Travel Behaviour of Vulnerable Social Groups: Pre, during, and Post COVID-19 Pandemic
Abstract
:1. Introduction and Background
2. Materials and Methods
2.1. Search Strategy
- COVID-19 AND older people;
- COVID-19 AND people with disabilities;
- COVID-19 AND gender, female, women;
- COVID-19 AND low-income people.
2.2. Selection Strategy
3. Results
3.1. Older Adluts
3.2. People with Disabilities
3.3. Gender Gap
3.4. Low-Income People
4. Conclusions and Policy Recommandations
- Active Transport: There is a link between safe bike lane or bike lanes with good connectivity to amenities with the increase of cycling activities [61]. In order to satisfy the need in active transportation during and post-pandemic (when mobility restrictions are lifted), implementing new cycling lanes and widening the pedestrian roads will improve the mobility of VSGs who are interested in active transport, in particular older people and persons with disabilities that encountered problems in keeping (1.5–2 m) social distances. Local authorities and policymakers should prepare contingency plans and guidelines for future emergencies where other modes of transport lose their capacity and efficiency. In such situations, people from different age-groups and with different physical abilities should be able to have access to alternative active transport options. Neighborhoods and urban built environments should become more people-friendly with more compact, diverse, and mixed land uses to encourage people to switch to active modes of transport.
- Public Transport: It is undoubtedly true that PT was the most negatively affected as there is a higher concern of the infection risk. There are two different PT systems around the world: owned and operated by private companies and owned and operated by local authorities/municipalities. During such crisis, bus and rail operators due to the collapse in revenue from ticket sales faced significant financial difficulties. Government or local authorities should provide these companies with public funds to ensure accessible and affordable PT services for VSGs. Apparently, more research is required about efficient business models, particularly if based on PPPs and subsidies for mobility services. This study emphasizes the importance of shifting governments and policymakers’ mindset towards a pandemic-focused governance. This includes adopting a more resilient mindset that is prepared for future emergencies that require governments and transport providers to shift public transport users to other modes of transport (e.g., ride sharing and bike sharing services). Another policy implication would be the ability of public transport providers to shift from fixed-hour services to more flexible services to be able to accommodate the needs of users in case of emergency.
- Shared mobility: Results of this study indicated that one of the main reasons for customers’ intention to avoid shared mobility during the pandemic was related to perceived health threat. There has also been a change in key factors in customers’ transportation choices, shifting from traditional cost and convenience to health and safety related factors. As a result, ‘reducing the risk of infection’ is now the primary factor in people’s choice of transportation mode. Therefore, shared mobility modes can benefit VSGs with more accessible transportation services than private cars and in some cases PT by providing precautionary measures during the pandemic such as distributing disinfectants to help drivers to keep cars clean, installing protective plastic sheets, reducing their fares during the pandemic, and disinfecting all high contact surfaces on bikes and scooters in respective depots. All these will increase the running cost for these companies that should be covered by the government through incentives and tax exemptions. The effectiveness of such measures in changing VSGs’ attitudes (scared to be infected) towards shared mobility modes can be a potential research topic.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
# | Thematic Focus | Authors | Study Area | Year | People on Low Income | Females | Older People | People with Disabilities |
1 | Public transit; carpooling; ride-hailing and taxi; car- haling; micro mobility sharing; bike; walk; private car | Bert et al. | Worldwide | 2020 | - | - | - | |
2 | Commuting behaviour | Tirachini et al. | Chile | 2020 | - | - | - | |
3 | Distance | Ruiz-Euler et al. | US | 2020 | - | - | - | |
4 | Travel satisfaction | Khaddar & Fatmi | Canada | 2020 | - | - | - | |
5 | Transportation policies in low- and middle-income countries | Koehl | Worldwide | 2020 | - | - | - | |
6 | Vehicle ownership, mode share, willingness to buy a new vehicle | Ramit et al. | India | 2020 | - | - | - | |
7 | Mode share, trip motives | Shamshiripour et al. | US | 2020 | - | - | - | |
8 | Mode share | Meena | India | 2020 | - | - | - | |
9 | Mean radius of gyration | Hernando et al. | Spain | 2020 | - | - | - | |
10 | Impacts on trip purposes | Lou et al. | Worldwide | 2020 | - | - | - | |
11 | Travel demand | Circella | US | 2020 | - | - | - | |
12 | Travel demand | Jay et al. | US | 2020 | - | - | - | |
13 | Radii of gyration; travel distance; frequency of travel | Iio et al. | US | 2021 | - | - | - | |
