Impact of COVID-19 restrictions on mode use and mode captivity the city of Santo Domingo in Latin America

Abdullah, 2020, Exploring the impacts of COVID-19 on travel behavior and mode preferences, Transp. Res. Interdisc. Perspect., 8, 100255 Andani, A., La Paix L., Geurs K., 2021. Chapter 13 job access and spatial equity of a toll road, in applications of access, by David Levinson and Alireza Ermagun, Faculty Eng., School Civil Eng. https://ses.library.usyd.edu.au/handle/2123/26890. Anwari, 2021, Exploring the travel behavior changes caused by the COVID-19 crisis: a case study for a developing country, Transp. Res. Interdisc. Perspect., 9, 100334 Ben-Akiva, 1999, Extended framework for modeling choice behaviour, Market Lett., 10, 187, 10.1023/A:1008046730291 Bierlaire, M., Fetiarison, M., 2009. Estimation of discrete choice models: extending BIOGEME, 9th Swiss Transport Research Conference, Ascona, Switzerland. Castaigne, M., Cornelis, E., Frederix, R., Tampere, C.M.J., Toint, P., Viti, F., Walle, F., 2009. BMW: Behaviour and Mobility within the Week. Project report commissioned for BELSPO. Chen, 2016, The promises of big data and small data for travel behavior (aka human mobility) analysis, Transp. Res. C: Emerg. Technol., 68, 285, 10.1016/j.trc.2016.04.005 Cherchi, E., Cirillo, C., 2008. A mixed logit mode choice model on panel data: accounting for systematic and random variations on responses and preferences, 87th Annual Meeting of the Transportation Research Board, Washington, DC. Chorus, C.G., Kroesen, M., 2014. On the (im-)possibility of deriving transport policy implications from hybrid choice models. Transp. Policy 36, 217–222. De Haas, M., Faber, R., Hamersma, M., 2020. How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: evidence from longitudinal data in the Netherlands. Transp. Res. Interdisc. Perspect. 6, 100150. https://doi.org/https://doi.org/10.1016/j.trip.2020.100150. De Vos, 2020, The effect of COVID-19 and subsequent social distancing on travel behavior, Transp. Res. Interdisc. Perspect., 5, 100121 Espino, 2006, Analysing Demand for suburban trips: a mixed RP/SP model with latent variables and interaction effects, Transportation, 33, 241, 10.1007/s11116-005-2299-0 Glerum, 2014, Using semi-open questions to integrate perceptions in choice models, J. Choice Model., 10, 11, 10.1016/j.jocm.2013.12.001 Hensher, 2002 Hu, 2017, Job accessibility and employment outcomes: which income groups benefit the most?, Transportation, 44, 1421, 10.1007/s11116-016-9708-4 Hurtubia, 2014, Integrating psychometric indicators in latent class choice models, Transp. Res. A: Policy Pract., 64, 135 Jenelius, 2020, Impacts of COVID-19 on public transport ridership in Sweden: analysis of ticket validations, sales and passenger counts, Transp. Res. Interdisc. Perspect., 8, 100242 Jing, 2014, Incorporating psychological latent variables into travel mode choice model, Zhongguo Gonglu Xuebao/China J. Highway Transp., 27 Khaddar, 2021, COVID-19: Are you satisfied with traveling during the pandemic?, Transp. Res. Interdisc. Perspect., 9, 100292 Kim, 2014, Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: application to intended purchase of electric cars, Transp. Res. A: Policy Pract., 69, 71 Kroesen, 2017, Do attitudes cause behavior or vice versa? An alternative conceptualization of the attitude-behavior relationship in travel behavior modeling, Transp. Res. A: Policy Pract., 101, 190 La Paix Puello, 2015, Modelling observed and unobserved factors in cycling to railway stations: application to transit-oriented-developments in the Netherlands, Eur. J. Transp. Infrastruct. Res., 15, 27 La Paix Puello, L., Geurs., K.T., 2016. Integration of unobserved effects in generalised transport access costs of cycling to railway stations. Eur. J. Transp. Infrastruct. Res. 16, 385–405. La Paix Puello, 2017, Measurement of non-random attrition effects on mobility rates using trip diaries data, Transp. Res. A: Policy Pract., 106, 51 Le, 2020, How much dissatisfaction is too much for transit? Linking transit user satisfaction and loyalty using panel data, Travel Behav. Soc., 20, 144, 10.1016/j.tbs.2020.03.007 Line, 2010, The travel behaviour intentions of young people in the context of climate change, J. Transp. Geogr., 18, 238, 10.1016/j.jtrangeo.2009.05.001 Moeckel, 2017, Working from home: modeling the impact of telework on transportation and land use, Transp. Res. Procedia, 26, 207, 10.1016/j.trpro.2017.07.021 Mogaji, E., 2020 Impact of COVID-19 on transportation in Lagos, Nigeria. Transp. Res. Interdisc. Perspect. 6. Ortúzar, 2011, Continuous mobility surveys: the state of practice, Transp. Rev., 31, 293, 10.1080/01441647.2010.510224 Pawar, 2020, Impact of physical distancing due to novel coronavirus (SARS-CoV-2) on daily travel for work during transition to lockdown, Transp. Res. Interdisc. Perspect., 7 Paulssen, 2014, Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice, Transportation, 41, 873, 10.1007/s11116-013-9504-3 Schmid, B., Jokubauskaite, S., Aschauer, F., Peer, S., Hössinger, R., Gerike, R., Jara-Diaz, S.R., Axhausen, K.W., 2019. A pooled RP/SP mode, route and destination choice model to investigate mode and user-type effects in the value of travel time savings. Transp. Res. A: Policy Pract. 124, 262–294. Shao, C.-Q., Liu, Y., Liu, X.-M., 2014. Valuation of travel time savings in viewpoint of WTA. Comput. Intell. Neurosci. 305285. Thomas, 2021, Commuting before and after COVID-19, Transp. Res. Interdisc. Perspect., 100423 Train, 2000 Train, 2003 Walker, 2002, Generalized random utility model, Math. Social Sci., 43, 303, 10.1016/S0165-4896(02)00023-9 Wang, D., Pedreschi, D., Song, C., Giannotti, F., Barabasi, A.-L., 2011. Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Diego, California, USA, pp. 1100–1108. Yáñez, M.F., Raveau, S., Ortúzar, J.d.D., 2010. Inclusion of latent variables in Mixed Logit models: modelling and forecasting. Transp. Res. A: Policy Pract. 44, 744–753. Yañez-Pagans, P., Martinez, D., Mitnik, O.A., et al., 2019. Urban transport systems in Latin America and the Caribbean: lessons and challenges. Lat. Am. Econ. Rev. 28, 15. Zhao, 2020, Car pride and its behavioral implications: an exploration in Shanghai, Transportation, 47, 793, 10.1007/s11116-018-9917-0