Travel behaviour and multimodality: a conceptual framework and critical review of research

References

• Akenji, L. (2014). Consumer scapegoatism and limits to green consumerism. Journal of Cleaner Production, 63, 13–23. https://doi.org/10.1016/j.jclepro.2013.05.022
   Web of Science ®Google Scholar
• Alessandretti, L., Orozco, L. G. N., Battiston, F., Saberi, M., & Szell, M. (2022). Multimodal urban mobility and multilayer transport networks. Environment and Planning B-Urban Analytics and City Science, https://doi.org/10.1177/23998083221108190
   Web of Science ®Google Scholar
• Alonso-Gonzalez, M. J., Hoogendoorn-Lanser, S., van Oort, N., Cats, O., & Hoogendoorn, S. (2020). Drivers and barriers in adopting Mobility as a Service (MaaS) - A latent class cluster analysis of attitudes. Transportation Research Part a-Policy and Practice, 132, 378–401. https://doi.org/10.1016/j.tra.2019.11.022
   Web of Science ®Google Scholar
• An, Y., Lin, X., Li, M., & He, F. (2021). Dynamic governance decisions on multi-modal inter-city travel during a large-scale epidemic spreading. Transport Policy, 104, 29–42. https://doi.org/10.1016/j.tranpol.2021.01.008
   PubMed Web of Science ®Google Scholar
• An, Z., Heinen, E., & Watling, D. (2021). When you are born matters: An age-period-cohort analysis of multimodality. Travel Behaviour and Society, 22, 129–145. https://doi.org/10.1016/j.tbs.2020.09.002
   Web of Science ®Google Scholar
• An, Z., Heinen, E., & Watling, D. (2022). Multimodal travel behaviour, attitudes, and cognitive dissonance. Transportation Research Part F-Traffic Psychology and Behaviour, 91, 260–273. https://doi.org/10.1016/j.trf.2022.10.007
   Web of Science ®Google Scholar
• An, Z., Heinen, E., & Watling, D. (2023). The level and determinants of multimodal travel behavior: Does trip purpose make a difference? International Journal of Sustainable Transportation, 17(2), 103–117. https://doi.org/10.1080/15568318.2021.1985195
   Web of Science ®Google Scholar
• Ardeshiri, A., & Vij, A. (2019). Lifestyles, residential location, and transport mode use: A hierarchical latent class choice model. Transportation Research Part a-Policy and Practice, 126, 342–359. https://doi.org/10.1016/j.tra.2019.06.016
   Web of Science ®Google Scholar
• Arentze, T. A., & Molin, E. J. E. (2013). Travelers’ preferences in multimodal networks: Design and results of a comprehensive series of choice experiments. Transportation Research Part a-Policy and Practice, 58, 15–28. https://doi.org/10.1016/j.tra.2013.10.005
   Web of Science ®Google Scholar
• Basu, R., & Ferreira, J. (2021). Sustainable mobility in auto-dominated Metro Boston: Challenges and opportunities post-COVID-19. Transport Policy, 103, 197–210. https://doi.org/10.1016/j.tranpol.2021.01.006
   PubMed Web of Science ®Google Scholar
• Best, H., & Lanzendorf, M. (2005). Division of labour and gender differences in metropolitan car use: An empirical study in Cologne, Germany. Journal of Transport Geography, 13(2), 109–121. https://doi.org/10.1016/j.jtrangeo.2004.04.007
   Google Scholar
• Blumenberg, E., & Pierce, G. (2014). Multimodal travel and the poor: Evidence from the 2009 national household travel survey. Transportation Letters-the International Journal of Transportation Research, 6(1), 36–45. https://doi.org/10.1179/1942787513y.0000000009
   Web of Science ®Google Scholar
• Bocker, L., Anderson, E., Uteng, T. P., & Throndsen, T. (2020). Bike sharing use in conjunction to public transport: Exploring spatiotemporal, age and gender dimensions in Oslo, Norway. Transportation Research Part a-Policy and Practice, 138, 389–401. https://doi.org/10.1016/j.tra.2020.06.009
   Web of Science ®Google Scholar