14 | Travel behaviour: distance travelled; work and non-work-related travel frequency | Kar et al. | US | 2021 | - | - | - | |
15 | Ride-hailing | Matson et al. | US | 2021 | - | - | - | |
16 | Trip motives | Assoumou Ella, | Belgium | 2020 | - | - | - | |
17 | Trip purpose; mode choice; distance travelled; and frequency of trips before and during COVID-19. | Abdullah et al. | Worldwide | 2020 | - | - | - | |
18 | Changes in the share of travel modes | Shakibaei et al. | Turkey | 2020 | - | - | - | |
19 | Walking distance, daily time spent in common areas | Yamada et al. | Japan | 2020 | - | - | - | |
20 | Activity time | Rantanen et al. | Finland | 2020 | - | - | - | |
21 | Modal share changes during COVID | Ragland et al. | US | 2020 | - | - | - | |
22 | Social isolation | Pant & Subedi | Worldwide | 2020 | - | - | - | |
23 | Travel motives | Oliver et al. | Spain | 2020 | - | - | - | |
24 | Behavioural changes: mask wearing, PT avoidance, guest avoidance | Daoust | Worldwide | 2020 | - | - | - | |
25 | Ridership, distances travelled | Kabiri et al. | US | 2020 | - | - | - | |
26 | Ridership, distances travelled | Pullano et al. | France | 2020 | - | - | - | |
27 | Car; driving behaviour | Stavrinos et al. | US | 2020 | - | |||
28 | Outdoor activities; working from home; home education; share of travel mode; share of trip motives | de Haas et al. | Netherland | 2020 | - | - | - | |
29 | Private car; public transport; ride hailing/sharing; ferry; train; walk; bicycle; trip motives | Beck & Hensher | Australia | 2020 | - | |||
30 | life-space mobility; active aging; walk | Rantanen et al. | Finland | 2021 | - | - | - | |
31 | Well-being and travel behaviour | Ainslie | UK | 2020 | - | - | - | |
32 | Street time occupancy | Eskytė et al. | UK | 2020 | - | - | - | |
33 | COVID and access on transportation | Cochran | US | 2020 | - | - | - | |
34 | Daily home time and daily distance travelled | Beukenhorst et al. | US | 2020 | - | - | - | |
35 | Commuting for PwDs | Schur et al. | US | 2020 | - | - | - | |
36 | PT, Private car, walk, bicycle | Thombre & Agarwal | India | 2020 | - | - | ||
37 | Changes in travel characteristics, perceived risk of different modes, mode preference after the pandemic | Dandapat et al. | India | 2020 | - | - | ||
38 | Trip length and motives: | Bhaduri et al. | India | 2020 | - | - | ||
39 | Private car; public transit; paratransit, transportation network companies; non- emergency medical transportation; walk and bicycle | Chen et al. | US | 2020 | - | |||
40 | Telecommuting Rates During the Pandemic | Matson et al. | US | 2021 | - | - | ||
41 | Mobile phone data | Heiler et al. | Austria | 2020 | - | |||
42 | Distance; active days; modal share; trip motives | Molloy | Switzerland | 2020 | - | |||
43 | Public transport; car ownership | Eisenmann et al. | Germany | 2021 | - | |||
44 | Activity and travel patterns | Lee et al. | South Korea | 2021 | - | |||
45 | Traits and regulatory compliance during COVID lockdown; mobility behaviour; willingness to reduce outdoor mobility | Chan et al. | Worldwide | 2021 | - | |||
46 | Life-space mobility; Autonomy in participation outdoor physical activities; walk | Leppä et al. | Finland | 2021 | ||||
47 | Shared mobility services | Rahimi et al. | US | 2021 | ||||
48 | Work-and non-work-based trip patterns | Pawar et al. | India | 2021 | - | |||
49 | Social vulnerability and stay-at-home behaviour | Fu & Zhai | US | 2021 | ||||
50 | Travel behaviour patterns | Politis et al. | Greece | 2021 | - |
References
- World Health Organisation. “WHO Coronavirus” WHO Website, no. April. 2020, pp. 18–19. Available online: https://www.who.int/health-topics/coronavirus (accessed on 15 May 2021).
- WHO. “Tracking SARS-CoV-2 Variants,” World Health Organization. 2022. Available online: https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/ (accessed on 30 January 2022).
- Aloi, A.; Alonso, B.; Benavente, J.; Cordera, R.; Echániz, E.; González, F.; Ladisa, C.; Lezama-Romanelli, R.; López-Parra, Á.; Mazzei, V.; et al. Effects of the COVID-19 lockdown on urban mobility: Empirical evidence from the city of Santander (Spain). Sustainability 2020, 12, 3870. [Google Scholar] [CrossRef]
- Borkowski, P.; Jażdżewska-Gutta, M.; Szmelter-Jarosz, A. Lockdowned: Everyday mobility changes in response to COVID-19. J. Transp. Geogr. 2021, 90, 102906. [Google Scholar] [CrossRef] [PubMed]
- Matson, G.; McElroy, S.; Circella, G.; Lee, Y. Telecommuting Rates during the Pandemic Differ by Job Type, Income, and Gender. UC Davis Natl. Cent. Sustain. Transp. 2021. [Google Scholar] [CrossRef]
- Abdullah, M.; Dias, C.; Muley, D.; Shahin, M. Exploring the impacts of COVID-19 on travel behavior and mode preferences. Transp. Res. Interdiscip. Perspect. 2020, 8, 100255. [Google Scholar] [CrossRef] [PubMed]
- de Haas, M.; Faber, R.; Hamersma, M. How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transp. Res. Interdiscip. Perspect. 2020, 6, 100150. [Google Scholar] [CrossRef]
- Deloitte. Bicycle, e-bike, Motorcycle, and Electric Moped Sales in the Netherlands during COVID-19 in the Netherlands from March to June 2020 [Graph]; Statista: Hamburg, Germany, 2020. [Google Scholar]
- Dunne, D. Living Lab: Impacts of COVID-19 on Urban Mobility Measuers Impacts on Aberdeen. 2020. Available online: https://www.eltis.org/sites/default/files/umd2020_dunne_david.pdf (accessed on 15 August 2021).
