyer, 2022). Regarding the fear of infection, one study in Taipei, Taiwan found that fear of infection caused a 50% decline in underground transit ridership during the peak of the epidemic (e.g. Wang, 2014). Nonetheless, it seems that positive emotions such as (mode-specific) travel liking (e.g. Morris & Guerra, 2015) and positive affects during travel and commute, have received greater attention. Many studies have linked positive affects to various factors associated with car use, including perceived reliability (Friman et al., 2017; Mokhtarian et al., 2004), a sense of control and freedom (Anable & Gatersleben, 2005), prestige, the driver’s ability to project positive qualities like mastery and skill onto themselves, and feelings of protection (ontological security) (Hiscock et al., 2002).

The need to treat emotions symmetrically extends to studies of commute mode use deploying the TPB and/or NAM.⁵ These frameworks often tend to over-rationalize behaviour, overlooking two important points regarding the significant role of emotions. First, the sense of protection offered by using a car or bicycle, fuelled by the heightened fears discussed earlier, can become a priority for commuters, surpassing factors such as travel time, comfort, and cost. This emphasis on protection may lead to decisions that may be perceived as “irrational” or avoidance of confronting the problem. Zhao and Gao (2022) also found that high levels of anxiety can damage individual’s peace of mind and lead them to an overestimate the consequences of their behaviour. These observations aligns with previous research suggesting that people’s inclination to seek positive emotions and avoid negative ones (Frijda, 2017), influences their behaviour. Individuals are generally found to avoid danger more than they seek safety, and this disproportion is stronger for safety than for finance (Tversky & Kahneman, 1991). As such, the desire to avoid a potentially risky situation on PT (due to infection or the absence of safety in numbers) was likely a stronger influence than financial costs due to using more expensive means of travel. Second, studies of commute mode use that utilise the TPB should explore the possibility of a direct influence of emotions on behavioural intentions, independent of attitudes. While traditionally emotions are considered to influence behavioural intentions through attitudes, the findings from the reviewed studies challenge this perspective. They indicate that emotions, particularly when they are intense and not yet long-lasting enough to shape attitudes, can directly and immediately drive intentions, regardless of individuals’ pre-existing attitudes or beliefs about a particular behaviour.

Furthermore, among the reviewed studies, some argued that repeated engagement in potentially risky behaviour (e.g. taking the commuter train) can lead to a decline in the feeling of fear, but not attitudes, over time, even in the persistence of the external sources of fear. This can occur through an internalisation process where, individuals must convince themselves that their actions (even those forced upon them) are reasonable and necessary, or simply that fear is reduced through repeated action without negative consequence. A decline in negative emotions can also occur through two other mechanisms. The first of these occurs when individuals possess sufficient awareness and perceived knowledge regarding the consequences of a given behaviour. Knowledgeable passengers are found to experience lower levels of fear, hence, they outweigh the benefits of using PT and the associated socio-economic benefits over a small possibility of catching coronavirus caused by using PT (Zhao & Gao, 2022). The second relates to the individuals’ perception of the psychological distance (Liberman et al., 2007) from the threatening event (McGraw et al., 2012). A study conducted in eight cities of China, found a positive association between PT passengers’ anxiety and being closer to the pandemic (Dong et al., 2021). Therefore, with the pandemic’s severity largely contained and an increased psychological distance, reduced fear and anxiety can positively influence the inclination of pandemic commuters to revert to using PT, a result that would be appreciated by PT practitioners.

Future research on commute mode use should not only treat emotions symmetrically. It should also recognise that models like the TPB – and other approaches that rely on people’s self-reporting of emotions, as in much research on subjective well-being (e.g. Ettema et al., 2011; Friman et al., 2017) – treat emotions as something of which people are consciously aware. Emotions and affect can, and have been, conceptualised in other, more embodied ways which involve automatic physiological responses and bodily sensations such as changes in heart rate, breathing, and muscle tension in response to external stimuli. For instance, studies have shown that embodied stress can operate below the threshold of consciousness, resulting in different manifestations compared to what individuals are cognitively aware of (Teixeira et al., 2020). The existing studies have mainly focused on examining the emotions that travellers experience during or after their trips and how these trips influence emotions. To further advance the field, future research could explore the reverse relationship, investigating how emotions impact modal use and other aspects of travel behaviour, such as travel frequency and time of departure. One approach could involve real-time phone surveys, where participants report their emotions before starting their daily travel. Additionally, integrating physiological measurements, such as skin conductivity and temperature, using wearable sensors worn by participants, could offer valuable perspectives. Given the integral role of both cognitive and embodied aspects in emotional experiences, a combination of these methods is recommended for assessing individual emotions in future studies.

