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Tonmoy Paul, Rohit Chakraborty, Nafis Anwari, Impact of COVID-19 on daily travel behaviour: a literature review, Transportation Safety and Environment, Volume 4, Issue 2, June 2022, tdac013, https://doi.org/10.1093/tse/tdac013
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Abstract
The coronavirus disease 2019 (COVID-19) pandemic made a perceptible impact on daily travel behaviour worldwide, especially through mode shifts and changes in trip frequencies with possible long-term repercussions. Non-therapeutic interventions adopted worldwide (e.g. lockdowns and travel restrictions) to reduce viral contagion need to be understood holistically because it is challenging for people to follow through these policies and stay home in developing nations. In this context, it is important to have a clear idea of how COVID-19 is shaping the mobility pattern and what policies must be taken (if not yet) to minimize viral transmission as well as develop a sustainable transportation system. To this end, this study presents a systematically analysed review of 56 international literatures from academic sources (Google Scholar, Scopus and Web of Science) on the impacts of COVID-19 on travel behaviour and focuses on policymaking measures. This article illustrates the modal shift, variation in frequencies of different trips and how sociodemographic characteristics have influenced the mobility pattern in response to COVID-19. Innate changes in travel patterns compared to the pre-COVID-19 era were observed. A noticeable apprehension on viral transmission in public transit has reduced public transit usage while increasing that of private vehicles. This poses challenges to develop sustainable transportation. This study concludes by discussing intervention measures to support transportation planners and policymakers to deal with the current pandemic as well as any future pandemics.
1. Introduction
The coronavirus disease 2019 (COVID-19) pandemic has been declared a major global public health issue by the World Health Organization (WHO) [1]. The outbreak not only affects human health but also disrupts the economy, social activities, mobility styles and habitual travel behaviours all over the world, aggravating people's living standards [2]. To mitigate the virus transmission, a range of strategies involving lockdowns, restrictions on out-of-home activities and safety guidelines of maintaining physical distances have been implemented in many countries [3]. Although it is still uncertain whether the implemented strategies are ideal for both developed and developing countries, they have caused a clear declination in global air pollution [4], bringing back blue skies and cleaner air.
Because most people live and work in cities, they are vulnerable to various natural and man-made disasters [5]. Hence, to minimize the impacts and ensure safe and sustainable transportation simultaneously across the planet, it is necessary to understand clearly how travel patterns are influenced by the pandemic and what actions are required to minimize the negative impacts. Nevertheless, the advancements in the transportation sector in recent years have made travel a major factor in rapid disease transmission worldwide [6]. Consequently, an overwhelming number of studies on COVID-19 and transportation have already appeared in the scientific literature after the outbreak. It is to be noted that similar public health crises have occurred earlier including the Ebola outbreak, the H1N1 influenza virus, the Zika virus and SARS. In response, numerous studies have been carried out to investigate travel pattern changes caused by the outbreaks [7–10]. However, the effect of these outbreaks was limited to particular geographic areas, whereas the present COVID-19 pandemic has affected the whole world [11]. Hence, a comprehensive literature review is highly essential to delineate the existing knowledge and findings of the impact of COVID-19 on travel behaviour and mobility style all around the world and highlight the gaps that need to be further studied. This study has attempted this feat through a holistic review of previous literature, whose process is outlined in Fig. 1. According to Fig. 1, this review article mainly aims to illustrate the global findings on daily transportation and hence has not included long-distance discretionary travel using modes such as ships, planes, etc. In addition, this study includes discussion on various local and national policies that can guide transport planners and policy makers in decision making. This can ensure a safe and sustainable trip worldwide while accomplishing greater community goals under such crucial circumstances.

2. Materials and method
This study has used academic literature relevant to COVID-19 and daily travel behaviour, obtained from three broad and the most appreciated scholarly databases: Web of Science, Google Scholar and Scopus. Initial search results using keywords ‘COVID-19' and ‘transport’ revealed approximately 8000 studies from January 2020 to May 2021 on the COVID-19 topic. However, many of these are focused on disciplines such as pharmaceutical sciences, nursing, medicine and issues related to patient transportation safety and healthcare professionals’ occupational safety. Inclusion criteria applied to finalize the relevant articles comprised of the following:
1. Source: The relevant articles were obtained from three highly credible scholarly databases.
2. Geographic location of the studies: In this review, the aim is to have a clear idea of how COVID-19 has influenced daily travel behaviour across the world. Hence studies from both developed and developing countries were considered.
3. Publication type: The attention was on peer-reviewed articles only. In most of the Literature Review Papers (LRPs), a minimum of 30 papers and a maximum of 100 papers are usually cited in the field of transport [12].
After filtering, screening and checking the abstracts of the articles to ensure their relevance, 32 English-written research papers published in peer-reviewed international journals were selected in our first submission. After an update in August 2021, in total 51 academic papers were considered relevant. In the third phase, 55 articles are selected in October 2021. Finally, updating in December 2021, 56 research articles were selected to be reviewed in detail. However, most of the papers selected were found in all three databases and the duplicated papers were removed. From all of the phases, a total of 94 papers were considered, of which 38 duplicate papers were removed and finally 56 papers were selected to be reviewed in detail. The dominance of the themes of the selected papers can be observed from the Keyword Co-occurrence Network (KCN) in Fig. 2 obtained from VOSviewer. This is a software tool for text mining and constructing and visualizing bibliometric networks. The text mining function of the software is used to extract keywords from titles, abstracts and citation contexts, which creates a co-occurrence network and displays it in a two-dimensional map [13]. The gap between two keywords is approximately inversely proportional to the relatedness between them, where relatedness is measured in terms of co-occurrence of keywords. The keywords are presented in clusters and are assigned to clusters based on their co-occurrence. Also, the area covered by each cluster is proportional to the amount of co-occurrence of the keywords under that cluster [14–16]. The KCN generated in this article contains 19 clusters. Fig. 2 reveals that ‘covid-19' is the most dominant key term, followed by ‘mobility patterns’, ‘travel behaviour’, ‘mobility’. The keywords closer to one another mean a higher rate of co-occurrence. So, keywords like ‘epidemic control’, ‘behavioural change’ and ‘expert survey’ are separated by large distances because of their lower rate of co-occurrence in keywords from our selected articles.

A wide range of issues were meticulously tabulated and analysed with the help of Microsoft Excel. The tabulation results are introduced in Table 1, which depicts the location, medium of data collection and the response count of the selected papers. Out of the 56 papers, 17 are within 2020 while the remaining are from 2021. Because the world continues to struggle with the COVID-19 crisis, research is continuously being done worldwide to improve understanding of the COVID-19 pandemic and mitigation of virus transmission to ensure safe transport of people. This literature review comprises an ever-growing body of studies carried out in both the developed countries and developing countries to understand the nature of the COVID-19 pandemic and explore solutions and policies to mitigate the devastating effects of this virus and ensure transport safety for the public. Among the reviewed papers, the majority of studies are from China and India, followed by Germany, the USA and Bangladesh, while few papers have used data from several countries worldwide. However, a global survey can pose limitations because of geographical variations [17]. Almost all of the studied literature has extracted data using the web-based questionnaire survey and through social media. This is because a web-based survey can reach a large number of people within a short period [18] and would ensure comfortable survey participation during the pandemic. However, the online survey system has drawbacks, such as lack of privacy, difficulties in survey design and inadequate participants [19]. König and Dreßler [20] conducted both household surveys and telephone interviews while Beck and Hensher [21, 22] performed both online and field surveys for two separate studies. Exceptions to this practice include Aloi et al. [23] and Jenelius and Cebecauer [24], who collected data through traffic counters and transport authorities. Other than [25–27], all of the survey-related studies collected more than 300 responses.
Place . | Prime trips during COVID-19 . | Online service . | Reference . |
---|---|---|---|
Lahore, Faisalabad and Rawalpindi, Pakistan | Work | – | [41] |
China (in cities) | Commute | – | [42] |
Thessaloniki, Greece | HBO (shopping, leisure, etc.) trips | – | [36] |
Punjab, Khyber Pakhtunkhwa, Balochistan, Sindh, Gilgit Baltistan and Islamabad, Pakistan | Trips for buying necessities and to hospital | – | [43] |
Greece (nationwide) | Commuting trips along with trips for workout/pet walking purposes | Increment of teleworking was mentioned | [40] |
Germany (nationwide) | – | Video conferencing and phone calls | [44] |
Spain (nationwide) | Work | – | [45] |
Switzerland (nationwide) | Shopping (bicycle) | – | [39] |
Sweden (in cities) | Negligible change for same studied group | Telework | [46] |
Different countries (Italy, Sweden, and India) | – | Work (Sweden, Italy, and others), entertainment (India) | [47] |
Worldwide | – | Commute and services (banking, ticket purchasing, etc.) will increase according to experts' opinion | [34] |
Bangladesh (nationwide) | Work | Shopping | [11] |
Different Countries | Shopping | – | [17] |
Tokyo and Northern Kanto, Japan | Shopping | – | [38] |
Various cities in India | Discretionary | Commute | [2] |
Australia (nationwide) | Shopping | Commute | [22] |
Australia (nationwide) | Social and recreational | Commute | [21] |
Chicago, USA | Commute | Shopping | [48] |
Netherlands (nationwide) | Grocery shopping | Grocery shopping, commute | [49] |
Istanbul, Turkey | Work | Shopping and commute | [27] |
Place . | Prime trips during COVID-19 . | Online service . | Reference . |
---|---|---|---|
Lahore, Faisalabad and Rawalpindi, Pakistan | Work | – | [41] |
China (in cities) | Commute | – | [42] |
Thessaloniki, Greece | HBO (shopping, leisure, etc.) trips | – | [36] |
Punjab, Khyber Pakhtunkhwa, Balochistan, Sindh, Gilgit Baltistan and Islamabad, Pakistan | Trips for buying necessities and to hospital | – | [43] |
Greece (nationwide) | Commuting trips along with trips for workout/pet walking purposes | Increment of teleworking was mentioned | [40] |
Germany (nationwide) | – | Video conferencing and phone calls | [44] |
Spain (nationwide) | Work | – | [45] |
Switzerland (nationwide) | Shopping (bicycle) | – | [39] |
Sweden (in cities) | Negligible change for same studied group | Telework | [46] |
Different countries (Italy, Sweden, and India) | – | Work (Sweden, Italy, and others), entertainment (India) | [47] |
Worldwide | – | Commute and services (banking, ticket purchasing, etc.) will increase according to experts' opinion | [34] |
Bangladesh (nationwide) | Work | Shopping | [11] |
Different Countries | Shopping | – | [17] |
Tokyo and Northern Kanto, Japan | Shopping | – | [38] |
Various cities in India | Discretionary | Commute | [2] |
Australia (nationwide) | Shopping | Commute | [22] |
Australia (nationwide) | Social and recreational | Commute | [21] |
Chicago, USA | Commute | Shopping | [48] |
Netherlands (nationwide) | Grocery shopping | Grocery shopping, commute | [49] |
Istanbul, Turkey | Work | Shopping and commute | [27] |
Place . | Prime trips during COVID-19 . | Online service . | Reference . |
---|---|---|---|
Lahore, Faisalabad and Rawalpindi, Pakistan | Work | – | [41] |
China (in cities) | Commute | – | [42] |
Thessaloniki, Greece | HBO (shopping, leisure, etc.) trips | – | [36] |
Punjab, Khyber Pakhtunkhwa, Balochistan, Sindh, Gilgit Baltistan and Islamabad, Pakistan | Trips for buying necessities and to hospital | – | [43] |
Greece (nationwide) | Commuting trips along with trips for workout/pet walking purposes | Increment of teleworking was mentioned | [40] |
Germany (nationwide) | – | Video conferencing and phone calls | [44] |
Spain (nationwide) | Work | – | [45] |
Switzerland (nationwide) | Shopping (bicycle) | – | [39] |
Sweden (in cities) | Negligible change for same studied group | Telework | [46] |
Different countries (Italy, Sweden, and India) | – | Work (Sweden, Italy, and others), entertainment (India) | [47] |
Worldwide | – | Commute and services (banking, ticket purchasing, etc.) will increase according to experts' opinion | [34] |
Bangladesh (nationwide) | Work | Shopping | [11] |
Different Countries | Shopping | – | [17] |
Tokyo and Northern Kanto, Japan | Shopping | – | [38] |
Various cities in India | Discretionary | Commute | [2] |
Australia (nationwide) | Shopping | Commute | [22] |
Australia (nationwide) | Social and recreational | Commute | [21] |
Chicago, USA | Commute | Shopping | [48] |
Netherlands (nationwide) | Grocery shopping | Grocery shopping, commute | [49] |
Istanbul, Turkey | Work | Shopping and commute | [27] |
Place . | Prime trips during COVID-19 . | Online service . | Reference . |
---|---|---|---|
Lahore, Faisalabad and Rawalpindi, Pakistan | Work | – | [41] |
China (in cities) | Commute | – | [42] |
Thessaloniki, Greece | HBO (shopping, leisure, etc.) trips | – | [36] |
Punjab, Khyber Pakhtunkhwa, Balochistan, Sindh, Gilgit Baltistan and Islamabad, Pakistan | Trips for buying necessities and to hospital | – | [43] |
Greece (nationwide) | Commuting trips along with trips for workout/pet walking purposes | Increment of teleworking was mentioned | [40] |
Germany (nationwide) | – | Video conferencing and phone calls | [44] |
Spain (nationwide) | Work | – | [45] |
Switzerland (nationwide) | Shopping (bicycle) | – | [39] |
Sweden (in cities) | Negligible change for same studied group | Telework | [46] |
Different countries (Italy, Sweden, and India) | – | Work (Sweden, Italy, and others), entertainment (India) | [47] |
Worldwide | – | Commute and services (banking, ticket purchasing, etc.) will increase according to experts' opinion | [34] |
Bangladesh (nationwide) | Work | Shopping | [11] |
Different Countries | Shopping | – | [17] |
Tokyo and Northern Kanto, Japan | Shopping | – | [38] |
Various cities in India | Discretionary | Commute | [2] |
Australia (nationwide) | Shopping | Commute | [22] |
Australia (nationwide) | Social and recreational | Commute | [21] |
Chicago, USA | Commute | Shopping | [48] |
Netherlands (nationwide) | Grocery shopping | Grocery shopping, commute | [49] |
Istanbul, Turkey | Work | Shopping and commute | [27] |
Some of the studies reviewed in this article have some limitations. For example, the data obtained in some studies are for a short period and do not create a deeper pool of data sets [28]. Moreover, some studies collected data in only one weather. Weather or climate plays a vital role in activity pattern change, particularly activity pattern change is more noticeable in warmer conditions [22]. It should be considered that not all studies measured all potentially relevant variables that could have controlled the travel behaviour change [29]. While most studies concentrated in single country may not represent the overall travel pattern change across the world, others that collected data through a global survey have high scatter and variation in data due to geographical location [17]. The socioeconomic conditions are diverse for different countries [3] and different countries had different levels of restrictions and different percentages of the infected population [17]. TableA1 displays the location, medium of data collection and response count of studied literature. This paper has segregated its review into four sections, namely, (i) mode choice, (ii) trip purpose, (iii) sociodemographic characteristics and (iv) policy implications, which are discussed in the subsequent sections.