- Shakibaei, S.; de Jong, G.C.; Alpkökin, P.; Rashidi, T.H. Impact of the COVID-19 pandemic on travel behavior in Istanbul: A panel data analysis. Sustain. Cities Soc. 2020, 65, 102619. [Google Scholar] [CrossRef] [PubMed]
- Thombre, A.; Agarwal, A. A Paradigm Shift in Urban Mobility: Policy Insights from Travel before and After COVID-19 to Seize the Opportunity. Transp. Policy 2020, 110, 335–353. [Google Scholar] [CrossRef]
- Molloy, J. MOBIS COVID-19 Mobility Report. In A Project of IVT, ETH Zürich and WWZ; Universität Basel: Basel, Switzerland, 2020. [Google Scholar]
- C2SMART. C2SMART COVID-19 Dashboard—C2SMART Home; C2SMART: New York, NY, USA, 2021. [Google Scholar]
- UITP. Public Transport Authorities and COVID-19; UITP: Brussels, Belgium, 2020; pp. 1–4. [Google Scholar]
- Beck, M.J.; Hensher, D.A. Insights into the impact of COVID-19 on household travel and activities in Australia—The early days under restrictions. Transp. Policy 2020, 96, 76–93. [Google Scholar] [CrossRef] [PubMed]
- Pawar, D.S.; Yadav, A.K.; Akolekar, N.; Velaga, N.R. Impact of physical distancing due to novel coronavirus (SARS-CoV-2) on daily travel for work during transition to lockdown. Transp. Res. Interdiscip. Perspect. 2020, 7, 100203. [Google Scholar] [CrossRef] [PubMed]
- Tirachini, A.; Cats, O. COVID-19 and public transportation: Current assessment, prospects, and research needs. J. Public Transp. 2020, 22, 1–34. [Google Scholar] [CrossRef]
- Tirachini, A.; Cats, O. On Virus Spreading Processes in Ride-Sharing Networks; ResearchGate: Amsterdam, The Neatherlands, 2020. [Google Scholar] [CrossRef]
- Yamada, Y.; Uchida, T.; Ogino, M.; Ikenoue, T.; Shiose, T.; Fukuma, S. Changes in Older People’s Activities during the Coronavirus Disease 2019 Pandemic in Japan. J. Am. Med. Dir. Assoc. 2020, 21, 1387–1388. [Google Scholar] [CrossRef]
- Daoust, J.-F. Elderly people and responses to COVID-19 in 27 Countries. PLoS ONE 2020, 15, e0235590. [Google Scholar] [CrossRef] [PubMed]
- Cochran, A.L. Impacts of COVID-19 on access to transportation for people with disabilities. Transp. Res. Interdiscip. Perspect. 2020, 8, 100263. [Google Scholar] [CrossRef]
- Eskytė, I.; Lawson, A.; Orchard, M.; Andrews, E. Out on the streets—Crisis, opportunity and disabled people in the era of COVID-19: Reflections from the UK. Alter 2020, 14, 329–336. [Google Scholar] [CrossRef] [PubMed]
- Dandapat, S.; Bhattacharyya, K.; Annam, S.K.; Saysardar, K.; Maitra, B. Impact of COVID-19 Outbreak on Travel Behaviour: Evidences from early stages of the Pandemic in India. SSRN Electron. J. 2020, 19. [Google Scholar] [CrossRef]
- Ella, G.A. Gender, Mobility, and COVID-19: The Case of Belgium. Fem. Econ. 2021, 27, 66–80. [Google Scholar] [CrossRef]
- Chen, K.L.; Brozen, M.; Rollman, J. Transportation Access to Health Care during the COVID-19 Pandemic: Trends and Implications for Significant Patient Populations and Health Care Needs. UC Off. Pres. Univ. Calif. Inst. Transp. Stud. 2020. [Google Scholar] [CrossRef]
- Heiler, G.; Hanbury, A.; Filzmoser, P. The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data. arXiv 2020, arXiv:2009.03798. [Google Scholar]
- Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ 2009, 339, 332–336. [Google Scholar] [CrossRef] [PubMed]
- Kabiri, A.; Darzi, A.; Zhou, W.; Sun, Q.; Zhang, L. How different age groups responded to the COVID-19 pandemic in terms of mobility behaviors: A case study of the United States. arXiv 2020, arXiv:2007.10436. [Google Scholar]
- Ragland, A.; Schor, M.; Felschundneff, M. The Impact of COVID-19 on the Mobility Needs of an Aging Population in Contra Costa County. UC Off. Pres. Univ. Calif. Inst. Transp. Stud. 2020. [Google Scholar] [CrossRef]
- Pullano, G.; Valdano, E.; Scarpa, N.; Rubrichi, S.; Colizza, V. Evaluating the impact of demographic, socioeconomic factors, and risk aversion on mobility during COVID-19 epidemic in France under lockdown: A population-based study. Lancet Digit. Health 2020, 2, e638–e649. [Google Scholar] [CrossRef]
- Oliver, N.; Barber, X.; Roomp, K.; Roomp, K. The Covid19Impact survey: Assessing the pulse of the COVID-19 Pandemic in Spain via 24 questions. arXiv 2020, arXiv:2004.01014. [Google Scholar]
- Pant, S.; Subedi, M. Impact of COVID-19 on the elderly. J. Patan Acad. Health Sci. 2020, 7, 32–38. [Google Scholar] [CrossRef]
- Stavrinos, D.; McManus, B.; Mrug, S.; He, H.; Gresham, B.; Albright, M.G.; Svancara, A.M.; Whittington, C.; Underhill, A.; White, D.M. Adolescent driving behavior before and during restrictions related to COVID-19. Accid. Anal. Prev. 2020, 144, 105686. [Google Scholar] [CrossRef]
- Leppä, H.; Karavirta, L.; Rantalainen, T.; Rantakokko, M.; Siltanen, S.; Portegijs, E.; Rantanen, T. Use of walking modifications, perceived walking difficulty and changes in outdoor mobility among community-dwelling older people during COVID-19 restrictions. Aging Clin. Exp. Res. 2021, 33, 2909–2916. [Google Scholar] [CrossRef]
