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. 2022 Jun:162:102718.
doi: 10.1016/j.tre.2022.102718. Epub 2022 Apr 26.

Advanced modelling of commuter choice model and work from home during COVID-19 restrictions in Australia

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Advanced modelling of commuter choice model and work from home during COVID-19 restrictions in Australia

Camila Balbontin et al. Transp Res E Logist Transp Rev. 2022 Jun.

Abstract

The decision to work from home (WFH) or to commute during COVID-19 is having a major structural impact on individuals' travel, work and lifestyle. There are many possible factors influencing this non-marginal change, some of which are captured by objective variables while others are best represented by a number of underlying latent traits captured by attitudes towards WFH and the use of specific modes of transport for the commute that have a bio-security risk such as public transport (PT). We develop and implement a hybrid choice model to investigate the sources of influence, accounting for the endogenous nature of latent soft variables for workers in metropolitan areas in New South Wales and Queensland. The data was collected between September-October 2020, during a period of no lockdown and relatively minor restrictions on workplaces and public gatherings. The results show that one of the most important attributes defining the WFH loving attitude is the workplace policy towards WFH, with workers that can decide where to work having a higher probability of WFH, followed by those that are being directed to, relative to other workplace policies. The bio-security concern with using shared modes such as public transport is a key driver of WFH and choosing to commute via the safer environment of the private car.

Keywords: COVID-19; Commuting activity; Hybrid choice model; Working from home.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Work from home policy of their place of employment as it stands today.
Fig. 2
Fig. 2
Commuting, WFH and no work behaviour.
Fig. 3
Fig. 3
Modal share prior to COVID-19 and currently.
Fig. 4
Fig. 4
Waiting time experience last time they used public transport.
Fig. 5
Fig. 5
Individuals’ daily alternatives structure.
Fig. 6
Fig. 6
Hybrid model framework.
Fig. 7
Fig. 7
Direct Mean Elasticity Effects Hybrid Choice Model.
Fig. 8
Fig. 8
Observed versus estimated (base scenario for simulation) probability to WFH.
Fig. 9
Fig. 9
Simulated scenarios results.

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