• Bovy, P. H. L., & Hoogendoorn-Lanser, S. (2005). Modelling route choice behaviour in multi-modal transport networks. Transportation, 32(4), 341–368. https://doi.org/10.1007/s11116-004-7963-2
   Web of Science ®Google Scholar
• Buehler, R., & Hamre, A. (2015). The multimodal majority? Driving, walking, cycling, and public transportation use among American adults. Transportation, 42(6), 1081–1101. https://doi.org/10.1007/s11116-014-9556-z
   Web of Science ®Google Scholar
• Buehler, R., & Pucher, J. (2012). Demand for public transport in Germany and the USA: An analysis of rider characteristics. Transport Reviews, 32(5), 541–567. https://doi.org/10.1080/01441647.2012.707695
   Web of Science ®Google Scholar
• Carpio-Pinedo, J. (2021). Multimodal transport and potential encounters with social difference: A novel approach based on network analysis. Journal of Urban Affairs, 43(1), 93–116. https://doi.org/10.1080/07352166.2019.1662727
   Web of Science ®Google Scholar
• Chakrabarti, S. (2022). Passively wait for gridlock, or proactively invest in service? Strategies to promote car-to-transit switches among aspirational urbanites in rapidly developing contexts. Transport Policy, 115, 251–261. https://doi.org/10.1016/j.tranpol.2021.11.018
   Web of Science ®Google Scholar
• Chan, H.-Y., Xu, Y., Chen, A., & Liu, X. (2022). Impacts of the walking environment on mode and departure time shifts in response to travel time change: Case study in the multi-layered Hong Kong metropolis. Travel Behaviour and Society, 28, 288–299. https://doi.org/10.1016/j.tbs.2022.04.010
   Web of Science ®Google Scholar
• Ciuffini, F., Tengattini, S., & Bigazzi, A. Y. (2021). Mitigating increased driving after the COVID-19 pandemic: An analysis on mode share, travel demand, and public transport capacity. Transportation Research Record, Article 03611981211037884. https://doi.org/10.1177/03611981211037884
   Web of Science ®Google Scholar
• Clauss, T., & Doeppe, S. (2016). Why do urban travelers select multimodal travel options: A repertory grid analysis. Transportation Research Part a-Policy and Practice, 93, 93–116. https://doi.org/10.1016/j.tra.2016.08.021
   Web of Science ®Google Scholar
• Cottrill, C. D., Brooke, S., Mulley, C., Nelson, J. D., & Wright, S. (2020). Can multi-modal integration provide enhanced public transport service provision to address the needs of vulnerable populations? Research in Transportation Economics, 83, Article 100954. https://doi.org/10.1016/j.retrec.2020.100954
   Web of Science ®Google Scholar
• de Freitas, L. M., Becker, H., Zimmerrnann, M., & Axhausen, K. W. (2019). Modelling intermodal travel in Switzerland: A recursive logit approach. Transportation Research Part a-Policy and Practice, 119, 200–213. https://doi.org/10.1016/j.tra.2018.11.009
   Web of Science ®Google Scholar
• Delbosc, A., & Nakanishi, H. (2017). A life course perspective on the travel of Australian millennials. Transportation Research Part a-Policy and Practice, 104, 319–336. https://doi.org/10.1016/j.tra.2017.03.014
   Web of Science ®Google Scholar
• Deschaintres, E., Morency, C., & Trepanier, M. (2021). Measuring changes in multimodal travel behavior resulting from transport supply improvement. Transportation Research Record, 2675(9), 533–546. Article 03611981211003104. https://doi.org/10.1177/03611981211003104
   Web of Science ®Google Scholar
• de Souza, F., Puello, L. L. P., Brussel, M., Orrico, R., & van Maarseveen, M. (2017). Modelling the potential for cycling in access trips to bus, train and metro in Rio de Janeiro. Transportation Research Part D-Transport and Environment, 56, 55–67. https://doi.org/10.1016/j.trd.2017.07.007
   Web of Science ®Google Scholar