- Rantanen, T.; Eronen, J.; Kauppinen, M.; Kokko, K.; Sanaslahti, S.; Kajan, N.; Portegijs, E. Life-Space Mobility and Active Aging as Factors Underlying Quality of Life among Older People before and during COVID-19 Lockdown in Finland—A Longitudinal Study. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2021, 76, E60–E67. [Google Scholar] [CrossRef]
- Eisenmann, C.; Nobis, C.; Kolarova, V.; Lenz, B.; Winkler, C. Transport mode use during the COVID-19 lockdown period in Germany: The car became more important, public transport lost ground. Transp. Policy 2021, 103, 60–67. [Google Scholar] [CrossRef]
- Chan, H.F.; Moon, J.W.; Savage, D.A.; Skali, A.; Torgler, B.; Whyte, S. Can Psychological Traits Explain Mobility Behavior During the COVID-19 Pandemic? Soc. Psychol. Personal. Sci. 2021, 12, 1018–1029. [Google Scholar] [CrossRef]
- Rahimi, E.; Shabanpour, R.; Shamshiripour, A.; Mohammadian, A. Perceived risk of using shared mobility services during the COVID-19 pandemic. Transp. Res. Part F Traffic Psychol. Behav. 2021, 81, 271–281. [Google Scholar] [CrossRef]
- Fu, X.; Zhai, W. Examining the spatial and temporal relationship between social vulnerability and stay-at-home behaviors in New York City during the COVID-19 pandemic. Sustain. Cities Soc. 2021, 67, 102757. [Google Scholar] [CrossRef] [PubMed]
- Lee, K.-S.; Eom, J.K.; Lee, J.; Ko, S. Analysis of the Activity and Travel Patterns of the Elderly Using Mobile Phone-Based Hourly Locational Trajectory Data: Case Study of Gangnam, Korea. Sustainability 2021, 13, 3025. [Google Scholar] [CrossRef]
- Ainslie, D. Coronavirus and the Social Impacts on Disabled People in Great Britain: May 2020; Office for National Statistics (ONS): London, UK, 2020; pp. 1–18.
- Beukenhorst, A.L.; Collins, E.; Burke, K.M.; Rahman, S.M.; Clapp, M.; Konanki, S.C.; Paganoni, S.; Miller, T.M.; Chan, J.; Onnela, J.-P. Smartphone data during the COVID-19 pandemic can quantify behavioral changes in people with ALS. Muscle Nerve 2020, 63, 258–262. [Google Scholar] [CrossRef] [PubMed]
- Schur, L.A.; Ameri, M.; Kruse, D. Telework after COVID: A ‘Silver Lining’ for Workers with Disabilities? J. Occup. Rehabil. 2020, 30, 521–536. [Google Scholar] [CrossRef] [PubMed]
- Bhaduri, E.; Manoj, B.S.; Wadud, Z.; Goswami, A.K.; Choudhury, C.F. Modelling the effects of COVID-19 on travel mode choice behaviour in India. Transp. Res. Interdiscip. Perspect. 2020, 8, 100273. [Google Scholar] [CrossRef]
- Politis, I.; Georgiadis, G.; Nikolaidou, A.; Kopsacheilis, A.; Fyrogenis, I.; Sdoukopoulos, A.; Verani, E.; Papadopoulos, E. Mapping travel behavior changes during the COVID-19 lock-down: A socioeconomic analysis in Greece. Eur. Transp. Res. Rev. 2021, 13, 21. [Google Scholar] [CrossRef]
- Dong, X. Linking TNC with passengers: Investigating TNC use among lower-income residents with limited access to cars. Travel Behav. Soc. 2022, 27, 184–191. [Google Scholar] [CrossRef]
- Ruiz-Euler, A.; Privitera, F.; Giuffrida, D.; Zara, I. Mobility Patterns and Income Distribution in Times of Crisis: U.S. Urban Centers during the COVID-19 Pandemic. SSRN Electron. J. 2020, 1–13. [Google Scholar] [CrossRef]
- Hernando, A.; Mateo, D.; Plastino, A. Social inequalities in human mobility during the Spanish lockdown and post-lockdown in the COVID-19 pandemic of 2020. medRxiv 2020, 1–7. [Google Scholar] [CrossRef]
- Ramit, R.; Nishant, S.; Shalini, S. Impact of COVID-19 on Urban Mobility in Indian Cities. Transp. Commun. Bull. Asia Pac. 2020, 90, 71–85. [Google Scholar]
- Matson, G.; McElroy, S.; Circella, G.; Lee, Y. Ridehailing Demand is Resilient Among Low-Income Travelers During the COVID-19 Pandemic. UC Davis Natl. Cent. Sustain. Transp. 2021. [Google Scholar] [CrossRef]
- Koehl, A. Urban transport and COVID-19: Challenges and prospects in low- and middle-income countries. Cities Health 2021, 5 (Suppl. S1), S185–S190. [Google Scholar] [CrossRef]
- Bert, J.; Schellong, D.; Hagenmaier, M.; Hornstein, D.; Wegscheider, A.K.; Palme, T. How COVID-19 Will Shape Urban Mobility; BCG: Boston, MA, USA, 2020. [Google Scholar]
- Tirachini, A.; Guevara, A.; Munizaga, M.; Carrasco, J.A.; Hurtubia, R. Using Disaggregated and Latent Variable Analysis to Investigate the Role of Income and Other Socioeconomic Factors in the Concerns about the COVID-19 Pandemic. SSRN Electron. J. 2020, 1–13. [Google Scholar] [CrossRef]
- Lou, J.; Shen, X.; Niemeier, D. Are stay-at-home orders more difficult to follow for low-income groups? J. Transp. Geogr. 2020, 89, 102894. [Google Scholar] [CrossRef] [PubMed]
- Pawar, D.S.; Yadav, A.K.; Choudhary, P.; Velaga, N.R. Modelling work- and non-work-based trip patterns during transition to lockdown period of COVID-19 pandemic in India. Travel Behav. Soc. 2021, 24, 46–56. [Google Scholar] [CrossRef]
- Iio, K.; Guo, X.; Kong, X.; Rees, K.; Wang, X.B. COVID-19 and social distancing: Disparities in mobility adaptation between income groups. Transp. Res. Interdiscip. Perspect. 2021, 10, 100333. [Google Scholar] [CrossRef] [PubMed]
- DfT. National Travel Attitudes Study (NTAS)—Wave 4 (Final); Department for Transport: London, UK, 2021.