3. Impact on mode choice
The present pandemic has created striking changes in travel mode choice around the world. Some of the changes are in response to restrictive measures imposed by the governments that involve complete and partial lockdowns, whereas others are driven because of safety concerns and/or by the obligation to slow down the spread of the virus for the betterment of society. Table A2 presents in detail the modal usage changes and preferences during the COVID-19 pandemic. The summarized results from Table A2 are presented in Figs. 3 and 4, respectively, where Fig. 3 depicts the comparison of the most and the least preferred modes during the pandemic. On the other hand, Fig. 4 elucidates the largest decrease and largest increase in the mode usage due to COVID-19 considered in the studies reviewed. It can be seen that, in almost all regions, public transport (usually bus, train, bus rapid transit (BRT), minibus) has expectedly been the least preferred mode to travel. The preferences for ride-sharing vehicles and ride-hailing services have decreased as well. This is because although the ride-hailing services may reduce the number of passenger contacts, smaller confined spaces and shorter social distances in cars may increase the chance of virus transmission (especially for drivers) [30]. A few studies have assessed the mode choice behaviour during the COVID-19 pandemic by performing statistical tests and developing models including exploratory factor analysis, multinomial logistic regression [17] and multiple discrete choice extreme value (MDCEV) models [2]. Mode choice has varied even during the COVID-19 period depending on the difference in soci-demographics [17], vehicle ownership [30], the status of employment [26] and purpose of the trip [2]. Apart from these factors, travel time saving, comfort and cost also affect mode choice, but these variables have less priority during the pandemic [17].


Numerous studies have demonstrated a shift in respondent's preference from public transport to other modes during the pandemic [11, 20]. Some studies have investigated mode shift using inertia analysis [2, 11, 31]. Among the most preferred/usage modes during the pandemic period, private cars have become the most preferred mode in many parts of the world including developing nations like Bangladesh and India [32, 33]. For example, Zhang et al. [34] found a modal shift of 64.8% on average from public transport to private cars. This trend can be attributed to people's perception of reduced physical contact and infection chances when travelling via cars [25]. However, the benefits of sustainable transportation cannot be replaced by private vehicles. Moreover, high car usage can worsen traffic congestion [30]. Although active transportation mode (walking and cycling) has been observed along with private transportations, the actual percentage of bicycle usage is very low. Eisenmann et al. [35] observed an insignificant increase from 6% to 9% for the mono-modal group, while Politis et al. [36] noticed a disparaging rise in bicycle usage from 1.09% to 2.01%. Hence, the usage of these modes should be encouraged more [34] as walking and cycling aid people in maintaining higher well-being levels [25]. In addition, personal vehicle (car and motorcycle) owners are less likely to choose rickshaw (non-motorized transport, NMT) as the mode of choice for shopping trips in Bangladesh [33]. So, for people not owning personal vehicles during COVID-19, a rickshaw can be a suitable mode of transport as it is pollution-free and cost-effective for short-distance movement. For instance, Anwari et al. [11] observed a decent usage of NMT in their study. Some of the studies have investigated mode shifts to online/virtual medium [2, 33], caused by an increase in work-from-home facilities and the ease of access as well as the prevalence of online shopping. However, it is possible to get infected via delivery people [2]. On the other hand, in Bangladesh, remote shopping is not that significant even during the pandemic situation because developing countries like Bangladesh have a weak e-commerce framework [33].
4. Impact on trip purposes
The most fundamental and dominating factor to generate a trip is its purpose. People make trips because they have activities to perform outside the home, and the types of activities determine the frequency of trip demand [37]. In general, people choose to travel either for commute purposes or for different discretionary purposes [2]. Based on the objectives of the studies, different authors categorized trips into different purposes. For example, Khaddar and Fatmi [25] analysed shopping trips, recreational activity trips and trips for buying household errand. Parady et al. [38] considered shopping and leisure trips for their study. During such a crisis, people may be able to reduce less important trips, although they are bound to travel for essential purposes. Table 1 represents the key purposes for which people all around the world have to generate trips even during the COVID-19 pandemic and for which people use the virtual medium as an alternate to make trips.
Shopping trips have been observed to be among the most highly participated ones during the pandemic. Shopping trips can be considered as trips for buying essential grocery items or as non-mandatory luxurious shopping [38]. Parady et al. [38] found contrasting results in their study because both essential trips (shopping) and non-essential shopping trips (dining outside) remained significant during the pandemic. Trips for commute purposes refer to travel between one's place of residence and place of work or study [3]. However, trips for some particular purposes have remained high using only a specific mode of transport. For example, Bhaduri et al. [2] found high discretionary trips using personal vehicles, and Molloy et al. [39] observed high shopping trips using bicycles only. In a few studies, although the trip reduction is the highest for particular purposes, the frequency remains large. For instance, Politis et al. [36] found the largest reduction in trips to be for home-based others (HBO) trips (shopping, leisure, etc.), although the rate of trips remained higher than home-based work (HBW) and non-home based (NHB) trips. Similarly, Politis et al. [40] observed the highest decrease in commute trips, yet a substantial percentage of people have continued to make these trips during the pandemic. On the other hand, Anke et al. [44] revealed that certain trip purposes have remained unchanged during the pandemic situation, while video conference usage was noticeable for work purposes. Similarly, Hiselius and Arnfalk [46] noticed a negligible change in work trips for the studied group, although the sample size of respondents seemed inadequate. Additionally, work trips remained as a prime trip purpose during the pandemic in the East Asian countries. In addition, Istanbul, a European city, has shown similar characteristics.
Online facility usage has varied from country to country. For instance, Italians and Swedish people heavily used the virtual medium for work purposes, while Indians used it for entertainment purposes [47]. Low work trip frequencies during the pandemic in several studies [17, 22] can be attributed to job losses and an increase in work-from-home facilities. However, work at home has both benefits and limitations. Based on the study by Shamshiripour et al. [48], high productivity has been attributed to reduced/eliminated commuting times and a casual home environment. On the other hand, low productivity has been attributed to frequent distractions at home and a lack of comfortable workspace [48]. Hence, the home environment can either improve or deteriorate the work experience, depending on how well people have adjusted to the new normal. There has also been an increase in the time spent on the internet for social interactions, news and entertainment [50].
5. Sociodemographic characteristics
Sociodemographic characteristics play a vital role in travelling. While the exact effect on trips during the pandemic can vary from country to country, a few trends can be observed because of very limited choices [2]. Table 2 portrays the demographic groups who are at high risk of infection, caused by their high travel frequencies during the COVID-19 pandemic. Studies from Greece and Canada reveal that males and commuters are at high risk, respectively [25, 36]. The risk is higher if the trip is made using shared vehicles or public transport, where a large group of unknown people travel together and cannot maintain physical distance measures. Based on both the studies from Germany, public transport users are at high risk of infection. Eisenmann et al. [35] mentioned that more than 60% of respondents feel uncomfortable travelling on public transport despite being forced to ride it. Although a decent percentage of people worked from home during the pandemic [2], a large portion still had to make trips to their workplaces. This might be due to the nature of the job or because their work does not support work-from-home practices [22]. Several researchers found increased daily trip preference among low-income holders because of their manual labour work, which increased their risk of virus contamination [21, 45, 50]. Moreover, Molloy et al. [39] reported that 20% of participants worked for short hours, despite trivially influencing daily trips. Healthcare personnel and people working in retail stores commuted daily during the pandemic [48].