• De Vos, J., Singleton, P. A., & Garling, T. (2022). From attitude to satisfaction: Introducing the travel mode choice cycle. Transport Reviews, 42(2), 204–221. https://doi.org/10.1080/01441647.2021.1958952
   Web of Science ®Google Scholar
• Diana, M., & Mokhtarian, P. L. (2009). Desire to change one's multimodality and its relationship to the use of different transport means. Transportation Research Part F-Traffic Psychology and Behaviour, 12(2), 107–119. https://doi.org/10.1016/j.trf.2008.09.001
   Web of Science ®Google Scholar
• Diana, M., & Pirra, M. (2016). A comparative assessment of synthetic indices to measure multimodality behaviours. Transportmetrica a-Transport Science, 12(9), 771–793. https://doi.org/10.1080/23249935.2016.1177133
   Web of Science ®Google Scholar
• Dixit, M., Brands, T., van Oort, N., Cats, O., & Hoogendoorn, S. (2019). Passenger travel time reliability for multimodal public transport journeys. Transportation Research Record, 2673(2), 149–160. https://doi.org/10.1177/0361198118825459
   Web of Science ®Google Scholar
• Docherty, I., Marsden, G., & Anable, J. (2018). The governance of smart mobility. Transportation Research Part a-Policy and Practice, 115, 114–125. https://doi.org/10.1016/j.tra.2017.09.012
   Web of Science ®Google Scholar
• Faber, R. M., Jonkeren, O., de Haas, M. C., Molin, E. J. E., & Kroesen, M. (2022). Inferring modality styles by revealing mode choice heterogeneity in response to weather conditions. Transportation Research Part a-Policy and Practice, 162, 282–295. https://doi.org/10.1016/j.tra.2022.06.003
   Web of Science ®Google Scholar
• Ferro, P. S., & Behrens, R. (2015). From direct to trunk-and-feeder public transport services in the Urban South: Territorial implications. Journal of Transport and Land Use, 8(1), 123–136. https://doi.org/10.5198/jtlu.2015.389
   Web of Science ®Google Scholar
• Firnkorn, J. (2012). Triangulation of two methods measuring the impacts of a free-floating carsharing system in Germany. Transportation Research Part a-Policy and Practice, 46(10), 1654–1672. https://doi.org/10.1016/j.tra.2012.08.003
   Web of Science ®Google Scholar
• Frank, L., Dirks, N., & Walther, G. (2021). Improving rural accessibility by locating multimodal mobility hubs. Journal of Transport Geography, 94, Article 103111. https://doi.org/10.1016/j.jtrangeo.2021.103111
   Web of Science ®Google Scholar
• Gross, S., & Grimm, B. (2018). Sustainable mode of transport choices at the destination – public transport at German destinations. Tourism Review, 73(3), 401–420. https://doi.org/10.1108/TR-11-2017-0177
   Web of Science ®Google Scholar
• Grotenhuis, J.-W., Wiegmans, B. W., & Rietveld, P. (2007). The desired quality of integrated multimodal travel information in public transport: Customer needs for time and effort savings. Transport Policy, 14(1), 27–38. https://doi.org/10.1016/j.tranpol.2006.07.001
   Web of Science ®Google Scholar
• Groth, S. (2019). Multimodal divide: Reproduction of transport poverty in smart mobility trends. Transportation Research Part a-Policy and Practice, 125, 56–71. https://doi.org/10.1016/j.tra.2019.04.018
   Web of Science ®Google Scholar
• Haustein, S., & Siren, A. (2015). Older people's mobility: Segments, factors, Trends. Transport Reviews, 35(4), 466–487. https://doi.org/10.1080/01441647.2015.1017867
   Web of Science ®Google Scholar
• Heinen, E. (2018). Are multimodals more likely to change their travel behaviour? A cross-sectional analysis to explore the theoretical link between multimodality and the intention to change mode choice. Transportation Research Part F-Traffic Psychology and Behaviour, 56, 200–214. https://doi.org/10.1016/j.trf.2018.04.010
   Web of Science ®Google Scholar