- di Ciommo, F.; Shiftan, Y. Transport equity analysis. Transp. Rev. 2017, 37, 139–151. [Google Scholar] [CrossRef]
- AAPD. Equity in Transportation for People with Disabilities; AAPD: Washington, DC, USA, 2016. [Google Scholar]
- Akyelken, N. Transport for women: Who decides what women need? Transp. Rev. 2020, 40, 687–688. [Google Scholar] [CrossRef]
- Hong, J.; McArthur, D.; Stewart, J.L. Can providing safe cycling infrastructure encourage people to cycle more when it rains? The use of crowdsourced cycling data (Strava). Transp. Res. Part A 2020, 133, 109–121. [Google Scholar] [CrossRef]
Study | Country | Mode | Main Findings |
---|---|---|---|
Beck & Hensher, 2020 [15] | Australia | Car | Older households made significantly less trips than younger households before and during the pandemic. Before the pandemic, older people were less concerned about the hygiene on public transit, but during the pandemic became as concerned as the other age groups. Older people were more likely to decrease the use of a car during the pandemic. |
Daoust, 2020 [20] | Worldwide (27 countries) | - | Older people were more likely to avoid crowded places (e.g., public transport, gatherings), but were less compliant to wear a mask (degree of compliance for 20 year-old person is 0.6, whereas degree of compliance for 80 year-old person is only 0.3) and were not significantly more likely to self-isolate than other age groups despite their vulnerability to the virus. |
de Haas et al., 2020 [7] | Netherlands | PT, car, bicycle and walk | The majority of older people (χ2 = 95.2 (1, N = 24920), p = 0.001) were much less active than before COVID-19 compared to other age groups for activities such as grocery shopping, shopping, exercising, and physical meetings. Sample size: 2500 respondents from the Netherlands Mobility Panel (MPN) |
Heiler et al., 2020 [26] | Austria | - | Older people were less compliant to mobility restriction than the other age groups despite their vulnerability to COVID-19. |
Kabiri et al., 2020 [28] | US | - | Older people were quick in accepting the stay-at-home measure, changing their behavior and practicing social distancing compared to other generations. |
Oliver et al., 2020 [31] | Spain | - | Older people were more likely to stay at home (14.9%) compared to younger generations (7.6%). |
Pant & Subedi, 2020 [32] | US | - | COVID precaution measures such as the stay-at-home measure increased the social isolation for all age groups, in particular older people. As a result, they could not meet their relatives and friends. |
Pullano et al., 2020 [30] | France | - | Older people almost stopped taking trips longer than 100 km and were likely to avoid leisure activities and family trips. |
Ragland et al., 2020 [29] | US | PT, car, ridesharing, special transportation service | In California between 2018 and 2020, for the age group 55 years and older, PT use decreased by 28.3%, special transportation services use increased by 2.9%, and ridesharing (only +65 years old) increased by slightly more than 10%. A small percentage of older people (3.7%) had a person to drive them to work before COVID-19 and this practice was no longer used in 2020. Older people changed home-to-work transport mode; a shift was mainly toward private cars (87.1% to 93.7%). |
Yamada et al., 2020 [19] | Japan | Walk | In Japan due to COVID restrictions from 1 January to 25 May 2020, daily time spent in common areas and walking distance in care retirement communities decreased from 94 min/day to less than 80 min/day and 1300 m/day to approximately 900 m/day, respectively. |
Stavrinos et al., 2020 [33] | US | Car | Post-COVID, both vehicle miles driven and driving days per week decreased by 35% and 37%, respectively. However, older adolescents, employed adolescents, and ethnic minorities were less likely to decrease their driving during the COVID-19 restriction period. |
Leppä et al., 2021 [34] | Finland | Walk | During social distancing, older respondents with no walking difficulties were able to partly compensate for their decreased social life activities and interactions by increasing their physical activities (5.5 min/day, SD 25.1). They also faced less steep decline in their life-space mobility compared to those older respondents with walking difficulties. |
Rantanen et al., 2021 [35] | Finland | Walk | During the COVID-19 outbreak, older people’s active aging scores (age and sex adjusted within subject B −24.1, SE 0.88, p < 0.001; η2 for time 0.508), as well as their life-space mobility score (B −10.8, SE 0.75, p < 0.001; η2 0.193, correspondingly), faced a steep decrease compared to the data from two years prior to the pandemic. |
Eisenmann et al., 2021 [36] | Germany | Car ownership | Younger people had higher tendency to miss having a car of their own compared to older respondents. These respondents were mainly women between the age of 18 and 44 who used public transport as their main means of transport during the lockdown restrictions and perceived inconvenience with the use of public transport. |
Chan et al., 2021 [37] | Worldwide (31 countries) | - | The age of respondents was found to be influential in their compliance to reduce their mobility and stay at home. Both older and younger respondents (compared to middle-aged [30–60 years old]) were more likely to stay at home during lockdown restrictions. |
Rahimi et al., 2021 [38] | US | shared mobility | Concerning age, older respondents perceived a higher risk than younger respondents regarding using shared mobility services. |
Pawar et al., 2021 [16] | India | - | Age was found to be a critical factor affecting the travel frequency of work-based trips, where younger commuters were found to be more likely to shift to no travel during the transition to lockdown restrictions. The analysis indicated that for each year increase in the age of travelers, their probability of no travel during the travel restriction would decrease by 2 percent. |
Fu and Zhai, 2021 [39] | US | - | Due to their dependence on the assistance of others, older respondents (aged 65 and older) generally had less compliance with social distancing and stay-at-home behaviors. |
