Exploring activity-travel behavior changes during the beginning of COVID-19 pandemic in Indonesia

Abstract

This study examines the change in activities and associated travel during the beginning of COVID-19 pandemic in Indonesia. This study is particularly interested in analyzing the role of attitudes, descriptive norms, protective behaviors toward COVID-19, travel frequency before the pandemic, and spatial and individual characteristics on activity-travel behavior changes in relation to information and communication technology (ICT) use. Data were obtained from 1062 respondents using a web-based questionnaire survey. Structural equation modeling was used to examine the complex relationships among variables. This study found that descriptive norms positively affected the frequency of travel during the COVID-19 pandemic. Teleworking and e-learning and attitudes toward COVID-19 directly affected activity-travel behavior changes. On the contrary, teleshopping did not contribute to reducing out-of-home activities during the COVID-19 pandemic. Experience of ICT influenced a decline in travel frequency and ride-hailing use. Furthermore, although personal attributes insignificantly influenced activity-travel behavior change, these attributes directly affected ICT use. Meanwhile, people living outside of Java Island had a higher travel frequency during the beginning of COVID-19 pandemic than their counterparts. Based on our findings, this study recommends that the very initial step in an emergency caused by a disaster be to massively socialize or educate people about the risk of the pandemic and to continue with a policy to minimize travel by encouraging teleworking and e-learning. Empowering ICT to support activities from home will beneficially minimize the spread of the pandemic.

Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova

“…Income was found to have a positive effect on the probability not to carry out the trip [4,45,46], but only for employees. Gender has an impact only for students, with males more willing to travel rather females [10,12,29,46,65]. Moreover, as a consequence of the choice to stay at home, the developed model predicts that employees might reduce their commuting trips using sub urban bus, bikes and walking.…”

“…Concerning dissimilarities, age showed an opposite effect on trip cancellation; specifically, younger students tend to go to university, whereas younger employees tend to stay at home [4,7,23,45]. Income was found to have a positive effect on the probability not to carry out the trip [4,45,46], but only for employees. Gender has an impact only for students, with males more willing to travel rather females [10,12,29,46,65].…”

“…Fatmi et al [45] used ordered and binary logit models to investigate changes in indoor and outdoor activities, as well as long-distance travels, in Canada. Irawan et al [46] developed a structural equation model to analyse variations in activity and travel behaviour after the COVID-19 outbreak in Indonesia. Politis et al [12,25] applied a generalized linear model and ordinary-least squares regressions to predict trip frequency and duration in Greece.…”

“…Specifically, long-lasting changes might be prompted by the following factors. Firstly, remote working and online learning may induce the cancellation of many systematic trips [4,8,9,46,50] since individuals have the possibility to carry out the corresponding activities at home [7,29,51]. Although factors influencing both remote working and the prediction of its adoption were investigated by many authors [7,10,13,29,51,52], only a few previous works focused on the impact of online learning and students' behaviours on travel demand, but without any predictive analysis.…”

“…In addition, it contributes to the analysis of factors affecting travel demand with mandatory purposes in the "new normal" phase, by using outcomes from people who had previously experienced remote working and online learning for several months, thus increasing the reliability of results; indeed, the majority of previous authors adopted data about people who did not carry out these online activities for long periods, potentially leading to biased results [52]. Furthermore, students and employees can actually choose whether to travel or not [46]. Moreover, in this paper, safety measures and new travel modes, which are likely to be adopted in the future, are explicitly and jointly considered for the first time, thereby predicting travel demand under the most probable future realistic conditions.…”