Year . | Country . | People having high infection risk . | Reference . |
---|---|---|---|
2021 | Pakistan | 1. Males, who have more non-commuting trips. 2. 30+ year-old people doing frequent non-commuting trips | [41] |
2021 | India | Students still using public transport. | [51] |
2020 | Males. | [2] | |
2021 | Greece | 1. Low-income holders. 2. Males. | [36] |
2021 | 1. Males 2. 41–64-year age group | [40] | |
2021 | USA | Less‐educated and lower‐income individuals | [52] |
2020 | Daily commuters. | [48] | |
2021 | Germany | 1. Public transport riding adults 2. Urban public transport riders | [35] |
2021 | Bus/tram riders. | [44] | |
2021 | Canada | Younger or older individuals, who are most likely to be working out of the home. | [25] |
2021 | 1. Both low-income and high-income workers who make frequent trips. 2. Residents of Halton and York cities, who made more commuting trips | [50] | |
2021 | Spain | Workers making frequent trips. | [45] |
2021 | Switzerland | Short-period workers. | [39] |
2021 | Bangladesh | 1. Males 2. Frequent travellers aged 51–60 | [11] |
2020 | Chile | 1. Workers from the low-income group, who have to go out for work. 2. People older than 46 years old, who make higher trips | [53] |
2020 | Different Countries | 1. Commuters. 2. People who are willing to take risks for shopping trips. 3. Essential workers, whose trip purpose has not changed. | [17] |
2020 | Japan | Shoppers. | [38] |
2020 | Australia | 1. Younger households who are still planning to make more trips. 2. Low-income groups who are more likely to work in retail environments, indoor spaces with small teams. | [22] |
Year . | Country . | People having high infection risk . | Reference . |
---|---|---|---|
2021 | Pakistan | 1. Males, who have more non-commuting trips. 2. 30+ year-old people doing frequent non-commuting trips | [41] |
2021 | India | Students still using public transport. | [51] |
2020 | Males. | [2] | |
2021 | Greece | 1. Low-income holders. 2. Males. | [36] |
2021 | 1. Males 2. 41–64-year age group | [40] | |
2021 | USA | Less‐educated and lower‐income individuals | [52] |
2020 | Daily commuters. | [48] | |
2021 | Germany | 1. Public transport riding adults 2. Urban public transport riders | [35] |
2021 | Bus/tram riders. | [44] | |
2021 | Canada | Younger or older individuals, who are most likely to be working out of the home. | [25] |
2021 | 1. Both low-income and high-income workers who make frequent trips. 2. Residents of Halton and York cities, who made more commuting trips | [50] | |
2021 | Spain | Workers making frequent trips. | [45] |
2021 | Switzerland | Short-period workers. | [39] |
2021 | Bangladesh | 1. Males 2. Frequent travellers aged 51–60 | [11] |
2020 | Chile | 1. Workers from the low-income group, who have to go out for work. 2. People older than 46 years old, who make higher trips | [53] |
2020 | Different Countries | 1. Commuters. 2. People who are willing to take risks for shopping trips. 3. Essential workers, whose trip purpose has not changed. | [17] |
2020 | Japan | Shoppers. | [38] |
2020 | Australia | 1. Younger households who are still planning to make more trips. 2. Low-income groups who are more likely to work in retail environments, indoor spaces with small teams. | [22] |
Year . | Country . | People having high infection risk . | Reference . |
---|---|---|---|
2021 | Pakistan | 1. Males, who have more non-commuting trips. 2. 30+ year-old people doing frequent non-commuting trips | [41] |
2021 | India | Students still using public transport. | [51] |
2020 | Males. | [2] | |
2021 | Greece | 1. Low-income holders. 2. Males. | [36] |
2021 | 1. Males 2. 41–64-year age group | [40] | |
2021 | USA | Less‐educated and lower‐income individuals | [52] |
2020 | Daily commuters. | [48] | |
2021 | Germany | 1. Public transport riding adults 2. Urban public transport riders | [35] |
2021 | Bus/tram riders. | [44] | |
2021 | Canada | Younger or older individuals, who are most likely to be working out of the home. | [25] |
2021 | 1. Both low-income and high-income workers who make frequent trips. 2. Residents of Halton and York cities, who made more commuting trips | [50] | |
2021 | Spain | Workers making frequent trips. | [45] |
2021 | Switzerland | Short-period workers. | [39] |
2021 | Bangladesh | 1. Males 2. Frequent travellers aged 51–60 | [11] |
2020 | Chile | 1. Workers from the low-income group, who have to go out for work. 2. People older than 46 years old, who make higher trips | [53] |
2020 | Different Countries | 1. Commuters. 2. People who are willing to take risks for shopping trips. 3. Essential workers, whose trip purpose has not changed. | [17] |
2020 | Japan | Shoppers. | [38] |
2020 | Australia | 1. Younger households who are still planning to make more trips. 2. Low-income groups who are more likely to work in retail environments, indoor spaces with small teams. | [22] |
Year . | Country . | People having high infection risk . | Reference . |
---|---|---|---|
2021 | Pakistan | 1. Males, who have more non-commuting trips. 2. 30+ year-old people doing frequent non-commuting trips | [41] |
2021 | India | Students still using public transport. | [51] |
2020 | Males. | [2] | |
2021 | Greece | 1. Low-income holders. 2. Males. | [36] |
2021 | 1. Males 2. 41–64-year age group | [40] | |
2021 | USA | Less‐educated and lower‐income individuals | [52] |
2020 | Daily commuters. | [48] | |
2021 | Germany | 1. Public transport riding adults 2. Urban public transport riders | [35] |
2021 | Bus/tram riders. | [44] | |
2021 | Canada | Younger or older individuals, who are most likely to be working out of the home. | [25] |
2021 | 1. Both low-income and high-income workers who make frequent trips. 2. Residents of Halton and York cities, who made more commuting trips | [50] | |
2021 | Spain | Workers making frequent trips. | [45] |
2021 | Switzerland | Short-period workers. | [39] |
2021 | Bangladesh | 1. Males 2. Frequent travellers aged 51–60 | [11] |
2020 | Chile | 1. Workers from the low-income group, who have to go out for work. 2. People older than 46 years old, who make higher trips | [53] |
2020 | Different Countries | 1. Commuters. 2. People who are willing to take risks for shopping trips. 3. Essential workers, whose trip purpose has not changed. | [17] |
2020 | Japan | Shoppers. | [38] |
2020 | Australia | 1. Younger households who are still planning to make more trips. 2. Low-income groups who are more likely to work in retail environments, indoor spaces with small teams. | [22] |
Various studies show that males are making comparatively more daily trips than females are and are therefore at more risk of virus contagion [2,11,22]. Consequently, males have higher infection and death rates [54]. Interestingly, Khaddar and Fatmi [25] noticed that individuals living in a high-income household are more likely to be working out of the home, whereas middle-aged people from low-income households are not working outside of the home. On the other hand, Beck and Hensher [21] observed that younger respondents are more prone to having commuting trips, trips for education and childcare, food shopping and general shopping than middle-aged and older respondents are prone to. Hence, the younger respondents are at greater risk of infection than the middle-aged and older respondents. On the other hand, Anwari et al. [11] observed that 51–60-year-olds are frequent travellers. People from low-income groups in several studies are not able to access work-from-home facilities, open spaces to maintain a safe distance in the workplace, or are unable to use private cars to make trips [21]. As a result, low-income people face greater risks of virus contagion than higher-income groups do.
6. Policy implications
The COVID-19 pandemic has ushered in unprecedented challenges worldwide. Mobility patterns have changed unconventionally in response to reduce virus transmission and mitigate the associated ramifications of the pandemic. While some habits may be reverted, other changes can transcend into a new regular way of living. The question is ‘Is the world ready to adapt to the new normal and its behavioural habit in the post-pandemic situation?’ To dispel the doubt, priority must be given to appropriate transport planning and policy measures that reflect sustainable mobility objectives and accomplish the greater community goals under such crucial circumstances [3].
In response to the pandemic, social distancing measures were taken by imposing lockdowns in many parts of the world including China, Italy, Spain and Bangladesh, while less-rigid social distancing measures were taken in other countries like the Netherlands, Sweden and Japan. However, the result of lockdowns and restrictions on movement may be ineffective in countries with high population density, poor transportation infrastructure and a large informal economy [55]. Public transport is a major transport mode, accommodating many captive users. As public transport has been associated with increased risk of viral transmission, the services have been reduced and users are facing constraints to use them as a travel mode. As a result, out-of-home activities and manual labour work become inaccessible to low-income people who do not own cars. Work from home cannot act as an alternative to blue-collar jobs. Thus, transport planners and operators should prioritize safe transportation of workers and craftsmen during this pandemic through dynamic planning. In response to reduction of public transport services and reduced trust in its usage, affluent people are shifting to private vehicles. Nevertheless, a private vehicle is not a sustainable solution to transportation as it creates greater traffic congestion and occupies a larger portion of the roadway for transporting the same number of people than public transport does [3]. Hence, policymakers need to be cautious about the likely decline in usage of public transport and emphasize the improvements of public transport strongly by upgrading both the safety and level of service of buses, trams and transits. As an immediate response, the city of Manila, Philippines introduced a separated lane for Bus Rapid Transit without imposing additional fares to attract more commuters and maintain high service frequencies [56]. The internal design of buses can be modified and rearranged while using dividers to facilitate physical distancing among passengers as they board and descend from them [2]. Contactless payment like mobile financial services (MFS) can be implemented to prevent virus transmission through banknote payments in buses and trains. It is quite challenging to rebuild trust in public transport as many governments have used the media to request people to avoid using public transport [11]. However, if the right measures are taken, public transport can be COVID-safe [57]. According to a study by the University of Colorado Boulder, the risk of being infected in a well-ventilated metro with minimal talking and movement is 0% after 70 minutes. Moreover, the result is even lower for a bus ride [58]. Advancing these scientific studies can restore trust in public transport services. In the case of ride-hailing services, provision of face masks and facilities for sanitization of both drivers and passengers may help to resolve user concerns [3, 11, 17]. For countries like Bangladesh, where a rickshaw is available as a non-motorized vehicle, such provisions can play a vital role in the access of these captive users. Besides, virus transmission among commuters can be reduced by educating commuters, raising awareness about COVID-19, providing them with coronavirus updates, and updating them on the impact of COVID-19 on public transit [55].
Several studies revealed a rise in walking activity during the COVID-19 pandemic among people who cannot afford to run cars or use public transport [59]. The ongoing pandemic created unemployment and affected economic stability [34]. Many economically backward regions do not have sufficiently well-maintained pedestrian facilities [60]. Hence, reducing public transit services in these regions may entice people to walk more in poor pedestrian facilities without maintaining proper social distancing procedures, which can increase the risk of virus exposure. Thus, the government needs to improve walkway facilities in the cities. For example, footpath width can be increased to increase social distancing among pedestrians. However, relying on supply management only will not be effective as substantial areas of footpaths are occupied by street vendors, on-street parking and other hindrances. Thus, urban planners need to focus on safe and smooth walkability. In addition to pedestrian activity, cycling activity was also noticeable in many countries during the pandemic. However, a few studies [27,47] on the opposite end of the spectrum have observed an insignificant increase in active transport (walking and cycling) despite the potential of bicycles to reduce air pollution and help maintain a healthy lifestyle. This might be because many cities around the world lack proper pedestrian and bicycle infrastructure [17], even though there are other influencing factors like cultural, climatic and topographic conditions. Hence, transport authorities should give attention to building and maintaining safe and adequate bicycle lanes to promote cycling among the youth and to attract the private vehicle riders to shift to bicycles. Moreover, adequate bike racks and necessary cycling-friendly facilities should be established at locations like malls, workplaces, restaurants, etc. that have high trip attractions. Although cycling facilities in the previous year have been expanded including new or expanded bike lanes and paths in large European and American cities [61], bike-sharing has suffered in many cities because of increased contact risk [62]. Further research needs to address ways to sanitize bicycles when transferring usage from one person to another. Usually, commuters have to make trips in the morning peak period even during the COVID-19 pandemic [63]. This may still create crowding situations and stimulate virus transmission. Hence, policymakers should dynamically plan by combining a range of strategies involving regulation of flexible operating hours of various managerial departments and essential businesses to cope with the increased demands of transport and shortage of options due to social distancing. Besides, the possibilities of work-from-home facilities should be explored further to shift more commuters into the virtual medium. All meetings, conferences and desk jobs should be encouraged to be performed online. Apart from work purposes, online facilities have been very popular for shopping. Shamshiripour et al. [48] mentioned that more than half the respondents would reportedly do online grocery shopping more frequently even long after the pandemic. Thus, helplines and psychological assistance services can be widened for all age groups and occupations to use the internet to meet a variety of social needs. However, developing countries usually have a large proportion of people who do not have access or have limited usage knowledge about Information and Comminucations Technologies (ICT) tools. Such countries should focus on ensuring how people can still participate in activities that have largely shifted to ICT solutions to reduce unnecessary travel demand [11]. In this modern age, artificial intelligence and machine learning can assist in the understanding of travel patterns and behaviour while facilitating dynamic future planning [64]. This will reduce virus exposure and road congestion.
Studies revealed that male trip-makers travel more compared to females even during the pandemic [3, 11]. This is because in developing countries like Bangladesh, most males have to work outside to earn for the family, whereas females look after the home [17]. Besides, males predominantly perform labour-intensive jobs and are hence more susceptible to COVID-19 infection. Thus, local authorities need to create awareness among men and take necessary action so that males take extra safety precautions during their pandemic trips. In addition, national authorities and policymakers should focus on the highly exposed age groups of people who make trips during the pandemic to minimize the virus contagion.
7. Conclusions
This article presents a review of the global findings on changes in transportation behaviour due to the COVID-19 pandemic along with an in-depth discussion on policymaking. Through a meticulous collection of research articles related to travel pattern changes during the present pandemic, this article presents a series of findings that include a brief discussion on mode shift and preferences during the pandemic, use of online facilities as an alternate of trip making, prime trip purposes during the pandemic, category of people at high risk of COVID-19 infection based on their travel behaviour and policy implications to provide insights for transport planners and policymakers. The article aims to showcase the scenario of daily travel all over the world due to the COVID-19 crisis that may help researchers conduct case studies under such circumstances in the future.
Previous studies showed reduced usage of public transportation but increased reliance on private cars [51, 37]. Restriction on public transit usage is viewed as an emergency, the response to which, at least in the short term, is an increased reliance on private modes. Non-motorized vehicle usage and walking prevalence increased mostly in European countries. Despite this pandemic situation, we must act now to move beyond ensuring the survival of public transport by developing the infrastructure of public transportation, which will be effective in emergencies like this. Moreover, observed preference for active transports (bicycling, walking) in few studies demands more attention of the infrastructure planners to develop enough facilities, attract people to these modes and reduce traffic congestion [3]. Flexible operating hours for commute and development in internet facilities for work purposes could minimize infection risks and decrease traffic congestions. Policymakers should emphasize the accessibility of online facilities because apart from work purposes, online shopping (especially grocery shopping) experienced increased internet usage in almost all studies. Furthermore, local authorities should provide extra security for males in developing countries where they are the main earning members, but cannot significantly reduce physical trips (especially for commute).