Lee et al., 2021 [40] | South Korea | - | The average non-home trips and activities for the older people was higher compared to non-older people, whereas the average non-home activity time per person for the older people was about 2 h and 10 min shorter. Furthermore, on average, the older people had slightly higher number of trips compared to non-older people (4.90 trips/person and 4.74 trips/person, respectively). People aged over 80 spent the longest time at home (average of 16.74 h) compared to people in their 30s that stayed at home for the shortest amount of time (average of 13.24 h). |
Study | Country | Mode | Disability Type | Main Findings |
---|---|---|---|---|
Ainslie, 2020 [41] | UK | - | Mental impairments, hearing impairments, mobility impairments. | PwDs were more likely to leave their home for medical purposes and to provide help to a vulnerable person than the rest of the population (19% against 7%). However, they were less likely to leave their home for leisure, to commute, to take the children to school, to grocery shop, to exercise, or to meet up with people. In the data collected in May 2020, 73.4% of PwDs left their home against 92.5% of non-disabled persons to do their regular activities. Among PwDs, 80% of persons with mental impairments left their home against 54.7% for persons with hearing impairments and 65.9% of persons with mobility impairments. |
Beukenhorst et al., 2020 [42] | US | - | Amyotrophic lateral sclerosis, a kind of mobility impairment | During the COVID-19 pandemic, the median time spent at home for amyotrophic lateral sclerosis (ALS) people increased from 19.4 h to almost 23.7 h, and the median daily distance travelled dropped from 42 km to 3.7 km. For general population in the US, daily time spent home increased from 10 to 14 h. |
Chen et al., 2020 [25] | US | Paratransit | - | Many PwDs rely on paratransit, such as a minibus (or van) equipped with wheelchair lifts or ramps to facilitate access. Paratransit use dropped during the beginning of the pandemic by around 80% but recovered to 50% of the normal service in late July 2020. A total of 47% of 2000 PwDs who participated in the survey rely on personal care, in which 27% of them (216 persons) stopped receiving those services during the outbreak. |
Cochran, 2020 [21] | US | PT and ride hailing (Uber, etc.) | Visual impairments: blind or low visibility, hearing impairments; other disabilities | The pandemic aggravated the difficulties of PwDs to access PT and created more reluctance to use them. PwDs experienced less assistance in accessing PT and completing daily living activities than prior to the COVID-19 pandemic. PwDs were worried about being infected from people (drivers/passengers) or surfaces (transportation facilities/vehicles), e.g., blind persons should touch surfaces for navigation while using PT or ride haling services. Blind persons could neither get accurate and timely information about transport service, such as whether or not it was operating, nor up to date information about spread of COVID-19 in their region. Then, it was difficult for them to evaluate the risk of travelling by PT or ride hailing services. |
Eskytė et al., 2020 [22] | UK | Walk | All forms of impairments |
The 2-m physical distancing while walking was difficult to follow for persons with visual/mobility/cognitive impairments or neurodiversity in non-disability-inclusive sidewalks. |
RIDC, 2020a, 2020b | UK | PT | - | Travel by PT was dropped significantly for most PWDs (64% of respondents) due to safety concern, a lack of trust with the information provided by the government, and a heightened feeling of vulnerability to COVID-19. In addition, 50% of respondents were no receiving health, personal care, as well as shopping assistant during the pandemic. |
Schur et al., 2020 [43] | US | - | - | Tele-working due to the COVID-19 pandemic positively influenced the employment opportunities for people with disabilities, but there is still a wage gap between non-disabled and disabled people. Increased availability of home-based work in the future can create more employment opportunities for people with disabilities. |
Leppä et al., 2021 [34] | Finland | Walk | - | Life-space mobility for older respondents with impaired walking decreased significantly, putting them at risk of being housebound (p = 0.001). Furthermore, respondents with impaired walking had a smaller decrease in autonomy of participating in outdoor activities (p = 0.017) and slighter increase in their physical activity (p < 0.001) compared to those with intact walking ability. |
Rahimi et al., 2021 [38] | U.S. | shared mobility | - | Respondents’ health background, such as their pre-existing health conditions and disability status, significantly influenced their risk perception associated with the usage of public transport. |
Fu and Zhai, 2021 [39] | U.S. | - | - | At the beginning of lockdown restrictions, people with disability were mainly staying at home due to their special needs and reliance on assistance of others. However, throughout the lockdown period and with the growth of the pandemic situation, disabled people generally decreased their social distancing and stay-at-home behaviors as they needed to take care of themselves or were dependent on support from other community members. |
Study | Country | Mode | Main Findings |
---|---|---|---|
Abdullah et al., 2020 [6] | Worldwide | - | Pre-COVID pandemic, mode choice for primary trips purposes were similar for females and males. Males used private transport modes at a higher rate and undertook more and longer trips during COVID-19 (+3.9% and +1%) compared to females (−9% and −2%), whereas females were not likely to change their mode choice. Females might be more concerned about being infected during the pandemic. |
Assoum ou et al., 2020 [24] | Belgium | PT | Considering jobs (caregiving, primary and pre-primary education, housework, and domestic work) held by females among the working-age (20–59 years old) population in Belgium, females were more vulnerable to be infected during lockdown as they had frontline jobs and their main transport mode was PT. The female/male COVID cases index confirmed this vulnerability. |