• Heinen, E., & Chatterjee, K. (2015). The same mode again? An exploration of mode choice variability in Great Britain using the national travel survey. Transportation Research Part a-Policy and Practice, 78, 266–282. https://doi.org/10.1016/j.tra.2015.05.015
   Web of Science ®Google Scholar
• Heinen, E., & Mattioli, G. (2019). Multimodality and CO2 emissions: A relationship moderated by distance. Transportation Research Part D-Transport and Environment, 75, 179–196. https://doi.org/10.1016/j.trd.2019.08.022
   Web of Science ®Google Scholar
• Heinen, E., & Ogilvie, D. (2016). Variability in baseline travel behaviour as a predictor of changes in commuting by active travel, car and public transport: A natural experimental study. Journal of Transport & Health, 3(1), 77–85. https://doi.org/10.1016/j.jth.2015.11.002
   PubMed Web of Science ®Google Scholar
• Huang, Y., Gan, H., Jing, P., & Wang, X. (2022). Analysis of park and ride mode choice behavior under multimodal travel information service. Transportation Letters-the International Journal of Transportation Research, 14(10), 1080–1090. https://doi.org/10.1080/19427867.2021.1988438
   Web of Science ®Google Scholar
• Huang, Y., & Li, Z. (2020). Optimal multimodal travelway design for an urban street network. Ieee Access, 8, 187700–187712. https://doi.org/10.1109/access.2020.3030234
   Google Scholar
• Hunecke, M., Groth, S., & Wittowsky, D. (2020). Young social milieus and multimodality: Interrelations of travel behaviours and psychographic characteristics. Mobilities, 15(3), 397–415. https://doi.org/10.1080/17450101.2020.1732099
   Web of Science ®Google Scholar
• Jamal, S., Newbold, K. B., & Scott, D. M. M. (2023). Developing a typology of daily travelers based on transportation attitudes: Application of latent class analysis using a survey of millennials and older adults in Hamilton, Ontario. Growth and Change, https://doi.org/10.1111/grow.12678
   Web of Science ®Google Scholar
• Jang, S., Caiati, V., Rasouli, S., Timmermans, H., & Choi, K. (2021). Does MaaS contribute to sustainable transportation? A mode choice perspective. International Journal of Sustainable Transportation, 15(5), 351–363. https://doi.org/10.1080/15568318.2020.1783726
   Web of Science ®Google Scholar
• Jappinen, S., Toivonen, T., & Salonen, M. (2013). Modelling the potential effect of shared bicycles on public transport travel times in Greater Helsinki: An open data approach. Applied Geography, 43, 13–24. https://doi.org/10.1016/j.apgeog.2013.05.010
   Web of Science ®Google Scholar
• Jiao, J., & Wang, F. (2021). Shared mobility and transit-dependent population: A new equity opportunity or issue? International Journal of Sustainable Transportation, 15(4), 294–305. https://doi.org/10.1080/15568318.2020.1747578
   Web of Science ®Google Scholar
• Kapuku, C., Kho, S.-Y., Kim, D.-K., & Cho, S.-H. (2021). Assessing and predicting mobility improvement of integrating bike-sharing into multimodal public transport systems. Transportation Research Record, 2675(11), 204–213. Article 03611981211045071. https://doi.org/10.1177/03611981211045071
   Web of Science ®Google Scholar
• Kent, J. L. (2014). Driving to save time or saving time to drive? The enduring appeal of the private car. Transportation Research Part a-Policy and Practice, 65, 103–115. https://doi.org/10.1016/j.tra.2014.04.009
   Web of Science ®Google Scholar
• Klinger, T. (2017). Moving from monomodality to multimodality? Changes in mode choice of new residents. Transportation Research Part a-Policy and Practice, 104, 221–237. https://doi.org/10.1016/j.tra.2017.01.008
   Web of Science ®Google Scholar
• Klinger, T., Kenworthy, J. R., & Lanzendorf, M. (2013). Dimensions of urban mobility cultures - a comparison of German cities. Journal of Transport Geography, 31, 18–29. https://doi.org/10.1016/j.jtrangeo.2013.05.002