However, various aspects of the COVID-19 pandemic still need further investigation. Researchers are uncertain when the present pandemic will be over and are concerned about the varying impacts of different waves of COVID-19 infection [65]. First, it is important to compare and contrast the travel pattern change as the first, second and third waves of the virus manifest impact over different time frames. After every wave of impact, while some people may adapt to the ‘new normal’ and shift the trips to online activity, others may again increase the use of public transport due to their poor financial condition and escalate the risk of virus contagion. It is quite impossible to implement lockdowns months after months, as people who earn their living through physical activities and manual labour have to suffer because of the absence of work and inability to make trips to their work destination. The investigation of the overall scenario of the COVID-19 influence level on daily travel behaviour throughout the different waves of the pandemic is essential as this would help transport policymakers plan strategically to meet the travel demand in the post-pandemic condition. Although Beck and Hensher [22] studied the travel pattern change in Australia during the lockdown and after the easing of the restrictions, further research is required until the pandemic ceases. Second, as the production and the supply of vaccines have begun in many countries, the mobility pattern can be compared among the ‘before vaccination period’, ‘during vaccination period’ and ‘after vaccination period’. As no vaccine is 100% effective [66], the virus may spread even after the completion of vaccination, especially because newer variants/mutations of the coronavirus may resist vaccines better [67]. Moreover, vaccinated people may try to return to their previous way of life and make discretionary trips without wearing any masks or maintaining social distancing while travelling to crowded locations. This may put unvaccinated people at risk, because the vaccine cannot protect people against transmission or sickness, but can only reduce the severity of the disease in the body [68]. Moreover, many countries may not get an adequate dosage of the vaccine on time because of vaccine diplomacy [69]. Besides, online activities may decline and the frequency of long-distance trips may rise again. Third, a comparison of travel behaviour is necessary between rural and urban people. Usually, in cities, the high population density can create greater havoc than in rural areas and hence serious movement restriction is required in cities and towns [70, 3]. Usually, rural people are well self-subsistent and have to make less grocery trips. They can easily collect fruits and vegetables from farms and fishes from local ponds. On the contrary, urban people especially in developing countries have to make trips to buy groceries. Hence, the present pandemic may engage the urban to involve in subsistence through the creation of individual mini gardens on rooftops and in verandas [71].
Preparedness of the transportation sector across the world is required to deal with any future pandemic while minimizing the damage. Worldwide collaboration of researchers along with local and global policymakers could bring more impactful results. As this study has highlighted different types of changes in transportation behaviour during the pandemic across the world and analysed the measurements taken by the authorities to contain the virus contagion through public transport, it will provide insights for future researchers to compare and choose a definite and more effective method and estimate changes in activities and travel behaviour in the post-pandemic situation.
Conflict of interest statement
No conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all authors for publication.
References
Appendix
Location, medium of data collection and response count of studied literature.
Year . | Country . | Time of survey . | Medium/source . | Response . | Reference . |
---|---|---|---|---|---|
2021 | China | June 2020 | Questionnaire survey | 1284 | [30] |
2021 | USA | May 2020 | 12 500 | [28] | |
Questionnaire survey | 339 | ||||
2021 | Canada | 24 March to 9 May 2020 | Questionnaire survey (online) | 202 | [25] |
2021 | USA | – | Cell phone data and novel survey | – | [52] |
2021 | Pakistan | October 2020 to November 2020 | Questionnaire survey (online) | 1516 | [34] |
2021 | Bangladesh | November 2020 to January 2021 | Questionnaire survey (online) and face-to-face interview | 806 | [3] |
2021 | China | – | Questionnaire survey (online) | 531 | [72] |
China Statistical Yearbook 2020 | – | ||||
2021 | USA | June and July 2020 | Field survey | 125 | [26] |
2021 | Pakistan | September 2020 to November 2020 | Questionnaire survey (online) | 565 | [43] |
2021 | Germany | 29 June to 6 July 2020 | Questionnaire survey (online) | 3092 | [29] |
2021 | Bangladesh | 22 May to 29 May 2020 | Questionnaire survey (social media) | 317 | [33] |
India | 24 March to 12 April 2020 | 498 | |||
2021 | Greece | 6 April to 19 April 2020 | Questionnaire survey (before COVID-19) | 1462 | [40] |
Questionnaire survey (during COVID-19) | 196 | ||||
2021 | Germany | 6 April to 10 April 2020 | Questionnaire survey (online) | 1000 | [35] |
2021 | Greece | 6 April to 19 April 2020 | Questionnaire survey (online) | 1259 | [36] |
2021 | Germany | – | Questionnaire survey (online) | 2512 (without lockdown) | [44] |
21 March to 19 April 2020 | 1645 (with lockdown) | ||||
2021 | Sweden | Mid-April to the beginning pf May 2020 | Questionnaire survey (online) | 719 | [46] |
2021 | Germany | Telephone interview | 15 | [20] | |
April to May 2020 | Household survey | 301 | |||
2021 | Different countries (Italy, Sweden and India) | 20 April to 18 May 2020 | Questionnaire survey (online) | 781 | [47] |
2021 | Spain | – | Questionnaire survey (online) | 984 | [45] |
2021 | Canada | – | Questionnaire survey | 3860 | [50] |
2021 | Germany, Austria and Switzerland | March 2020 | Questionnaire survey | 1158 | [73] |
2 weeks later | 212 (2 weeks later) | ||||
2021 | Different countries | Between end of April and late May 2020 | WCTRS (World Conference on Transport Research Society) | 284 (experts) | [34] |
2021 | Bangladesh | 1 May to 30 June 2020 | Questionnaire survey (online) | 572 | [11] |
2021 | Switzerland | – | GPS and online survey | 1439 | [39] |
2021 | Pakistan | 9 May to 31 May 2020 | Questionnaire survey (online) | 671 | [41] |
2021 | International | 10 April to 10 May 2020 | Questionnaire survey (online) | 585 | [74] |
2021 | India | 29 April to 30 May 2020 | Questionnaire survey (online) | 840 | [51] |
2021 | China | February to March 2020 | Questionnaire survey (online) | 513 | [42] |
2021 | China | – | Questionnaire survey (online) | 559 | [75] |
2021 | Canada | June 2020 | Canadian Perspectives Survey Series (CPSS) 3: Resuming Economic and Social Activities During COVID-19 | A subset (n∼2900 representing 23 069 500 Canadians) of 4200 participants | [76] |
2021 | India | 15 March to 24 March 2020 | Questionnaire survey (online) | 1945 | [77] |
2021 | Indonesia | March to April 2020 | Questionnaire survey (online) | 1062 | [78] |
2021 | International (10 countries across 6 continents) | May 2020 | Questionnaire survey (online) | 9394 | [79] |
2021 | Gdansk, Poland | May to June 2020 | Diagnostic survey | 302 (public transport users) | [80] |
2021 | Bangladesh | 1 July to 31 August 2020 | Questionnaire survey (online) | 804 | [60] |
2021 | China | February and March 2020 | Questionnaire survey (online) | 513 | [81] |
2021 | Spain | March 2020 | Questionnaire survey (online) | 478 | [82] |
2021 | India | 20 May to 30 June 2020 | Questionnaire survey (online) | 410 | [83] |
2021 | USA | August 2020 | Smartphone-based panel | – | [84] |
Questionnaire survey | 531 | ||||
2020 | Canada | – | Questionnaire survey (online) | – | [85] |
2020 | USA | February to March in 2019 and 2020 | Open data policy | – | [86] |
2020 | Chile | 23 March to 29 March2020 | Questionnaire survey (online) | 4395 | [53] |
2020 | China | January to April in 2018, 2019 and 2020 | Web-mapping service | – | [87] |
2020 | Different Countries | 9 May to 31 May 2020 | Questionnaire survey (online) | 1203 | [17] |
2020 | India | 24 March to 12 April 2020 | Questionnaire survey (online) | 498 | [2] |
2020 | Spain | March 2020 | Traffic counters, public transport ITS, traffic control cameras and environmental sensors | – | [23] |
2020 | Netherlands | 27 March to 4 April 2020 | Netherlands Mobility Panel | Nearly 2500 | [49] |
2020 | Bangladesh | July to August 2020 | Questionnaire survey (online) | 800 | [31] |
2020 | Nigeria | 18 May to 24 May 2020 | Questionnaire survey (online) | 329 | [55] |
2020 | India | 24 March to 31 March 2020 | Questionnaire survey (online) | 3148 | [32] |
2020 | Sweden | Regional public transport authorities | – | [24] | |
2020 | Japan | Several times in January, February and April 2020 | Panel data web-survey | 800 | [38] |
2020 | Australia | 23 May to 15 June 2020 | Questionnaire survey using online panel survey company | 1073 | [21] |
2020 | Australia | 30 March to 15 April 2020 | In field survey | 762 (Wave 1) | [22] |
695 (additional in Wave 2) | |||||
2020 | USA | 25 April to 2 June 2020 | Questionnaire survey through Qualtrics | 915 (18+) | [48] |
2020 | Turkey | January to April 2020 | Questionnaire survey | 144 | [27] |
Year . | Country . | Time of survey . | Medium/source . | Response . | Reference . |
---|---|---|---|---|---|
2021 | China | June 2020 | Questionnaire survey | 1284 | [30] |
2021 | USA | May 2020 | 12 500 | [28] | |
Questionnaire survey | 339 | ||||
2021 | Canada | 24 March to 9 May 2020 | Questionnaire survey (online) | 202 | [25] |
2021 | USA | – | Cell phone data and novel survey | – | [52] |
2021 | Pakistan | October 2020 to November 2020 | Questionnaire survey (online) | 1516 | [34] |
2021 | Bangladesh | November 2020 to January 2021 | Questionnaire survey (online) and face-to-face interview | 806 | [3] |
2021 | China | – | Questionnaire survey (online) | 531 | [72] |
China Statistical Yearbook 2020 | – | ||||
2021 | USA | June and July 2020 | Field survey | 125 | [26] |
2021 | Pakistan | September 2020 to November 2020 | Questionnaire survey (online) | 565 | [43] |
2021 | Germany | 29 June to 6 July 2020 | Questionnaire survey (online) | 3092 | [29] |
2021 | Bangladesh | 22 May to 29 May 2020 | Questionnaire survey (social media) | 317 | [33] |
India | 24 March to 12 April 2020 | 498 | |||
2021 | Greece | 6 April to 19 April 2020 | Questionnaire survey (before COVID-19) | 1462 | [40] |
Questionnaire survey (during COVID-19) | 196 | ||||
2021 | Germany | 6 April to 10 April 2020 | Questionnaire survey (online) | 1000 | [35] |
2021 | Greece | 6 April to 19 April 2020 | Questionnaire survey (online) | 1259 | [36] |
2021 | Germany | – | Questionnaire survey (online) | 2512 (without lockdown) | [44] |
21 March to 19 April 2020 | 1645 (with lockdown) | ||||
2021 | Sweden | Mid-April to the beginning pf May 2020 | Questionnaire survey (online) | 719 | [46] |
2021 | Germany | Telephone interview | 15 | [20] | |
April to May 2020 | Household survey | 301 | |||
2021 | Different countries (Italy, Sweden and India) | 20 April to 18 May 2020 | Questionnaire survey (online) | 781 | [47] |
2021 | Spain | – | Questionnaire survey (online) | 984 | [45] |
2021 | Canada | – | Questionnaire survey | 3860 | [50] |
2021 | Germany, Austria and Switzerland | March 2020 | Questionnaire survey | 1158 | [73] |
2 weeks later | 212 (2 weeks later) | ||||
2021 | Different countries | Between end of April and late May 2020 | WCTRS (World Conference on Transport Research Society) | 284 (experts) | [34] |
2021 | Bangladesh | 1 May to 30 June 2020 | Questionnaire survey (online) | 572 | [11] |
2021 | Switzerland | – | GPS and online survey | 1439 | [39] |
2021 | Pakistan | 9 May to 31 May 2020 | Questionnaire survey (online) | 671 | [41] |
2021 | International | 10 April to 10 May 2020 | Questionnaire survey (online) | 585 | [74] |
2021 | India | 29 April to 30 May 2020 | Questionnaire survey (online) | 840 | [51] |
2021 | China | February to March 2020 | Questionnaire survey (online) | 513 | [42] |
2021 | China | – | Questionnaire survey (online) | 559 | [75] |
2021 | Canada | June 2020 | Canadian Perspectives Survey Series (CPSS) 3: Resuming Economic and Social Activities During COVID-19 | A subset (n∼2900 representing 23 069 500 Canadians) of 4200 participants | [76] |
2021 | India | 15 March to 24 March 2020 | Questionnaire survey (online) | 1945 | [77] |
2021 | Indonesia | March to April 2020 | Questionnaire survey (online) | 1062 | [78] |
2021 | International (10 countries across 6 continents) | May 2020 | Questionnaire survey (online) | 9394 | [79] |
2021 | Gdansk, Poland | May to June 2020 | Diagnostic survey | 302 (public transport users) | [80] |
2021 | Bangladesh | 1 July to 31 August 2020 | Questionnaire survey (online) | 804 | [60] |
2021 | China | February and March 2020 | Questionnaire survey (online) | 513 | [81] |
2021 | Spain | March 2020 | Questionnaire survey (online) | 478 | [82] |
2021 | India | 20 May to 30 June 2020 | Questionnaire survey (online) | 410 | [83] |
2021 | USA | August 2020 | Smartphone-based panel | – | [84] |
Questionnaire survey | 531 | ||||
2020 | Canada | – | Questionnaire survey (online) | – | [85] |
2020 | USA | February to March in 2019 and 2020 | Open data policy | – | [86] |
2020 | Chile | 23 March to 29 March2020 | Questionnaire survey (online) | 4395 | [53] |
2020 | China | January to April in 2018, 2019 and 2020 | Web-mapping service | – | [87] |
2020 | Different Countries | 9 May to 31 May 2020 | Questionnaire survey (online) | 1203 | [17] |
2020 | India | 24 March to 12 April 2020 | Questionnaire survey (online) | 498 | [2] |
2020 | Spain | March 2020 | Traffic counters, public transport ITS, traffic control cameras and environmental sensors | – | [23] |
2020 | Netherlands | 27 March to 4 April 2020 | Netherlands Mobility Panel | Nearly 2500 | [49] |
2020 | Bangladesh | July to August 2020 | Questionnaire survey (online) | 800 | [31] |
2020 | Nigeria | 18 May to 24 May 2020 | Questionnaire survey (online) | 329 | [55] |
2020 | India | 24 March to 31 March 2020 | Questionnaire survey (online) | 3148 | [32] |
2020 | Sweden | Regional public transport authorities | – | [24] | |
2020 | Japan | Several times in January, February and April 2020 | Panel data web-survey | 800 | [38] |
2020 | Australia | 23 May to 15 June 2020 | Questionnaire survey using online panel survey company | 1073 | [21] |
2020 | Australia | 30 March to 15 April 2020 | In field survey | 762 (Wave 1) | [22] |
695 (additional in Wave 2) | |||||
2020 | USA | 25 April to 2 June 2020 | Questionnaire survey through Qualtrics | 915 (18+) | [48] |
2020 | Turkey | January to April 2020 | Questionnaire survey | 144 | [27] |
Location, medium of data collection and response count of studied literature.