Bhaduri et al., 2020 [44] | India | - | Females’ work and discretionary activities decreased more than males during the COVID-19 pandemic (a decrease of 17% of work activities compared to 9% for males and decrease of 34% of females’ discretionary activities against 28% for males). The shift to home-based work was slightly higher for females (+19%) than males (+16%). Females have less tendency to use a car to commute (because of less driver’s license ownership compared to males) but had more tendency to work from home and to use other means of transport. |
Beck & Hensher, 2020 [15] | Australia | Car and PT | Females’ concern about levels of hygiene on PT was similar before and after COVID-19. Females’ activities such as shopping, visiting relatives and friends, and healthcare appointments have been interrupted due to COVID-19. |
Chen et al., 2020 [25] | US | - | During the pandemic, pregnant females were less willing to travel outside their home for prenatal care (usually not amenable to telemedicine). Females had disproportionately more childcare obligation and were more impacted than males by school closures. |
Matson et al., 2021 [5] | US | - | The attitude toward tele-working is different between females depending on the presence of children in the household. Working mothers stated there are unwanted distractions while tele-working. |
Dandap at et al., 2020 [23] | India | - | Males had less tendency to work from home than females. |
Heiler et al., 2020 [26] | Austria | - | Mobility behaviour of the male worker population changed more significantly than females, possibly explained by the obligation of home office work. |
Molloy, 2020 [12] | Switzerland | - | During lockdown, males travelled longer distances. Average daily distances travelled had a more consequent drop for females (from 38 km prior COVID-19 to 12.5 km at the beginning of the lockdown, recovered at 37 km in late August 2020) than for males (46 km, 18 km, and 46 km, respectively). |
Shakibaei et al., 2020 [10] | Turkey | Rail transit and Car | Females were used to rail transit more than males pre-, during, and post-lockdown. However, females’ travel behaviour changed during the outbreak. This shows rail transit was more reliable and secure for females compared to road transportation. An increase in females’ car use to commute (home to work trips) was observed, but still they used a car less than males, and grocery shopped less than males. |
Thombre & Agarwal, 2020 [11] | India | - | In India pre-lockdown, PT and walking were the most preferred modes among females. After the outbreak, the share of PT decreased (27% to 22%), the share of walking travels slightly increased (27% to 29%), and the share of motorized vehicle significantly increased (18% to 25% in the five biggest megacities). After the lockdown, non-motorized transports (NMT) and intermediate public transport (IPT) decreased (24% to 15%). The findings highlight the importance of PT, NMT, and IPT modes to ensure gender equity. |
Eisenma nn et al., 2021 [36] | Germany | Bicycle/car ownership/PT | Bicycle usage decreased more sharply for males (minus 10 percentage points) than for females (minus 5 percentage points). Public transport usage dropped more significantly amongst males (from 22% to 10%) than amongst females (from 24% to 15%). The regression model indicates that females were more likely than males to miss having a car of their own. |
Chan et al., 2021 [37] | Worldwide | - | Concerning gender, women (compared to men) were more compliant and cooperative to stay at home previously (b ¼ 0.037, SE ¼ 0.015) and continue to stay at home in the future (OR ¼ 0.79, SE ¼ 0.044). They were also more likely to reduce their mobility during lockdown restrictions. |
Rahimi et al., 2021 [38] | US | shared mobility | Gender of respondents had a significant role on their perceived risk of using shared mobility services during the pandemic. Females perceived higher risks of using shared mobility modes. |
Lee et al., 2021 [40] | South Korea | - | In terms of activity behaviors by gender, women, regardless of their age group, had longer duration of home activity time than men. For instance, the average home activity time for both non-older and older women (14.51 h and 16.76 h, respectively) is longer than those of non-older and older men (12.68 h and 14.83 h, respectively). The average home activity times were 12.68 h for non-older men, 14.51 h for non-older women, 14.83 h for older men, and 16.76 h for older women. On average, men tend to participate in more non-home activities per day compared to women. On average, women have shorter non-home activities (9.41 h) compared to men (11.20 h). |
Politis et al., 2021 [45] | Greece | - | In terms of both travel duration and trip frequencies, men tended to make longer and more trips during lockdown restrictions compared to their women counterparts. Men had a hazard ratio of 0.90, which indicated that the duration of travel for male travelers was somewhat longer compared to female travelers. In general, trips made by walking and cycling were likely to be shorter than the trips made by cars (hazard ratios of 1.49 and 1.94, respectively). Moreover, trips made by public transport were more likely to have a significantly longer duration compared to cars (hazard ratio of 0.56). |
Study | Country | Mode | Main Findings |
---|---|---|---|
Bert et al., 2020 [52] | Worldwide (China, EU, US) | Privat e car, | In post-lockdown, MIP (middle-income population) were slightly more willing to buy a car compared to LIP in the U.S., while in the EU, all income groups had similar likelihood to buy a new car post-lockdown. In China, LIP were less likely to buy a new car post-lockdown compared to MIP and HIP. |
Beck & Hensher, 2020 [15] | Australia | Car and PT | Pre-COVID, LIP made significantly less trips per week compared to other income groups, while post COVID, there was no difference between income groups in terms of the number of trips. As most of the LIP were less likely to own a car, they have shown significantly lower average car use reduction compared to HIP. LIP were less likely to do work from home compared to MIP and HIP. As expected, LIP were less likely to show reduction in some activities such as going to restaurants, cafés, pubs or bars, gyms or exercise, watching professional sport, playing organised sports, or work functions. |