   Web of Science ®Google Scholar
• Kroesen, M. (2014). Modeling the behavioral determinants of travel behavior: An application of latent transition analysis. Transportation Research Part a-Policy and Practice, 65, 56–67. https://doi.org/10.1016/j.tra.2014.04.010
   Web of Science ®Google Scholar
• Krygsman, S., Dijst, M., & Trb. (2001). Multimodal trips in the Netherlands – Microlevel individual attributes and residential context. Transportation Research Record, 1753(1), 11–19. doi:10.3141/1753-02
   Google Scholar
• Kuhnimhof, T., Buehler, R., Wirtz, M., & Kalinowska, D. (2012). Travel trends among young adults in Germany: Increasing multimodality and declining car use for men. Journal of Transport Geography, 24, 443–450. https://doi.org/10.1016/j.jtrangeo.2012.04.018
   Web of Science ®Google Scholar
• Kuhnimhof, T., Wirtz, M., & Manz, W. (2012). Decomposing young Germans’ altered car use patterns lower incomes, more students, decrease in car travel by men, and more multimodality. Transportation Research Record, 2320(1), 64–71. https://doi.org/10.3141/2320-08
   Web of Science ®Google Scholar
• Lanzini, P., & Khan, S. A. (2017). Shedding light on the psychological and behavioral determinants of travel mode choice: A meta-analysis [Article]. Transportation Research Part F-Traffic Psychology and Behaviour, 48, 13–27. https://doi.org/10.1016/j.trf.2017.04.020
   Web of Science ®Google Scholar
• Lee, S. (2022). Exploring associations between multimodality and built environment characteristics in the U.S. Sustainability, 14(11), Article 6629. https://doi.org/10.3390/su14116629
   Web of Science ®Google Scholar
• Lee, Y., Circella, G., Mokhtarian, P. L., & Guhathakurta, S. (2020). Are millennials more multimodal? A latent-class cluster analysis with attitudes and preferences among millennial and Generation X commuters in California. Transportation, 47(5), 2505–2528. https://doi.org/10.1007/s11116-019-10026-6
   Web of Science ®Google Scholar
• Lemonde, C., Arsenio, E., & Henriques, R. (2021). Integrative analysis of multimodal traffic data: Addressing open challenges using big data analytics in the city of Lisbon. European Transport Research Review, 13(1), Article 64. https://doi.org/10.1186/s12544-021-00520-3
   Web of Science ®Google Scholar
• Li, Z.-C., Yao, M.-Z., Lam, W. H. K., Sumalee, A., & Choi, K. (2015). Modeling the effects of public bicycle schemes in a congested multi-modal road network. International Journal of Sustainable Transportation, 9(4), 282–297. https://doi.org/10.1080/15568318.2013.767398
   Web of Science ®Google Scholar
• Liao, F., Tian, Q., Arentze, T., Huang, H.-J., & Timmermans, H. J. P. (2020). Travel preferences of multimodal transport systems in emerging markets: The case of Beijing. Transportation Research Part a-Policy and Practice, 138, 250–266. https://doi.org/10.1016/j.tra.2020.05.026
   Web of Science ®Google Scholar
• Lin, T., Hasnine, S., & Habib, K. M. N. (2019). Influence of latent attitudinal factors on the multimodality post-secondary students in Toronto. Canadian Journal of Civil Engineering, 46(4), 251–260. https://doi.org/10.1139/cjce-2017-0333
   Web of Science ®Google Scholar
• Lu, Q.-C., & Lin, S. (2019). Vulnerability analysis of urban rail transit network within multi-modal public transport networks. Sustainability, 11(7), Article 2109. https://doi.org/10.3390/su11072109
   Web of Science ®Google Scholar
• Lu, Y., Prato, C. G., Sipe, N., Kimpton, A., & Corcoran, J. (2022). The role of household modality style in first and last mile travel mode choice. Transportation Research Part a-Policy and Practice, 158, 95–109. https://doi.org/10.1016/j.tra.2022.02.003
   Web of Science ®Google Scholar