Year . | Country . | Time of survey . | Medium/source . | Response . | Reference . |
---|---|---|---|---|---|
2021 | China | June 2020 | Questionnaire survey | 1284 | [30] |
2021 | USA | May 2020 | 12 500 | [28] | |
Questionnaire survey | 339 | ||||
2021 | Canada | 24 March to 9 May 2020 | Questionnaire survey (online) | 202 | [25] |
2021 | USA | – | Cell phone data and novel survey | – | [52] |
2021 | Pakistan | October 2020 to November 2020 | Questionnaire survey (online) | 1516 | [34] |
2021 | Bangladesh | November 2020 to January 2021 | Questionnaire survey (online) and face-to-face interview | 806 | [3] |
2021 | China | – | Questionnaire survey (online) | 531 | [72] |
China Statistical Yearbook 2020 | – | ||||
2021 | USA | June and July 2020 | Field survey | 125 | [26] |
2021 | Pakistan | September 2020 to November 2020 | Questionnaire survey (online) | 565 | [43] |
2021 | Germany | 29 June to 6 July 2020 | Questionnaire survey (online) | 3092 | [29] |
2021 | Bangladesh | 22 May to 29 May 2020 | Questionnaire survey (social media) | 317 | [33] |
India | 24 March to 12 April 2020 | 498 | |||
2021 | Greece | 6 April to 19 April 2020 | Questionnaire survey (before COVID-19) | 1462 | [40] |
Questionnaire survey (during COVID-19) | 196 | ||||
2021 | Germany | 6 April to 10 April 2020 | Questionnaire survey (online) | 1000 | [35] |
2021 | Greece | 6 April to 19 April 2020 | Questionnaire survey (online) | 1259 | [36] |
2021 | Germany | – | Questionnaire survey (online) | 2512 (without lockdown) | [44] |
21 March to 19 April 2020 | 1645 (with lockdown) | ||||
2021 | Sweden | Mid-April to the beginning pf May 2020 | Questionnaire survey (online) | 719 | [46] |
2021 | Germany | Telephone interview | 15 | [20] | |
April to May 2020 | Household survey | 301 | |||
2021 | Different countries (Italy, Sweden and India) | 20 April to 18 May 2020 | Questionnaire survey (online) | 781 | [47] |
2021 | Spain | – | Questionnaire survey (online) | 984 | [45] |
2021 | Canada | – | Questionnaire survey | 3860 | [50] |
2021 | Germany, Austria and Switzerland | March 2020 | Questionnaire survey | 1158 | [73] |
2 weeks later | 212 (2 weeks later) | ||||
2021 | Different countries | Between end of April and late May 2020 | WCTRS (World Conference on Transport Research Society) | 284 (experts) | [34] |
2021 | Bangladesh | 1 May to 30 June 2020 | Questionnaire survey (online) | 572 | [11] |
2021 | Switzerland | – | GPS and online survey | 1439 | [39] |
2021 | Pakistan | 9 May to 31 May 2020 | Questionnaire survey (online) | 671 | [41] |
2021 | International | 10 April to 10 May 2020 | Questionnaire survey (online) | 585 | [74] |
2021 | India | 29 April to 30 May 2020 | Questionnaire survey (online) | 840 | [51] |
2021 | China | February to March 2020 | Questionnaire survey (online) | 513 | [42] |
2021 | China | – | Questionnaire survey (online) | 559 | [75] |
2021 | Canada | June 2020 | Canadian Perspectives Survey Series (CPSS) 3: Resuming Economic and Social Activities During COVID-19 | A subset (n∼2900 representing 23 069 500 Canadians) of 4200 participants | [76] |
2021 | India | 15 March to 24 March 2020 | Questionnaire survey (online) | 1945 | [77] |
2021 | Indonesia | March to April 2020 | Questionnaire survey (online) | 1062 | [78] |
2021 | International (10 countries across 6 continents) | May 2020 | Questionnaire survey (online) | 9394 | [79] |
2021 | Gdansk, Poland | May to June 2020 | Diagnostic survey | 302 (public transport users) | [80] |
2021 | Bangladesh | 1 July to 31 August 2020 | Questionnaire survey (online) | 804 | [60] |
2021 | China | February and March 2020 | Questionnaire survey (online) | 513 | [81] |
2021 | Spain | March 2020 | Questionnaire survey (online) | 478 | [82] |
2021 | India | 20 May to 30 June 2020 | Questionnaire survey (online) | 410 | [83] |
2021 | USA | August 2020 | Smartphone-based panel | – | [84] |
Questionnaire survey | 531 | ||||
2020 | Canada | – | Questionnaire survey (online) | – | [85] |
2020 | USA | February to March in 2019 and 2020 | Open data policy | – | [86] |
2020 | Chile | 23 March to 29 March2020 | Questionnaire survey (online) | 4395 | [53] |
2020 | China | January to April in 2018, 2019 and 2020 | Web-mapping service | – | [87] |
2020 | Different Countries | 9 May to 31 May 2020 | Questionnaire survey (online) | 1203 | [17] |
2020 | India | 24 March to 12 April 2020 | Questionnaire survey (online) | 498 | [2] |
2020 | Spain | March 2020 | Traffic counters, public transport ITS, traffic control cameras and environmental sensors | – | [23] |
2020 | Netherlands | 27 March to 4 April 2020 | Netherlands Mobility Panel | Nearly 2500 | [49] |
2020 | Bangladesh | July to August 2020 | Questionnaire survey (online) | 800 | [31] |
2020 | Nigeria | 18 May to 24 May 2020 | Questionnaire survey (online) | 329 | [55] |
2020 | India | 24 March to 31 March 2020 | Questionnaire survey (online) | 3148 | [32] |
2020 | Sweden | Regional public transport authorities | – | [24] | |
2020 | Japan | Several times in January, February and April 2020 | Panel data web-survey | 800 | [38] |
2020 | Australia | 23 May to 15 June 2020 | Questionnaire survey using online panel survey company | 1073 | [21] |
2020 | Australia | 30 March to 15 April 2020 | In field survey | 762 (Wave 1) | [22] |
695 (additional in Wave 2) | |||||
2020 | USA | 25 April to 2 June 2020 | Questionnaire survey through Qualtrics | 915 (18+) | [48] |
2020 | Turkey | January to April 2020 | Questionnaire survey | 144 | [27] |
Year . | Country . | Time of survey . | Medium/source . | Response . | Reference . |
---|---|---|---|---|---|
2021 | China | June 2020 | Questionnaire survey | 1284 | [30] |
2021 | USA | May 2020 | 12 500 | [28] | |
Questionnaire survey | 339 | ||||
2021 | Canada | 24 March to 9 May 2020 | Questionnaire survey (online) | 202 | [25] |
2021 | USA | – | Cell phone data and novel survey | – | [52] |
2021 | Pakistan | October 2020 to November 2020 | Questionnaire survey (online) | 1516 | [34] |
2021 | Bangladesh | November 2020 to January 2021 | Questionnaire survey (online) and face-to-face interview | 806 | [3] |
2021 | China | – | Questionnaire survey (online) | 531 | [72] |
China Statistical Yearbook 2020 | – | ||||
2021 | USA | June and July 2020 | Field survey | 125 | [26] |
2021 | Pakistan | September 2020 to November 2020 | Questionnaire survey (online) | 565 | [43] |
2021 | Germany | 29 June to 6 July 2020 | Questionnaire survey (online) | 3092 | [29] |
2021 | Bangladesh | 22 May to 29 May 2020 | Questionnaire survey (social media) | 317 | [33] |
India | 24 March to 12 April 2020 | 498 | |||
2021 | Greece | 6 April to 19 April 2020 | Questionnaire survey (before COVID-19) | 1462 | [40] |
Questionnaire survey (during COVID-19) | 196 | ||||
2021 | Germany | 6 April to 10 April 2020 | Questionnaire survey (online) | 1000 | [35] |
2021 | Greece | 6 April to 19 April 2020 | Questionnaire survey (online) | 1259 | [36] |
2021 | Germany | – | Questionnaire survey (online) | 2512 (without lockdown) | [44] |
21 March to 19 April 2020 | 1645 (with lockdown) | ||||
2021 | Sweden | Mid-April to the beginning pf May 2020 | Questionnaire survey (online) | 719 | [46] |
2021 | Germany | Telephone interview | 15 | [20] | |
April to May 2020 | Household survey | 301 | |||
2021 | Different countries (Italy, Sweden and India) | 20 April to 18 May 2020 | Questionnaire survey (online) | 781 | [47] |
2021 | Spain | – | Questionnaire survey (online) | 984 | [45] |
2021 | Canada | – | Questionnaire survey | 3860 | [50] |
2021 | Germany, Austria and Switzerland | March 2020 | Questionnaire survey | 1158 | [73] |
2 weeks later | 212 (2 weeks later) | ||||
2021 | Different countries | Between end of April and late May 2020 | WCTRS (World Conference on Transport Research Society) | 284 (experts) | [34] |
2021 | Bangladesh | 1 May to 30 June 2020 | Questionnaire survey (online) | 572 | [11] |
2021 | Switzerland | – | GPS and online survey | 1439 | [39] |
2021 | Pakistan | 9 May to 31 May 2020 | Questionnaire survey (online) | 671 | [41] |
2021 | International | 10 April to 10 May 2020 | Questionnaire survey (online) | 585 | [74] |
2021 | India | 29 April to 30 May 2020 | Questionnaire survey (online) | 840 | [51] |
2021 | China | February to March 2020 | Questionnaire survey (online) | 513 | [42] |
2021 | China | – | Questionnaire survey (online) | 559 | [75] |
2021 | Canada | June 2020 | Canadian Perspectives Survey Series (CPSS) 3: Resuming Economic and Social Activities During COVID-19 | A subset (n∼2900 representing 23 069 500 Canadians) of 4200 participants | [76] |
2021 | India | 15 March to 24 March 2020 | Questionnaire survey (online) | 1945 | [77] |
2021 | Indonesia | March to April 2020 | Questionnaire survey (online) | 1062 | [78] |
2021 | International (10 countries across 6 continents) | May 2020 | Questionnaire survey (online) | 9394 | [79] |
2021 | Gdansk, Poland | May to June 2020 | Diagnostic survey | 302 (public transport users) | [80] |
2021 | Bangladesh | 1 July to 31 August 2020 | Questionnaire survey (online) | 804 | [60] |
2021 | China | February and March 2020 | Questionnaire survey (online) | 513 | [81] |
2021 | Spain | March 2020 | Questionnaire survey (online) | 478 | [82] |
2021 | India | 20 May to 30 June 2020 | Questionnaire survey (online) | 410 | [83] |
2021 | USA | August 2020 | Smartphone-based panel | – | [84] |
Questionnaire survey | 531 | ||||
2020 | Canada | – | Questionnaire survey (online) | – | [85] |
2020 | USA | February to March in 2019 and 2020 | Open data policy | – | [86] |
2020 | Chile | 23 March to 29 March2020 | Questionnaire survey (online) | 4395 | [53] |
2020 | China | January to April in 2018, 2019 and 2020 | Web-mapping service | – | [87] |
2020 | Different Countries | 9 May to 31 May 2020 | Questionnaire survey (online) | 1203 | [17] |
2020 | India | 24 March to 12 April 2020 | Questionnaire survey (online) | 498 | [2] |
2020 | Spain | March 2020 | Traffic counters, public transport ITS, traffic control cameras and environmental sensors | – | [23] |
2020 | Netherlands | 27 March to 4 April 2020 | Netherlands Mobility Panel | Nearly 2500 | [49] |
2020 | Bangladesh | July to August 2020 | Questionnaire survey (online) | 800 | [31] |
2020 | Nigeria | 18 May to 24 May 2020 | Questionnaire survey (online) | 329 | [55] |
2020 | India | 24 March to 31 March 2020 | Questionnaire survey (online) | 3148 | [32] |
2020 | Sweden | Regional public transport authorities | – | [24] | |
2020 | Japan | Several times in January, February and April 2020 | Panel data web-survey | 800 | [38] |
2020 | Australia | 23 May to 15 June 2020 | Questionnaire survey using online panel survey company | 1073 | [21] |
2020 | Australia | 30 March to 15 April 2020 | In field survey | 762 (Wave 1) | [22] |
695 (additional in Wave 2) | |||||
2020 | USA | 25 April to 2 June 2020 | Questionnaire survey through Qualtrics | 915 (18+) | [48] |
2020 | Turkey | January to April 2020 | Questionnaire survey | 144 | [27] |
Ref. . | Type of investigation . | Modes used in the study . | Largest increase in mode usage caused by the COVID-19 pandemic . | Largest decrease in mode usage caused by COVID-19 pandemic . | Modes most preferred during COVID-19 . | Modes least preferred during COVID-19 . |
---|---|---|---|---|---|---|
[41] | Mode usage comparison due to COVID-19 | Public transport, office transport, taxi/rickshaw, private car, motorcycle, bicycle, walking | Short distance (<5 km): private car (2% rise); bicycle (1% rise) | Short distance (<5 km): public transport (2% drop) | – | – |
All except bicycle and walking for negligible response | Long-distance (>5km): private car (3% rise); motorcycle (2% rise) | Long-distance (>5 km): public transport (5% drop) | – | – | ||
[74] | Mode usage comparison due to COVID-19 | Public transport, motorcycle, bike, car, walk | – | Bus (62.4% drop) Railways (30.7%) | – | – |
