Exploring long term travel behavior changes due to the COVID-19 pandemic and its regional implications on urban mobility

Introduction

As a response to the COVID-19 pandemic, governments worldwide have taken radical measures to mitigate the spread of the virus, including policies regarding social distancing, protective masks, and the temporary closing of societal sectors such as schools and workplaces [1]. These policy measures in combination with subjective perceptions of the virus such as the fear of contamination have influenced activity and travel patterns in an unprecedented way [2]. For example, after the first confirmed case of COVID-19 in the Netherlands and the initiation of the lockdown, travel movements by car decreased by 50%, public transportation experienced 87% less travelers and flight movements went down by 90% [3]. Even though the COVID-19 vaccines and vaccination strategies in place suggest that within the near future the socioeconomic impacts of the pandemic may be largely contained, survey results indicate lasting changes in lifestyles and travel behavior even after the COVID-19 pandemic can be considered over. For example, a large group of people (45%) expect to work from home more often post-COVID-19 and some people (20%) expect to cycle and walk more after the COVID-19 pandemic [4]. This raises the question which long term travel behavior changes might be expected post-COVID-19, and to which magnitude these travel behavior changes will affect urban transportation.

Potential long term travel behavior changes due to the COVID-19 pandemic

The way people travel is grasped by the theory of travel behavior, which refers to the choices individual travelers make regarding mode, route, departure time, destination, and so on [5]. Travel behavior is not static but can change due to changes within economical, psychological and geographical factors as conceptualized in different travel behavior change theories [6]. These theories allow to conceptualize the direct effects of the COVID-19 pandemic and policy measures on travel and activity patterns, from which three main travel behavior changes can be identified. These include (1) a shift from onsite to online work-related activities, (2) the retiming and respacing of work-related travel patterns and (3) a modal shift towards active modes such as cycling and walking [7].

1. A shift from onsite to online work-related activities

The first main travel behavior change to be expected post-COVID-19 is a shift from onsite to online work related activities, based on (1) increased attitudes towards online activities, (2) increased utility of online activities and (3) the development of new habitual behavior that includes online activities [7]. Due to the COVID-19 pandemic and associated policy measures, working from home became the new norm. The majority (54%) of employers stimulated to work from home as much as possible, more than half (54%) of the working population worked partly from home and a slightly smaller group (39%) worked almost completely from home during the COVID-19 pandemic, which was 6% before the pandemic [4], [8]. By working from home more often, the knowledge and experiences with online tools arguably increases, and in turn increases the utility of working from home. People indicate that working from home gets easier when compared to the start of the pandemic and the vast majority (71%) of people have positive experiences with working from home [8]. Considering the duration of the pandemic (>18 months at the time of writing) it is very plausible that habitual behaviors such as the daily commute to work have been broken and new habitual behavior has formed which includes working from home.

2. The respacing and retiming of travel patterns

The second main travel behavior change to be expected post-COVID-19 is the respacing and retiming of travel patterns due to increased flexibility [7]. If a shift from onsite to online work related activities may be expected after the COVID-19 pandemic, the respacing and retiming of associated work related travel patterns is evident. By working from home, time limitations disappear which results in increased flexibility. For example, a large group of people (45%) expect to work from home more, from which the majority (60%) expects to work from home for two or three days in the week when the COVID-19 pandemic is over [8]. By only traveling to the office two or three days in the week, the reduced travel times allow to participate in other activities which are further away, or even to live further away from the office which will lead to the respacing of travel patterns [6]. Moreover, if people choose to work from home partly during the day to avoid rush hours, a retiming of travel patterns might be expected towards the rest of day [6].

3. A modal shift towards active modes such as cycling and walking

The third main travel behavior change that may be expected post-COVID-19 is a modal shift towards active modes such as cycling and walking, based on (1) increased attitudes towards cycling, (2) increased utility of cycling and (3) the development of new habitual behavior that includes cycling [7].  Utilities and attitudes not only play an important role when deciding upon where and when to undertake activities, but also influences mode choice [9]. During the COVID-19 pandemic both the sales of e-bikes, the share of cyclists and positive experiences with cycling increased [4], [10]. These are slight indicators that argue for increased utility of cycling due to increased knowledge and experiences, increased attitudes towards cycling as mode of transport and perhaps the creation of new habitual behavior which includes cycling.

Potential future development paths of post-COVID-19 travel behavior

With the identification of three main travel behavior changes to be expected post-COVID-19, the follow-up question is to which extend these travel behavior changes may be expected, and to what magnitude this post-COVID-19 travel behavior will affect a urban transportation system. Based on exploratory scenario planning methodology, potential future development paths of post-COVID-19 travel behavior show to be dependent on the future developments of five critical uncertainties: (1) the valuation of active modes of transportation through personal drivers of travelers, (2) travelers’ attitude towards online activities, (3) technological developments to enable the substitution of onsite for online activities, (4) policy of employers with regards to flexibility in both working location and office hours as well as supporting working-from-home and (5) governmental policies [7].