• Luo, Q., Li, S., & Hampshire, R. C. (2021). Optimal design of intermodal mobility networks under uncertainty: Connecting micromobility with mobility-on-demand transit. Euro Journal on Transportation and Logistics, 10, Article 100045. https://doi.org/10.1016/j.ejtl.2021.100045
   Web of Science ®Google Scholar
• Makarewicz, C., & Nemeth, J. (2018). Are multimodal travelers more satisfied with their lives? A study of accessibility and wellbeing in the Denver, Colorado metropolitan area. Cities, 74, 179–187. https://doi.org/10.1016/j.cities.2017.12.001
   Web of Science ®Google Scholar
• Mao, Z., Ettema, D., & Dijst, M. (2016). Commuting trip satisfaction in Beijing: Exploring the influence of multimodal behavior and modal flexibility. Transportation Research Part a-Policy and Practice, 94, 592–603. https://doi.org/10.1016/j.tra.2016.10.017
   Web of Science ®Google Scholar
• Matyas, M. (2020). Opportunities and barriers to multimodal cities: Lessons learned from in-depth interviews about attitudes towards mobility as a service. European Transport Research Review, 12(1), Article 7. https://doi.org/10.1186/s12544-020-0395-z
   Web of Science ®Google Scholar
• McLaren, A. T. (2016). Families and transportation: Moving towards multimodality and altermobility? Journal of Transport Geography, 51, 218–225. https://doi.org/10.1016/j.jtrangeo.2016.01.006
   Web of Science ®Google Scholar
• McLeod, S., Scheurer, J., & Curtis, C. (2017). Urban public transport: Planning principles and emerging practice. Journal of Planning Literature, 32(3), 223–239. https://doi.org/10.1177/0885412217693570
   Web of Science ®Google Scholar
• Mehdizadeh, M., & Ermagun, A. (2020). I'll never stop driving my child to school": on multimodal and monomodal car users. Transportation, 47(3), 1071–1102. https://doi.org/10.1007/s11116-018-9949-5
   Web of Science ®Google Scholar
• Mehdizadeh, M., Zavareh, M. F., & Nordfjaern, T. (2019). Mono- and multimodal green transport use on university trips during winter and summer: Hybrid choice models on the norm-activation theory. Transportation Research Part a-Policy and Practice, 130, 317–332. https://doi.org/10.1016/j.tra.2019.09.046
   Web of Science ®Google Scholar
• Meng, M., Rau, A., & Mahardhika, H. (2018). Public transport travel time perception: Effects of socioeconomic characteristics, trip characteristics and facility usage. Transportation Research Part a-Policy and Practice, 114, 24–37. https://doi.org/10.1016/j.tra.2018.01.015
   Web of Science ®Google Scholar
• Molin, E., & Maat, K. (2015). Bicycle parking demand at railway stations: Capturing price-walking trade offs. Research in Transportation Economics, 53, 3–12. https://doi.org/10.1016/j.retrec.2015.10.014
   Web of Science ®Google Scholar
• Molin, E., Mokhtarian, P., & Kroesen, M. (2016). Multimodal travel groups and attitudes: A latent class cluster analysis of Dutch travelers. Transportation Research Part a-Policy and Practice, 83, 14–29. https://doi.org/10.1016/j.tra.2015.11.001
   Web of Science ®Google Scholar
• Mulley, C., & Moutou, C. J. (2015). Not too late to learn from the Sydney Olympics experience: Opportunities offered by multimodality in current transport policy. Cities, 45, 117–122. https://doi.org/10.1016/j.cities.2014.10.004
   Web of Science ®Google Scholar
• Nash, S., & Mitra, R. (2019). University students’ transportation patterns, and the role of neighbourhood types and attitudes. Journal of Transport Geography, 76, 200–211. https://doi.org/10.1016/j.jtrangeo.2019.03.013
   Web of Science ®Google Scholar
• Natera, L., Battiston, F., Iñiguez, G., & Szell, M. (2020). Extracting the multimodal fingerprint of urban transportation networks. arXiv preprint, Article arXiv: 03435.