[51] | Mode usage comparison due to COVID-19 | Shared transport, unshared transport, and active travel mode | Car (80% of the original users); other modes (59%) (excluding public transport), car and original mode) | Public transport (23% of original users); shared cabs (10% of original users) | – | – |
[42] | Mode usage during COVID-19 | Car, public transit, semi-public transit, nonmotorized transit | Car (64%) | Public transport and walk (2.7%) | – | – |
[86] | Mode usage comparison due to COVID-19 | Subway and city bike ridership | – | Subway (90% decrease) | – | – |
[3] | Mode preference during COVID-19 | Bus, car, rickshaw, motorcycle, cycle, leguna, C.N.G.(CNG-run 3-wheelers locally known as the C.N.G.), walk | Walk | Bus | Walk | Cycle |
[53] | Mode usage comparison due to COVID-19 | Metro, ride-hailing, bus, motorcycle, auto and walking | – | Metro (55% decrease); ride-hailing (51% decrease); bus (45% decrease) | – | – |
[76] | Mode usage comparison of pre COVID-19 transit users | Transit, personal motor vehicle, cycling, and walking | – | – | Transit (18.2%); personal motor vehicle (13%) | – |
Mode usage comparison due to COVID-19 | Walking, cycling, personal motor vehicle, carpooling, public transit, and others | Personal motor vehicle | Carpooling or rideshare | – | – | |
[78] | Mode usage during COVID-19 | Public transit, ride-hailing, motorcycle, car and bicycle | Motorcycle (50%) | – | – | – |
[77] | Safety perception analysis on mode usage | Public transport. private vehicle, taxi, bicycle and walk | – | – | Private Vehicle | Public Transport |
[80] | Public transport usage during COVID-19 | Public transport | – | Only 9% of previous public transport users used | – | – |
[60] | Expected changes in mode usage during COVID-19 | Public transport, shared vehicles, walking and cycling | – | – | Walking and cycling | Public transport and shared vehicle |
[79] | Mode usage during COVID-19 | Public transport, private transport | – | Public transport across the countries assessed | – | – |
[81] | Mode usage during COVID-19 | Car, public transit, semi-public transit, non-motorized vehicles | Car | Public transit | ||
[85] | Mode usage comparison during COVID-19 | Personal vehicle, public transit, bicycle, taxi/ride share, walking | Walking, cycling and personal vehicle | Public transit | – | – |
[75] | Probability of self-infection using various modes | On foot/bicycle, taxi/taxi-hailing, private car, public transport | – | – | On foot/bicycle | Public transport taxi/taxi-hailing |
[84] | Mode usage of transit and non-transit riders during COVID-19 | Personal vehicles, biking, ride sharing, carpooling | Driving vehicles | Carpooling services | – | – |
[82] | Mode usage comparison during COVID-19 | Public transport, private vehicle, motorcycle, bike, walking and others | Private vehicles | Public transport, bike | – | – |
[83] | The shift of mode in work trips | Public transport, personal vehicle, ride-hailing service and non-motorized vehicle | Personal vehicles | Public transport | – | – |
[87] | Mode usage comparison during COVID-19 | Public transit, walk, private vehicle and bicycle | Private vehicles, bicycles | Public transit | – | – |
[28] | Mode preference during COVID-19 | Ride-hailing services, public transit services, organized ride-sharing services | – | – | Ride-hailing services | Organized ridesharing programs |
[25] | Travel satisfaction | Car, walk, or bike | – | – | Bike walk | – |
[26] | Mode usage comparison due to COVID-19 | Bike-sharing services | Bike-sharing services (for 43% of unemployed respondents) | Bike-sharing services (for 36% of employed respondents) | – | – |
[88] | Mode preference during COVID-19 | Solo modes, public transport | – | – | Solo modes | Public transport |
[43] | Mode usage comparison due to COVID-19 | Public transport, para-transit transport, car, two-wheelers, walking, shared transport | Car | Public transport | Two-wheelers, walking | Para-transit transport, shared transport |
[29] | Mode usage comparison due to COVID-19 | Bike, car, public transportation, walk long-distance train, remote bus, car-sharing, plane | Walk | Public transport | – | – |
[33] | Mode usage shifting due to COVID-19 | Active transport, NMT (non-motorized transport), private vehicles, shared vehicles, ride-hailing services | Active mode (walking and cycling); private mode (private car, motorcycle, and office cars) | Shared mode (bus, human hauler, and auto-rickshaw); ride-hailing services | – | – |
NMT | Shared mode (bus, human hauler, and auto-rickshaw); ride-hailing services | – | – | |||
[31] | Mode usage comparison due to COVID-19 | Car, ride-hailing, rickshaw, cng auto-rickshaw, bus, motorcycle, walk, bicycle | Walk | Bus, rickshaw, CNG auto-rickshaw | – | – |
[36] | Mode usage comparison due to COVID-19 | Car, walk, public transport, bicycle, other | Walk, bicycle | Public transport | – | – |
[35] | Mode usage comparison due to COVID-19 | Bicycle, public transport, car | Private car, bicycle | Public transport | – | – |
[17] | Mode usage comparison due to COVID-19 | Public transport, private car, office/campus transport, taxi, rickshaw, motorcycle, bicycle, walking | Walk, private car | Public transport | – | – |
[44] | Mode usage comparison due to COVID-19 | Car, train, walking, cycling, bus/tram, other | Car, walk, bicycle | Bus/tram train | – | – |
[47] | Mode usage comparison due to COVID-19 | Car, motorcycle, public transport, bicycle or walk, other | Car | Public transport | – | – |
[39] | Mode usage comparison due to COVID-19 | Bicycle, bus, car, train, tram, walk, ferry, metro | Bicycle | Public transport (bus, tram, ferry, metro, train) | – | – |
[30] | Mode choice probability | Bus, metro, shared-transit, private car | – | – | Walk (carless respondents for both purposes); private car (car owner respondents for commute); walk (car owner respondents for entertainment/ shopping) | Ride-hailing/taxi (carless respondents for commute); private car (carless respondents for entertainment/shopping); ride-hailing/taxi (car owner respondents for both purposes) |
[2] | Mode usage comparison due to COVID-19 | Non-motorized transport, auto-rickshaw, taxi, ride-hailing, car, motorbike, ride-sharing, bus, railway | Personal vehicles (discretionary purposes) | On-demand private vehicles (for both commute and discretionary purposes) | – | – |
[40] | Mode usage comparison due to COVID-19 | Bicycle, motorcycle, walking, private car, public transport, special bus, taxi | Private car; walk | Public transport | – | – |
[34] | Modal shifts from public transport | Car, walk, cycle, motorcycle, others | – | – | From public transport to car | From public transport to motorcycle |
[72] | Mode usage comparison due to COVID-19 | Public transport, sharing bike or car, taxi, bike, e-bike, car, on foot | Private car | Public transport | – | – |
[22] | Comfortable perspective | Private car, walk/bicycle, train/light rail, bus, taxi/ride-hail, ferry | – | – | Private car | Bus |
[49] | Mode usage comparison due to COVID-19 | Car, train, bus/tram/metro, moped, bicycle, walk, others | Walk; car as driver; bicycle | Bus; train | – | – |
[48] | Risk perceptions | Personal vehicles, taxi, and ride-hailing, pooled ride-hailing, transit, shared bike, private bike, shared electric-scooter/moped, walk | – | – | Personal vehicles | Transit; taxi; ride-hailing services |
[27] | Mode usage comparison due to COVID-19 | Walk, cycle, road public transport, rail, private car, rideshare | Private car | Public transport (train, bus, BRT and minibus) | – | – |
[24] | Flow comparison with public transport | Public transport (metro, bus, commuter trains, trams), bike, walk, motor vehicles | – | – | Bike | – |
[32] | Mode usage comparison due to COVID-19 | Walk, bicycle, two-wheeler, car, taxi, auto, public transport, other | Walk; car | Taxi; auto-rickshaw; public transport | – | – |
[23] | Mode usage comparison due to COVID-19 | Walk, private transport (motorized), public transport, others | Private car | Public transport | – | – |
[11] | Mode usage comparison due to COVID-19 | Public transport, Private vehicle, paratransit, non-motorised vehicle, walk, others | Non-motorized vehicle | Public transport | – | – |
[21] | Mode usage comparison due to COVID-19 | Private car, taxi/ride-hailing, train, bus, ferry, walk/cycle | Private car Walk | Public transport | – | – |
Ref. . | Type of investigation . | Modes used in the study . | Largest increase in mode usage caused by the COVID-19 pandemic . | Largest decrease in mode usage caused by COVID-19 pandemic . | Modes most preferred during COVID-19 . | Modes least preferred during COVID-19 . |
---|---|---|---|---|---|---|
[41] | Mode usage comparison due to COVID-19 | Public transport, office transport, taxi/rickshaw, private car, motorcycle, bicycle, walking | Short distance (<5 km): private car (2% rise); bicycle (1% rise) | Short distance (<5 km): public transport (2% drop) | – | – |
All except bicycle and walking for negligible response | Long-distance (>5km): private car (3% rise); motorcycle (2% rise) | Long-distance (>5 km): public transport (5% drop) | – | – | ||
[74] | Mode usage comparison due to COVID-19 | Public transport, motorcycle, bike, car, walk | – | Bus (62.4% drop) Railways (30.7%) | – | – |
[51] | Mode usage comparison due to COVID-19 | Shared transport, unshared transport, and active travel mode | Car (80% of the original users); other modes (59%) (excluding public transport), car and original mode) | Public transport (23% of original users); shared cabs (10% of original users) | – | – |
[42] | Mode usage during COVID-19 | Car, public transit, semi-public transit, nonmotorized transit | Car (64%) | Public transport and walk (2.7%) | – | – |
[86] | Mode usage comparison due to COVID-19 | Subway and city bike ridership | – | Subway (90% decrease) | – | – |
[3] | Mode preference during COVID-19 | Bus, car, rickshaw, motorcycle, cycle, leguna, C.N.G.(CNG-run 3-wheelers locally known as the C.N.G.), walk | Walk | Bus | Walk | Cycle |
[53] | Mode usage comparison due to COVID-19 | Metro, ride-hailing, bus, motorcycle, auto and walking | – | Metro (55% decrease); ride-hailing (51% decrease); bus (45% decrease) | – | – |
[76] | Mode usage comparison of pre COVID-19 transit users | Transit, personal motor vehicle, cycling, and walking | – | – | Transit (18.2%); personal motor vehicle (13%) | – |
Mode usage comparison due to COVID-19 | Walking, cycling, personal motor vehicle, carpooling, public transit, and others | Personal motor vehicle | Carpooling or rideshare | – | – | |
[78] | Mode usage during COVID-19 | Public transit, ride-hailing, motorcycle, car and bicycle | Motorcycle (50%) | – | – | – |
[77] | Safety perception analysis on mode usage | Public transport. private vehicle, taxi, bicycle and walk | – | – | Private Vehicle | Public Transport |
[80] | Public transport usage during COVID-19 | Public transport | – | Only 9% of previous public transport users used | – | – |
[60] | Expected changes in mode usage during COVID-19 | Public transport, shared vehicles, walking and cycling | – | – | Walking and cycling | Public transport and shared vehicle |
[79] | Mode usage during COVID-19 | Public transport, private transport | – | Public transport across the countries assessed | – | – |
[81] | Mode usage during COVID-19 | Car, public transit, semi-public transit, non-motorized vehicles | Car | Public transit | ||
[85] | Mode usage comparison during COVID-19 | Personal vehicle, public transit, bicycle, taxi/ride share, walking | Walking, cycling and personal vehicle | Public transit | – | – |
[75] | Probability of self-infection using various modes | On foot/bicycle, taxi/taxi-hailing, private car, public transport | – | – | On foot/bicycle | Public transport taxi/taxi-hailing |
[84] | Mode usage of transit and non-transit riders during COVID-19 | Personal vehicles, biking, ride sharing, carpooling | Driving vehicles | Carpooling services | – | – |
[82] | Mode usage comparison during COVID-19 | Public transport, private vehicle, motorcycle, bike, walking and others | Private vehicles | Public transport, bike | – | – |