Bhaduri et al., 2020 [44] | India | - | In terms of working habits during the pandemic, LIP reduced working much more than HIP (−29% compared to −1%), possibly due to their lower tendency to telecommute and their lower rate of car ownership. HIP were more likely to shift to work from home than LIP (+20% and +11%, respectively). |
Dandapat et al., 2020 [23] | India | PT | Mostly being from low-income groups, captive riders to PT are more likely to use PT during the pandemic despite their concern about the risk of infection. |
Hernando et al., 2020 [48] | Spain | - | Means of the daily radius of gyration collected using mobile phone data has been used as a measure to evaluate the mobility inequality across the Spanish population:
inequality sharply increased from 17% to 47% due to teleworking in new normal (post- lockdown). |
Koehl, 2020 [51] | UK | - | Increasing the share of active transportation (cycling and walking), as also suggested by the WHO during the COVID-19 pandemic, can decrease the pressure on the often-overloaded PT systems which is the most used transport modes in LIP and MIP countries. |
Ramit et al., 2020 [49] | India | All modes | Only a 23% shift was expected for the intra-city urban rail used in Mumbai and Chennai post-lockdown as LIP (income < 25,000 INR) who do not own a vehicle were the highest portion among PT users. Regardless the income, only 24% of respondents were more likely to buy a new vehicle post lockdown. Among them, LIP and MIP (25,000 < income < 50,000 INR) were most likely to by a two-wheeler. |
Ruiz-euler et al., 2020 [47] | US | - | Lockdown policies increased the mobility gap (differences in mobility across income levels) and inequality in urban centres of American cities. LIP were unable to reduce mobility (distance travelled) as much as high-income groups during the outbreak. |
Thombre & Agarwal, 2020 [11] | India | All modes | Before the lockdown, the preferred modes of LIP for primary activities in megacities were PT, walking, and motorized two-wheeler, respectively. In post-lockdown, PT and walking trips sharply decreased among LIP, and they made a shift to motorized transports for LIP, possibly explained by a higher preference to safety than affordability. |
Tirachini et al., 2020 [53] | Chile | - | Low-income workers were less (1 out of 4 workers) able to work from home. |
Lou et al., 2020 [54] | Worldwide | - | The “stay-at-home” measure has less effect on LIP’s mobility than higher income groups’ mobility. Work and non-work-related trips were less reduced for LIP as more essential jobs were held by LIP as they could not afford online shopping or do tele-working. This difference in mobility is less significant in sparsely populated regions. |
Pawar et al., 2021 [55] | India | - | Higher income groups were less likely (approximately 14–25% reduction in chances of having reduced travel) to switch to no travel compared to LIP. All income groups were likely to significantly reduce their non-work-based trips, but higher income groups were more likely to travel regularly for non-work-related purpose than LIP. |
Iio et al., 2021 [56] | US | - | Before COVID restrictions, the distance travelled by income groups were similar. During the pandemic (in April 2020), the median monthly distance travelled by high-income groups had a large decrease compared to LIP. The radius of gyration and the number of locations visited dropped in a larger proportion for higher income groups than that for LIP. |
Matson et al., 2021a [5] | US | - | LIP are less likely to work from home and benefit from the ensuing travel time savings; therefore, a long-term shift toward tele-working may increase the current mobility inequities. |
Matson et al., 2021b [50] | US | Ride-hailing | The use of ride hailing services for LIP does not change pre- and during the COVID outbreak but the change for HIP and MIP was obvious. |
DfT, 2021 [57] | UK | - | LIP travelled less than high-and middle-income respondents, while high-income groups had similar travel pattern such as before the pandemic. |
Rahimi et al., 2021 [38] | US | shared mobility | People’s income was found to play a critical role on their risk-perception behavior associated with shared mobility services. According to the result, those respondents from extremely low-income background (with less than $20 K income per year) perceived higher risks of exposure to COVID-19 associated with the use of public transport modes. |
Pawar et al., 2021 [55] | India | - | In terms of the effect of travelers’ income on their work-and non-work-related travel frequency, travelers from higher-income brackets (3 to 6 lakh rupees or 6 to 12 lakh rupees) were significantly less likely to opt to no travel during the transition to lockdown period compared to lower income travelers (up to 3 lakh rupees). The findings revealed that the chances of switching to ‘no travel’ decreased by 61% and 45% for travelers with income of 6–12 lakhs and 3–6 lakhs, respectively, compared to those with income less than 3 lakh rupees (1 lakh = 0.1 million). |
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Dadashzadeh, N.; Larimian, T.; Levifve, U.; Marsetič, R. Travel Behaviour of Vulnerable Social Groups: Pre, during, and Post COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2022, 19, 10065. https://doi.org/10.3390/ijerph191610065
Dadashzadeh N, Larimian T, Levifve U, Marsetič R. Travel Behaviour of Vulnerable Social Groups: Pre, during, and Post COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 2022; 19(16):10065. https://doi.org/10.3390/ijerph191610065
Chicago/Turabian StyleDadashzadeh, Nima, Taimaz Larimian, Ulysse Levifve, and Rok Marsetič. 2022. "Travel Behaviour of Vulnerable Social Groups: Pre, during, and Post COVID-19 Pandemic" International Journal of Environmental Research and Public Health 19, no. 16: 10065. https://doi.org/10.3390/ijerph191610065
APA StyleDadashzadeh, N., Larimian, T., Levifve, U., & Marsetič, R. (2022). Travel Behaviour of Vulnerable Social Groups: Pre, during, and Post COVID-19 Pandemic. International Journal of Environmental Research and Public Health, 19(16), 10065. https://doi.org/10.3390/ijerph191610065