   Google Scholar
• Nobis, C. (2007). Multimodality - facets and causes of sustainable mobility behavior. Transportation Research Record, 2010(1), 37–46. https://doi.org/10.3141/2010-05
   Google Scholar
• Olafsson, A. S., Nielsen, T. S., & Carstensen, T. A. (2016). Cycling in multimodal transport behaviours: Exploring modality styles in the Danish population. Journal of Transport Geography, 52, 123–130. https://doi.org/10.1016/j.jtrangeo.2016.03.010
   Web of Science ®Google Scholar
• Pan, H., Shen, Q., & Xue, S. (2010). Intermodal transfer between bicycles and rail transit in Shanghai, China. Transportation Research Record, 2144(1), 181–188. https://doi.org/10.3141/2144-20
   Google Scholar
• Park, K., Farb, A., & Chen, S. (2021). First-/last-mile experience matters: The influence of the built environment on satisfaction and loyalty among public transit riders. Transport Policy, 112, 32–42. https://doi.org/10.1016/j.tranpol.2021.08.003
   Web of Science ®Google Scholar
• Pucher, J., & Buehler, R. (2008). Making cycling irresistible: Lessons from the Netherlands, Denmark and Germany. Transport Reviews, 28(4), 495–528. https://doi.org/10.1080/01441640701806612
   Web of Science ®Google Scholar
• Ralph, K. M. (2017). Multimodal millennials? The four traveler types of young people in the United States in 2009. Journal of Planning Education and Research, 37(2), 150–163. https://doi.org/10.1177/0739456(16651930
   Web of Science ®Google Scholar
• Ravensbergen, L., Buliung, R., Mendonca, M., & Garg, N. (2018). Biking to ride: Investigating the challenges and barriers of integrating cycling with regional rail transit. Transportation Research Record, 2672(8), 374–383. https://doi.org/10.1177/0361198118777080
   Web of Science ®Google Scholar
• Riggs, W., & Sethi, S. A. (2020). Multimodal travel behaviour, walkability indices, and social mobility: How neighbourhood walkability, income and household characteristics guide walking, biking & transit decisions. Local Environment, 25(1), 57–68. https://doi.org/10.1080/13549839.2019.1698529
   Web of Science ®Google Scholar
• Rongen, T., Tillema, T., Arts, J., Alonso-Gonz, M. J., & Witte, J.-J. (2022). An analysis of the mobility hub concept in the Netherlands: Historical lessons for its implementation. Journal of Transport Geography, 104, Article 103419. https://doi.org/10.1016/j.jtrangeo.2022.103419
   Web of Science ®Google Scholar
• Schakenbos, R., La Paix, L., Nijenstein, S., & Geurs, K. T. (2016). Valuation of a transfer in a multimodal public transport trip. Transport Policy, 46, 72–81. https://doi.org/10.1016/j.tranpol.2015.11.008
   Web of Science ®Google Scholar
• Scheiner, J., Chatterjee, K., & Heinen, E. (2016). Key events and multimodality: A life course approach. Transportation Research Part a-Policy and Practice, 91, 148–165. https://doi.org/10.1016/j.tra.2016.06.028
   Web of Science ®Google Scholar
• Shaheen, S., & Cohen, A. (2020). Similarities and Differences of Mobility on Demand (MOD) and Mobility as a Service (MaaS). Ite Journal-Institute of Transportation Engineers, 90(6), 29–35.