4.3. The potential norm-changing consequences of the COVID-19 pandemic

According to the NAM, personal norms determine what individuals believe and how they behave through awareness of consequences and ascription of responsibility (Schwartz, 1977). According to Bouman et al. (2021), if it was not because of strong personal norms and the feelings of being morally responsible, no rapid and powerful prosocial actions were taken during the pandemic just like many other global environmental crises where public responses are less forceful. Building on NAM, we hypothesise that:

1. The perceived knowledge of the coronavirus’s ways of spreading, such as through saliva droplets and nasal discharge (WHO, 2020) and the ensuing health threats, likely heightened commuters’ awareness of the negative outcomes associated with choosing PT as a “risky” mode.

2. Consequently, this heightened awareness can trigger feelings of personal responsibility and/or fear regarding potential negative outcomes, such as the risk of contracting or transmitting the virus to others, particularly in close contact with immunocompromised individuals (variable of “proximity” in Figure 3).

3. These mechanisms can evoke or strengthen personal norms and guide decision-making regarding commute mode use, potentially leading to the avoidance of PT and a shift towards alternatives modes of transportation or WFH.

Figure 3. Theoretical model for understanding commute mode use, inspired by the TPB and NAM. The blue colours, including the arrows and the boxes, represent the variables that were emphasised in the literature of pandemic commuters.

The caring about hygiene and health of others – whether known or unknown – as a determinant of travel mode use reflects an altruistic and prosocial behaviour that was not pronounced prior to the emergence of the COVID-19 pandemic. If this trend continues, commuters (who have a choice) are likely to continue avoiding PT and instead opt for individual modes such as cars, bikes, or WFH as soon as they feel sick or display COVID/flu-like symptoms. This avoidance behaviour may also extend to caregivers when their children or care-recipients become ill, necessitating time off from work to reduce the risk of disease transmission (whereas before it might only have been acceptable if the child was very sick). These changes in social norms can potentially lead to policy adjustments, such as employers allowing keyworkers the option to WFH or providing caregiver-related vacation days. In that respect, the existing literature on commuting behaviour during COVID has primarily focused on individuals themselves rather than considering the wider network of people with whom the traveller interacts, shares space, and cares about protecting. Furthermore, it is worthwhile that future studies examine whether this altruistic behaviour will persist beyond the pandemic, given the possibility of forgetfulness and selfishness among some cultures/individuals.

The long-term effects of these changes and the social scarring mentioned earlier can have significant implications for commuting behaviour and addressing overcrowding on PT. They also have longer-term consequences for policy attempts aimed at promoting PT usage (Rothengatter et al., 2021). Rebuilding trust and instilling a sense of security in PT may require time and concerted efforts, considering that these changes vary among individuals and communities (Fang et al., 2023). With the shifting norms influenced by the pandemic and their likely continuation, it is advisable for future research to place more emphasis on filling this knowledge gap in our understanding of commute mode use. Understanding and addressing these social and psychological impacts is crucial for designing effective strategies and interventions to support individuals’ well-being and facilitate the recovery of PT usage.

4.4. Lessons learnt for how commute mode use is theorised

The above reflections on choice, (negative) emotions and norms prompted by corporeal proximity to other human beings reinforce previous statements about the importance of extending the TPB and NAM and their synthesis (Figure 3). By recognising that commuting is not always an outcome of making choices we can refine the TPB and NAM by acknowledging that assessing intentions based on attitudes, PBC, and social/personal norms is relevant only in the presence of a choice alternatives, encompassing the decision of whether to commute and which mode to select. It is therefore suggested that future research that deploy these models account for the varying levels of choice individuals have in their commute mode decisions. As Figure 3 illustrates, the absence of choice alternatives may lead to either the “use” of whatever alternative exists or fundamental changes in one’s commute or employment situation. Conversely, the availability of alternatives allows for the commonly understood decision-making process where (both positive and negative) emotions and experienced proximity become crucial factors in commute mode decisions.

5. Conclusion

This study has investigated the influences of the COVID-19 on pandemic commuters’ mode use. Through a systematic review of 36 scientific publications, the study has identified two significant findings related to the intertwined institutional, physical, and socio-psychological processes that influenced the adoption of different transport modes. Firstly, reduced PT services and concerns surrounding COVID-19 have led to significant shifts away from PT towards private car use and active travel among pandemic commuters. Secondly, PT avoidance is more likely pronounced among those with pre-existing car use habits and heightened negative emotions like fear triggered by the potential health risks resulting mainly from proximity to other bodies. Additionally, the study revealed a significant link between commute mode use challenges and low socio-economic status groups during the pandemic, underscoring the importance of addressing inequality in transportation access and resilience during crises.