[83] | The shift of mode in work trips | Public transport, personal vehicle, ride-hailing service and non-motorized vehicle | Personal vehicles | Public transport | – | – |
[87] | Mode usage comparison during COVID-19 | Public transit, walk, private vehicle and bicycle | Private vehicles, bicycles | Public transit | – | – |
[28] | Mode preference during COVID-19 | Ride-hailing services, public transit services, organized ride-sharing services | – | – | Ride-hailing services | Organized ridesharing programs |
[25] | Travel satisfaction | Car, walk, or bike | – | – | Bike walk | – |
[26] | Mode usage comparison due to COVID-19 | Bike-sharing services | Bike-sharing services (for 43% of unemployed respondents) | Bike-sharing services (for 36% of employed respondents) | – | – |
[88] | Mode preference during COVID-19 | Solo modes, public transport | – | – | Solo modes | Public transport |
[43] | Mode usage comparison due to COVID-19 | Public transport, para-transit transport, car, two-wheelers, walking, shared transport | Car | Public transport | Two-wheelers, walking | Para-transit transport, shared transport |
[29] | Mode usage comparison due to COVID-19 | Bike, car, public transportation, walk long-distance train, remote bus, car-sharing, plane | Walk | Public transport | – | – |
[33] | Mode usage shifting due to COVID-19 | Active transport, NMT (non-motorized transport), private vehicles, shared vehicles, ride-hailing services | Active mode (walking and cycling); private mode (private car, motorcycle, and office cars) | Shared mode (bus, human hauler, and auto-rickshaw); ride-hailing services | – | – |
NMT | Shared mode (bus, human hauler, and auto-rickshaw); ride-hailing services | – | – | |||
[31] | Mode usage comparison due to COVID-19 | Car, ride-hailing, rickshaw, cng auto-rickshaw, bus, motorcycle, walk, bicycle | Walk | Bus, rickshaw, CNG auto-rickshaw | – | – |
[36] | Mode usage comparison due to COVID-19 | Car, walk, public transport, bicycle, other | Walk, bicycle | Public transport | – | – |
[35] | Mode usage comparison due to COVID-19 | Bicycle, public transport, car | Private car, bicycle | Public transport | – | – |
[17] | Mode usage comparison due to COVID-19 | Public transport, private car, office/campus transport, taxi, rickshaw, motorcycle, bicycle, walking | Walk, private car | Public transport | – | – |
[44] | Mode usage comparison due to COVID-19 | Car, train, walking, cycling, bus/tram, other | Car, walk, bicycle | Bus/tram train | – | – |
[47] | Mode usage comparison due to COVID-19 | Car, motorcycle, public transport, bicycle or walk, other | Car | Public transport | – | – |
[39] | Mode usage comparison due to COVID-19 | Bicycle, bus, car, train, tram, walk, ferry, metro | Bicycle | Public transport (bus, tram, ferry, metro, train) | – | – |
[30] | Mode choice probability | Bus, metro, shared-transit, private car | – | – | Walk (carless respondents for both purposes); private car (car owner respondents for commute); walk (car owner respondents for entertainment/ shopping) | Ride-hailing/taxi (carless respondents for commute); private car (carless respondents for entertainment/shopping); ride-hailing/taxi (car owner respondents for both purposes) |
[2] | Mode usage comparison due to COVID-19 | Non-motorized transport, auto-rickshaw, taxi, ride-hailing, car, motorbike, ride-sharing, bus, railway | Personal vehicles (discretionary purposes) | On-demand private vehicles (for both commute and discretionary purposes) | – | – |
[40] | Mode usage comparison due to COVID-19 | Bicycle, motorcycle, walking, private car, public transport, special bus, taxi | Private car; walk | Public transport | – | – |
[34] | Modal shifts from public transport | Car, walk, cycle, motorcycle, others | – | – | From public transport to car | From public transport to motorcycle |
[72] | Mode usage comparison due to COVID-19 | Public transport, sharing bike or car, taxi, bike, e-bike, car, on foot | Private car | Public transport | – | – |
[22] | Comfortable perspective | Private car, walk/bicycle, train/light rail, bus, taxi/ride-hail, ferry | – | – | Private car | Bus |
[49] | Mode usage comparison due to COVID-19 | Car, train, bus/tram/metro, moped, bicycle, walk, others | Walk; car as driver; bicycle | Bus; train | – | – |
[48] | Risk perceptions | Personal vehicles, taxi, and ride-hailing, pooled ride-hailing, transit, shared bike, private bike, shared electric-scooter/moped, walk | – | – | Personal vehicles | Transit; taxi; ride-hailing services |
[27] | Mode usage comparison due to COVID-19 | Walk, cycle, road public transport, rail, private car, rideshare | Private car | Public transport (train, bus, BRT and minibus) | – | – |
[24] | Flow comparison with public transport | Public transport (metro, bus, commuter trains, trams), bike, walk, motor vehicles | – | – | Bike | – |
[32] | Mode usage comparison due to COVID-19 | Walk, bicycle, two-wheeler, car, taxi, auto, public transport, other | Walk; car | Taxi; auto-rickshaw; public transport | – | – |
[23] | Mode usage comparison due to COVID-19 | Walk, private transport (motorized), public transport, others | Private car | Public transport | – | – |
[11] | Mode usage comparison due to COVID-19 | Public transport, Private vehicle, paratransit, non-motorised vehicle, walk, others | Non-motorized vehicle | Public transport | – | – |
[21] | Mode usage comparison due to COVID-19 | Private car, taxi/ride-hailing, train, bus, ferry, walk/cycle | Private car Walk | Public transport | – | – |
Ref. . | Type of investigation . | Modes used in the study . | Largest increase in mode usage caused by the COVID-19 pandemic . | Largest decrease in mode usage caused by COVID-19 pandemic . | Modes most preferred during COVID-19 . | Modes least preferred during COVID-19 . |
---|---|---|---|---|---|---|
[41] | Mode usage comparison due to COVID-19 | Public transport, office transport, taxi/rickshaw, private car, motorcycle, bicycle, walking | Short distance (<5 km): private car (2% rise); bicycle (1% rise) | Short distance (<5 km): public transport (2% drop) | – | – |
All except bicycle and walking for negligible response | Long-distance (>5km): private car (3% rise); motorcycle (2% rise) | Long-distance (>5 km): public transport (5% drop) | – | – | ||
[74] | Mode usage comparison due to COVID-19 | Public transport, motorcycle, bike, car, walk | – | Bus (62.4% drop) Railways (30.7%) | – | – |
[51] | Mode usage comparison due to COVID-19 | Shared transport, unshared transport, and active travel mode | Car (80% of the original users); other modes (59%) (excluding public transport), car and original mode) | Public transport (23% of original users); shared cabs (10% of original users) | – | – |
[42] | Mode usage during COVID-19 | Car, public transit, semi-public transit, nonmotorized transit | Car (64%) | Public transport and walk (2.7%) | – | – |
[86] | Mode usage comparison due to COVID-19 | Subway and city bike ridership | – | Subway (90% decrease) | – | – |
[3] | Mode preference during COVID-19 | Bus, car, rickshaw, motorcycle, cycle, leguna, C.N.G.(CNG-run 3-wheelers locally known as the C.N.G.), walk | Walk | Bus | Walk | Cycle |
[53] | Mode usage comparison due to COVID-19 | Metro, ride-hailing, bus, motorcycle, auto and walking | – | Metro (55% decrease); ride-hailing (51% decrease); bus (45% decrease) | – | – |
[76] | Mode usage comparison of pre COVID-19 transit users | Transit, personal motor vehicle, cycling, and walking | – | – | Transit (18.2%); personal motor vehicle (13%) | – |
Mode usage comparison due to COVID-19 | Walking, cycling, personal motor vehicle, carpooling, public transit, and others | Personal motor vehicle | Carpooling or rideshare | – | – | |
[78] | Mode usage during COVID-19 | Public transit, ride-hailing, motorcycle, car and bicycle | Motorcycle (50%) | – | – | – |
[77] | Safety perception analysis on mode usage | Public transport. private vehicle, taxi, bicycle and walk | – | – | Private Vehicle | Public Transport |
[80] | Public transport usage during COVID-19 | Public transport | – | Only 9% of previous public transport users used | – | – |
[60] | Expected changes in mode usage during COVID-19 | Public transport, shared vehicles, walking and cycling | – | – | Walking and cycling | Public transport and shared vehicle |
[79] | Mode usage during COVID-19 | Public transport, private transport | – | Public transport across the countries assessed | – | – |
[81] | Mode usage during COVID-19 | Car, public transit, semi-public transit, non-motorized vehicles | Car | Public transit | ||
[85] | Mode usage comparison during COVID-19 | Personal vehicle, public transit, bicycle, taxi/ride share, walking | Walking, cycling and personal vehicle | Public transit | – | – |
[75] | Probability of self-infection using various modes | On foot/bicycle, taxi/taxi-hailing, private car, public transport | – | – | On foot/bicycle | Public transport taxi/taxi-hailing |
[84] | Mode usage of transit and non-transit riders during COVID-19 | Personal vehicles, biking, ride sharing, carpooling | Driving vehicles | Carpooling services | – | – |
[82] | Mode usage comparison during COVID-19 | Public transport, private vehicle, motorcycle, bike, walking and others | Private vehicles | Public transport, bike | – | – |
[83] | The shift of mode in work trips | Public transport, personal vehicle, ride-hailing service and non-motorized vehicle | Personal vehicles | Public transport | – | – |
[87] | Mode usage comparison during COVID-19 | Public transit, walk, private vehicle and bicycle | Private vehicles, bicycles | Public transit | – | – |
[28] | Mode preference during COVID-19 | Ride-hailing services, public transit services, organized ride-sharing services | – | – | Ride-hailing services | Organized ridesharing programs |
[25] | Travel satisfaction | Car, walk, or bike | – | – | Bike walk | – |
[26] | Mode usage comparison due to COVID-19 | Bike-sharing services | Bike-sharing services (for 43% of unemployed respondents) | Bike-sharing services (for 36% of employed respondents) | – | – |
[88] | Mode preference during COVID-19 | Solo modes, public transport | – | – | Solo modes | Public transport |
[43] | Mode usage comparison due to COVID-19 | Public transport, para-transit transport, car, two-wheelers, walking, shared transport | Car | Public transport | Two-wheelers, walking | Para-transit transport, shared transport |
[29] | Mode usage comparison due to COVID-19 | Bike, car, public transportation, walk long-distance train, remote bus, car-sharing, plane | Walk | Public transport | – | – |
[33] | Mode usage shifting due to COVID-19 | Active transport, NMT (non-motorized transport), private vehicles, shared vehicles, ride-hailing services | Active mode (walking and cycling); private mode (private car, motorcycle, and office cars) | Shared mode (bus, human hauler, and auto-rickshaw); ride-hailing services | – | – |
NMT | Shared mode (bus, human hauler, and auto-rickshaw); ride-hailing services | – | – | |||
[31] | Mode usage comparison due to COVID-19 | Car, ride-hailing, rickshaw, cng auto-rickshaw, bus, motorcycle, walk, bicycle | Walk | Bus, rickshaw, CNG auto-rickshaw | – | – |
[36] | Mode usage comparison due to COVID-19 | Car, walk, public transport, bicycle, other | Walk, bicycle | Public transport | – | – |
[35] | Mode usage comparison due to COVID-19 | Bicycle, public transport, car | Private car, bicycle | Public transport | – | – |
[17] | Mode usage comparison due to COVID-19 | Public transport, private car, office/campus transport, taxi, rickshaw, motorcycle, bicycle, walking | Walk, private car | Public transport | – | – |
[44] | Mode usage comparison due to COVID-19 | Car, train, walking, cycling, bus/tram, other | Car, walk, bicycle | Bus/tram train | – | – |
[47] | Mode usage comparison due to COVID-19 | Car, motorcycle, public transport, bicycle or walk, other | Car | Public transport | – | – |
[39] | Mode usage comparison due to COVID-19 | Bicycle, bus, car, train, tram, walk, ferry, metro | Bicycle | Public transport (bus, tram, ferry, metro, train) | – | – |