   Web of Science ®Google Scholar
• Sunitiyoso, Y., Rahayu, W. A., Nuraeni, S., Nurdayat, I. F., Pambudi, N. F., & Hadiansyah, F. (2022). Role of ride-hailing in multimodal commuting. Case Studies on Transport Policy, 10(2), 1283–1298. https://doi.org/10.1016/j.cstp.2022.04.013
   Web of Science ®Google Scholar
• Tasic, I., & Porter, R. J. (2016). Modeling spatial relationships between multimodal transportation infrastructure and traffic safety outcomes in urban environments. Safety Science, 82, 325–337. https://doi.org/10.1016/j.ssci.2015.09.021
   Web of Science ®Google Scholar
• Tsirimpa, A., Polydoropoulou, A., Pagoni, L., & Tsouros, L. (2019). A reward-based instrument for promoting multimodality. Transportation Research Part F-Traffic Psychology and Behaviour, 65, 121–140. https://doi.org/10.1016/j.trf.2019.07.002
   Web of Science ®Google Scholar
• Van Eenoo, E., Fransen, K., & Boussauw, K. (2022). Perceived car dependence and multimodality in urban areas in Flanders (Belgium). European Journal of Transport and Infrastructure Research, 22(1), 42–62. https://doi.org/10.18757/ejtir.2022.22.1.6044
   Web of Science ®Google Scholar
• van Nes, R., & Bovy, P. H. L. (2004). Multimodal traveling and its impact on urban transit network design. Journal of Advanced Transportation, 38(3), 225–241. https://doi.org/10.1002/atr.5670380302
   Web of Science ®Google Scholar
• Van Wee, B., & Banister, D. (2016). How to write a literature review paper? [Review]. Transport Reviews, 36(2), 278–288. https://doi.org/10.1080/01441647.2015.1065456
   Web of Science ®Google Scholar
• Venter, C. J. (2020). Measuring the quality of the first/last mile connection to public transport. Research in Transportation Economics, 83, Article 100949. https://doi.org/10.1016/j.retrec.2020.100949
   Web of Science ®Google Scholar
• Vij, A., Carrel, A., & Walker, J. L. (2013). Incorporating the influence of latent modal preferences on travel mode choice behavior. Transportation Research Part a-Policy and Practice, 54, 164–178. https://doi.org/10.1016/j.tra.2013.07.008
   Web of Science ®Google Scholar
• Wang, R., Chen, F., Liu, X., Liu, X., Li, Z., & Zhu, Y. (2021). A matching model for door-to-door multimodal transit by integrating taxi-sharing and subways. Isprs International Journal of Geo-Information, 10(7), Article 469. https://doi.org/10.3390/ijgi10070469
   Web of Science ®Google Scholar
• Wang, X., Yan, X., Zhao, X., & Cao, Z. (2022). Identifying latent shared mobility preference segments in low-income communities: Ride-hailing, fixed-route bus, and mobility-on-demand transit. Travel Behaviour and Society, 26, 134–142. https://doi.org/10.1016/j.tbs.2021.09.011
   Web of Science ®Google Scholar
• Watkins, K. E., Ferris, B., Borning, A., Rutherford, G. S., & Layton, D. (2011). Where is my bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders. Transportation Research Part a-Policy and Practice, 45(8), 839–848. https://doi.org/10.1016/j.tra.2011.06.010
   Web of Science ®Google Scholar
• Xie, Y., Danaf, M., Azevedo, C. L., Akkinepally, A. P., Atasoy, B., Jeong, K., Seshadri, R., & Ben-Akiva, M. (2019). Behavioral modeling of on-demand mobility services: General framework and application to sustainable travel incentives. Transportation, 46(6), 2017–2039. https://doi.org/10.1007/s11116-019-10011-z
   Web of Science ®Google Scholar
• Yin, B., & Leurent, F. (2022). What are the multimodal patterns of individual mobility at the day level in the Paris region? A two-stage data-driven approach based on the 2018 household travel survey. Transportation, https://doi.org/10.1007/s11116-022-10285-w
   Web of Science ®Google Scholar
• Zgheib, N., Abou-Zeid, M., & Kaysi, I. (2020). Modeling demand for ridesourcing as feeder for high capacity mass transit systems with an application to the planned Beirut BRT. Transportation Research Part a-Policy and Practice, 138, 70–91. https://doi.org/10.1016/j.tra.2020.05.019
   Web of Science ®Google Scholar
• Zhang, L., Liu, Y., Lieske, S. N., & Corcoran, J. (2023). Understanding cycling dissonance in travel mode choice for work trip chains: The role of modality styles. Applied Geography, 153, Article 102895. https://doi.org/10.1016/j.apgeog.2023.102895
   Web of Science ®Google Scholar