Additionally, the study offered new insights into the traditional understanding of the TPB and NAM. Firstly, it questioned the assumption of a standard set of travel mode alternatives for commuters, shedding light on the complexities of mode “choice” versus “use”. Secondly, it emphasised the significant role of (negative) emotions, triggered by physical proximity to others, in commuting mode use, regardless of attitudes. It underscored the importance of treating emotions differently, considering the full range from positive to negative in travel behaviour research, and paying greater attention to embodied affect exerting influences below the threshold of consciousness. Lastly, the study highlighted the potential impact of the COVID-19 pandemic on transforming mode-use-related norms and the necessity for future research to explore its long-term policy implications. Thus, employing more rigorous longitudinal research methods to study post-pandemic waves is highly recommended.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Fonds de recherche du Québec – Société et culture (FRQSC).

Notes

1 To ensure consistency, we used U.S. spelling throughout the manuscript; however, we included both U.K. and U.S. spelling variations in our search queries.

2 Some studies reported a decline in the use of all modes during the pandemic because of telecommuting.

3 Among the reviewed articles, only two studies applied a qualitative approach to report stories from keyworkers regarding their commute-related concerns (Plyushteva, 2022; Jamal et al., 2022). Participants discussed both negative and positive financial implications related to commuting during the pandemic. While some participants found (increased) commute cost as unjust when many others had the option to WFH, some emphasized that even working from home only half of the time resulted in a substantial difference in the household budget.

4 Such conditions may also exist in other locations such as cafés, but the difference is that PT is often an essential service, whereas being in a café is typically a matter of personal choice. A real question is whether it is better to continue masking in PT to respond to worries, or whether this practice would create an impression that PT is a risky choice.

5 Within these frameworks, emotions are considered to affect behavior indirectly through attitudes which are shaped by three clusters of processes: i.e., affective (emotions experienced during an activity like a commute trip), cognitive (referring to an evaluation of the trip), and behavioral processes (referring to the actual performance of a (travel) behavior). Both behavioral and cognitive processes can impact affective processes, while affective and cognitive processes can also impact behaviors.

Appendix

Search query for Web of Science, PubMed and TRID. Equivalent queries have been used for Google scholar and Scopus.

TS = (“Travel mode” OR “travel modes” OR “travel habit” OR “travel habits” OR “travelling habit” OR “mobility” OR “mobilities” OR “travel behavior” OR “travel behaviour” OR “travel behaviours” OR “travel behaviors” OR “travelling behavior” OR “travelling behaviour” OR “transport mode” OR “transport modes” OR “transportation mode” OR “transportation modes” OR “means of transport” OR “transport means” OR “transportation means” OR “transport use” OR “transportation use” OR “metro use” OR “bus use” OR “subway use” OR “train use” OR “transit use” OR “railway use” OR “car use” OR “mass transit” OR “mass-transit” OR “active travel” OR “active transportation” OR “active transport” OR “car sharing” OR “car share” OR “carshar*” OR “car-shar*” OR “bike sharing” OR “bike share” OR “bikeshare” OR “bike-sharing” OR “bike-share” OR “biking” OR “cycling” OR “bicycl*” OR “ride-hailing” OR “ride hailing” OR “ridesharing” OR “ride share” OR “liftshare” OR “lift-shar*” OR “lift shar*” OR “public transport*” OR “public transit” OR carpool* OR car-pool* OR “daily travel” OR “daily transport” OR “daily transportation” OR “work-based travel” OR “work-based travels” OR “work-based trip” OR “work-based trips” OR “travel to work” OR “travels to work” OR “travel-to-work” OR “travelling to work” OR “daily trip” OR “trip to work” OR “trips to work” OR “trip-to-work” OR “mode choice” OR “modal choice” OR commut* OR “work travels” OR “work travel” OR “work trip” OR “work trips” OR “working trip” OR “working trips” OR “working travel” OR “working travels” OR “essential travel” OR “essential travels” OR “essential trip” OR “essential trips” OR “pandemic travel*” OR “pandemic trip*” OR “modal split” OR “mode use*” OR “modal use*” OR “used bus” OR “used metro” OR “used train” OR “used railway” OR “used subway” OR ridership* OR “work journey” OR “journey-to-work” OR “journey to work” OR “journeys to work” OR “working journeys”)

AND TS = (covid OR covid19 OR covid-19 OR “COVID 19” OR pandemic OR coronavirus OR corona OR SARS-CoV-2)

AND TS = (“worker*” OR “staff*” OR “employee*” OR “personnel” OR “workforce” OR “work force” OR “labour force” OR “labor force” OR “essential service*” OR “essential job*” OR “essential occupation*” OR “essential profession*” OR “essential work*” OR “essential profession*” OR nurse* OR doctor* OR educator* OR pharmacist* OR “essential care providers” OR “health-care providers” OR “health-care providers” OR “essential care practitioners” OR “healthcare practitioners” OR “health-care practitioners”)

TS = Topic (includes Title, Abstract, and Keywords)