[30] | Mode choice probability | Bus, metro, shared-transit, private car | – | – | Walk (carless respondents for both purposes); private car (car owner respondents for commute); walk (car owner respondents for entertainment/ shopping) | Ride-hailing/taxi (carless respondents for commute); private car (carless respondents for entertainment/shopping); ride-hailing/taxi (car owner respondents for both purposes) |
[2] | Mode usage comparison due to COVID-19 | Non-motorized transport, auto-rickshaw, taxi, ride-hailing, car, motorbike, ride-sharing, bus, railway | Personal vehicles (discretionary purposes) | On-demand private vehicles (for both commute and discretionary purposes) | – | – |
[40] | Mode usage comparison due to COVID-19 | Bicycle, motorcycle, walking, private car, public transport, special bus, taxi | Private car; walk | Public transport | – | – |
[34] | Modal shifts from public transport | Car, walk, cycle, motorcycle, others | – | – | From public transport to car | From public transport to motorcycle |
[72] | Mode usage comparison due to COVID-19 | Public transport, sharing bike or car, taxi, bike, e-bike, car, on foot | Private car | Public transport | – | – |
[22] | Comfortable perspective | Private car, walk/bicycle, train/light rail, bus, taxi/ride-hail, ferry | – | – | Private car | Bus |
[49] | Mode usage comparison due to COVID-19 | Car, train, bus/tram/metro, moped, bicycle, walk, others | Walk; car as driver; bicycle | Bus; train | – | – |
[48] | Risk perceptions | Personal vehicles, taxi, and ride-hailing, pooled ride-hailing, transit, shared bike, private bike, shared electric-scooter/moped, walk | – | – | Personal vehicles | Transit; taxi; ride-hailing services |
[27] | Mode usage comparison due to COVID-19 | Walk, cycle, road public transport, rail, private car, rideshare | Private car | Public transport (train, bus, BRT and minibus) | – | – |
[24] | Flow comparison with public transport | Public transport (metro, bus, commuter trains, trams), bike, walk, motor vehicles | – | – | Bike | – |
[32] | Mode usage comparison due to COVID-19 | Walk, bicycle, two-wheeler, car, taxi, auto, public transport, other | Walk; car | Taxi; auto-rickshaw; public transport | – | – |
[23] | Mode usage comparison due to COVID-19 | Walk, private transport (motorized), public transport, others | Private car | Public transport | – | – |
[11] | Mode usage comparison due to COVID-19 | Public transport, Private vehicle, paratransit, non-motorised vehicle, walk, others | Non-motorized vehicle | Public transport | – | – |
[21] | Mode usage comparison due to COVID-19 | Private car, taxi/ride-hailing, train, bus, ferry, walk/cycle | Private car Walk | Public transport | – | – |
Ref. . | Type of investigation . | Modes used in the study . | Largest increase in mode usage caused by the COVID-19 pandemic . | Largest decrease in mode usage caused by COVID-19 pandemic . | Modes most preferred during COVID-19 . | Modes least preferred during COVID-19 . |
---|---|---|---|---|---|---|
[41] | Mode usage comparison due to COVID-19 | Public transport, office transport, taxi/rickshaw, private car, motorcycle, bicycle, walking | Short distance (<5 km): private car (2% rise); bicycle (1% rise) | Short distance (<5 km): public transport (2% drop) | – | – |
All except bicycle and walking for negligible response | Long-distance (>5km): private car (3% rise); motorcycle (2% rise) | Long-distance (>5 km): public transport (5% drop) | – | – | ||
[74] | Mode usage comparison due to COVID-19 | Public transport, motorcycle, bike, car, walk | – | Bus (62.4% drop) Railways (30.7%) | – | – |
[51] | Mode usage comparison due to COVID-19 | Shared transport, unshared transport, and active travel mode | Car (80% of the original users); other modes (59%) (excluding public transport), car and original mode) | Public transport (23% of original users); shared cabs (10% of original users) | – | – |
[42] | Mode usage during COVID-19 | Car, public transit, semi-public transit, nonmotorized transit | Car (64%) | Public transport and walk (2.7%) | – | – |
[86] | Mode usage comparison due to COVID-19 | Subway and city bike ridership | – | Subway (90% decrease) | – | – |
[3] | Mode preference during COVID-19 | Bus, car, rickshaw, motorcycle, cycle, leguna, C.N.G.(CNG-run 3-wheelers locally known as the C.N.G.), walk | Walk | Bus | Walk | Cycle |
[53] | Mode usage comparison due to COVID-19 | Metro, ride-hailing, bus, motorcycle, auto and walking | – | Metro (55% decrease); ride-hailing (51% decrease); bus (45% decrease) | – | – |
[76] | Mode usage comparison of pre COVID-19 transit users | Transit, personal motor vehicle, cycling, and walking | – | – | Transit (18.2%); personal motor vehicle (13%) | – |
Mode usage comparison due to COVID-19 | Walking, cycling, personal motor vehicle, carpooling, public transit, and others | Personal motor vehicle | Carpooling or rideshare | – | – | |
[78] | Mode usage during COVID-19 | Public transit, ride-hailing, motorcycle, car and bicycle | Motorcycle (50%) | – | – | – |
[77] | Safety perception analysis on mode usage | Public transport. private vehicle, taxi, bicycle and walk | – | – | Private Vehicle | Public Transport |
[80] | Public transport usage during COVID-19 | Public transport | – | Only 9% of previous public transport users used | – | – |
[60] | Expected changes in mode usage during COVID-19 | Public transport, shared vehicles, walking and cycling | – | – | Walking and cycling | Public transport and shared vehicle |
[79] | Mode usage during COVID-19 | Public transport, private transport | – | Public transport across the countries assessed | – | – |
[81] | Mode usage during COVID-19 | Car, public transit, semi-public transit, non-motorized vehicles | Car | Public transit | ||
[85] | Mode usage comparison during COVID-19 | Personal vehicle, public transit, bicycle, taxi/ride share, walking | Walking, cycling and personal vehicle | Public transit | – | – |
[75] | Probability of self-infection using various modes | On foot/bicycle, taxi/taxi-hailing, private car, public transport | – | – | On foot/bicycle | Public transport taxi/taxi-hailing |
[84] | Mode usage of transit and non-transit riders during COVID-19 | Personal vehicles, biking, ride sharing, carpooling | Driving vehicles | Carpooling services | – | – |
[82] | Mode usage comparison during COVID-19 | Public transport, private vehicle, motorcycle, bike, walking and others | Private vehicles | Public transport, bike | – | – |
[83] | The shift of mode in work trips | Public transport, personal vehicle, ride-hailing service and non-motorized vehicle | Personal vehicles | Public transport | – | – |
[87] | Mode usage comparison during COVID-19 | Public transit, walk, private vehicle and bicycle | Private vehicles, bicycles | Public transit | – | – |
[28] | Mode preference during COVID-19 | Ride-hailing services, public transit services, organized ride-sharing services | – | – | Ride-hailing services | Organized ridesharing programs |
[25] | Travel satisfaction | Car, walk, or bike | – | – | Bike walk | – |
[26] | Mode usage comparison due to COVID-19 | Bike-sharing services | Bike-sharing services (for 43% of unemployed respondents) | Bike-sharing services (for 36% of employed respondents) | – | – |
[88] | Mode preference during COVID-19 | Solo modes, public transport | – | – | Solo modes | Public transport |
[43] | Mode usage comparison due to COVID-19 | Public transport, para-transit transport, car, two-wheelers, walking, shared transport | Car | Public transport | Two-wheelers, walking | Para-transit transport, shared transport |
[29] | Mode usage comparison due to COVID-19 | Bike, car, public transportation, walk long-distance train, remote bus, car-sharing, plane | Walk | Public transport | – | – |
[33] | Mode usage shifting due to COVID-19 | Active transport, NMT (non-motorized transport), private vehicles, shared vehicles, ride-hailing services | Active mode (walking and cycling); private mode (private car, motorcycle, and office cars) | Shared mode (bus, human hauler, and auto-rickshaw); ride-hailing services | – | – |
NMT | Shared mode (bus, human hauler, and auto-rickshaw); ride-hailing services | – | – | |||
[31] | Mode usage comparison due to COVID-19 | Car, ride-hailing, rickshaw, cng auto-rickshaw, bus, motorcycle, walk, bicycle | Walk | Bus, rickshaw, CNG auto-rickshaw | – | – |
[36] | Mode usage comparison due to COVID-19 | Car, walk, public transport, bicycle, other | Walk, bicycle | Public transport | – | – |
[35] | Mode usage comparison due to COVID-19 | Bicycle, public transport, car | Private car, bicycle | Public transport | – | – |
[17] | Mode usage comparison due to COVID-19 | Public transport, private car, office/campus transport, taxi, rickshaw, motorcycle, bicycle, walking | Walk, private car | Public transport | – | – |
[44] | Mode usage comparison due to COVID-19 | Car, train, walking, cycling, bus/tram, other | Car, walk, bicycle | Bus/tram train | – | – |
[47] | Mode usage comparison due to COVID-19 | Car, motorcycle, public transport, bicycle or walk, other | Car | Public transport | – | – |
[39] | Mode usage comparison due to COVID-19 | Bicycle, bus, car, train, tram, walk, ferry, metro | Bicycle | Public transport (bus, tram, ferry, metro, train) | – | – |
[30] | Mode choice probability | Bus, metro, shared-transit, private car | – | – | Walk (carless respondents for both purposes); private car (car owner respondents for commute); walk (car owner respondents for entertainment/ shopping) | Ride-hailing/taxi (carless respondents for commute); private car (carless respondents for entertainment/shopping); ride-hailing/taxi (car owner respondents for both purposes) |
[2] | Mode usage comparison due to COVID-19 | Non-motorized transport, auto-rickshaw, taxi, ride-hailing, car, motorbike, ride-sharing, bus, railway | Personal vehicles (discretionary purposes) | On-demand private vehicles (for both commute and discretionary purposes) | – | – |
[40] | Mode usage comparison due to COVID-19 | Bicycle, motorcycle, walking, private car, public transport, special bus, taxi | Private car; walk | Public transport | – | – |
[34] | Modal shifts from public transport | Car, walk, cycle, motorcycle, others | – | – | From public transport to car | From public transport to motorcycle |
[72] | Mode usage comparison due to COVID-19 | Public transport, sharing bike or car, taxi, bike, e-bike, car, on foot | Private car | Public transport | – | – |
[22] | Comfortable perspective | Private car, walk/bicycle, train/light rail, bus, taxi/ride-hail, ferry | – | – | Private car | Bus |
[49] | Mode usage comparison due to COVID-19 | Car, train, bus/tram/metro, moped, bicycle, walk, others | Walk; car as driver; bicycle | Bus; train | – | – |
[48] | Risk perceptions | Personal vehicles, taxi, and ride-hailing, pooled ride-hailing, transit, shared bike, private bike, shared electric-scooter/moped, walk | – | – | Personal vehicles | Transit; taxi; ride-hailing services |
[27] | Mode usage comparison due to COVID-19 | Walk, cycle, road public transport, rail, private car, rideshare | Private car | Public transport (train, bus, BRT and minibus) | – | – |
[24] | Flow comparison with public transport | Public transport (metro, bus, commuter trains, trams), bike, walk, motor vehicles | – | – | Bike | – |
[32] | Mode usage comparison due to COVID-19 | Walk, bicycle, two-wheeler, car, taxi, auto, public transport, other | Walk; car | Taxi; auto-rickshaw; public transport | – | – |
[23] | Mode usage comparison due to COVID-19 | Walk, private transport (motorized), public transport, others | Private car | Public transport | – | – |
[11] | Mode usage comparison due to COVID-19 | Public transport, Private vehicle, paratransit, non-motorised vehicle, walk, others | Non-motorized vehicle | Public transport | – | – |
[21] | Mode usage comparison due to COVID-19 | Private car, taxi/ride-hailing, train, bus, ferry, walk/cycle | Private car Walk | Public transport | – | – |