If in the future, travelers’ attitude shift towards predominantly online activities in stead of onsite, technology improves to support online activities for professions where it is currently not possible, and policies of employers are highly flexible in both time and space (i.e. flexible office hours and working location) a strong shift from onsite to online work-related activities may be expected and thus also the strong respacing and retiming of work related travel patterns [7]. If personal drivers shift towards a higher valuation of active modes, and policy of the government is progressive with a high interference on mobility a strong modal shift towards active modes such as cycling and walking may be expected [7].

‍Potential implications of post-COVID-19 travel behavior scenarios on the urban transportation system of Amsterdam

Analysis of four distinct post-COVID-19 travel behavior scenarios within a tour based travel model of the city of Amsterdam show that, if a shift from onsite to online activities may be expected post-COVID-19, the reduced work-related activities and its consequential changes to work-related travel patterns causes a modal shift away from public transportation and towards cars (+1.5% share in modal split) and walking (+5.4% share in modal split). Scenarios which, besides the shift from onsite to online also include higher attractiveness of cycling as mode of transport, show a modal shift away from public transportation towards cycling. From these results it can be concluded that, given different states of the aforementioned five critical uncertainties, public transportation loses share (ranging from -4.2% to -17.3%) in the modal split. The retiming of trip patterns is apparent in all four post-COVID-19 scenarios, as results indicate trips during the morning and evening rush hours are reduced (by respectively -2.5% to -6.1% and -1.5% to 3.7%) whereas the trips during the rest-of-day increased (by 0.9% to 2.1%) [7].

The implications of the shift from onsite to online work-related activities, the respacing and retiming of travel patterns and a modal shift towards cycling show to strongly alleviate congestion rates during the morning and evening rush hours as depicted in figures 1 to 4. Moreover, travel times for trips within the city are reduced by 1 to 2 minutes when traveling from the city center to certain zones within the city.

‍This insight provides an abbreviated version of Leonard Oirbans’ master thesis which explores post-COVID-19 travel behavior and its implications on urban mobility, the complete thesis (and corresponding information on the scenario’s and other analysis) is publicly available on the TU Delft repository: http://resolver.tudelft.nl/uuid:1def0bee-7912-4d8a-9bc7-af1d7c8375c3

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References

[1] M. Roser, H. Ritchie, E. Ortiz-Ospina, and J. Hasell, “Coronavirus Pandamic (COVID-19),” Our World Data, 2020.

[2] M. de Haas, R. Faber, and M. Hamersma, “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., vol. 6, p. 100150, Jul. 2020.

[3] Kennisinstituut voor Mobiliteitsbeleid (KiM), “Kerncijfers Mobiliteit 2020,” 2020.

[4] M. de Haas, R. Faber, and M. Hamersma, “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., vol. 6, p. 100150, Jul. 2020.

[5] M. Li, M. Zou, and H. Li, “Urban Travel Behavior Study Based on Data Fusion Model,” in Data-Driven Solutions to Transportation Problems, Elsevier, 2019, pp. 111–135.

[6] B. van Wee, “Langetermijneffecten mobiliteit? Een discussie.,” Tijdschrift Vervoerswetenschap 56, (4), pp. 13–21, Dec-2020.

[7] L. Oirbans, “Imagining a post-COVID world: Exploring long term travel behavior changes due to the COVID-19 pandemic and its regional implications on urban mobility.,” Delft University of Technology, 2021.

[8] M. de Haas, M. Hamersma, and R. Faber, “Nieuwe inzichten mobiliteit en de coronacrisis,” 2020.

[9] B. Verplanken, H. Aarts, A. Knippenberg, and C. Knippenberg, “Attitude Versus General Habit: Antecedents of Travel Mode Choice 1,” J. Appl. Soc. Psychol., vol. 24, no. 4, pp. 285–300, Feb. 1994.

[10] H. Taale, M.-J. Olde Kalter, R. De Bruin, R. Smit, and B. Barnas, “Achtergrondrapportage ‘Monitoring mobiliteit en ververoer,’” 2020.

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Suggestions for other post-COVID-19 travel behavior projections

The Future of Mobility after COVID-19: Scenarios for transportation in a postcoronavirus world (Deloitte Insights)

Five COVID-19 Aftershocks Reshaping Moiblity’s Future (McKinsey Center for Future Mobility)

COVID-19 and urban mobility: impacts and perspectives, European Parliament, Policy Department for Structural and Cohesion Policies (Research for TRAN Committee)

The Future of Mobility Cost-COVID: Turning the crisis into an opportunity to accelerate towards more sustainable, resilient and human-centric urban mobility systems (Arthur D. Little Future Lab)

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