
Published 2024-12-18
Keywords
- survey,
- mobility behavior,
- urban mobility patterns,
- spatiotemporal transportation,
- Cyprus
How to Cite
Copyright (c) 2024 Philip Fayad, Phaedon Kyriakidis, Constantinos Tsioutis, Dimitris Kavroudakis

This work is licensed under a Creative Commons Attribution 4.0 International License.
Accepted 2024-12-18
Published 2024-12-18
Abstract
In today's highly mobile world, people of all age groups constantly move between various locations, such as their homes, grocery stores, schools, and workplaces, reflecting the dynamic nature of modern society. The recent COVID-19 pandemic has underscored the profound impact of human mobility on the rapid spread of the virus within and between urban areas. The current study investigates the daily mobility behaviors of residents of the Republic of Cyprus. Through a comprehensive survey, the research documents travel patterns, commuting habits, and transportation preferences across various age groups and districts of Cyprus. With a total of 787 responses from all districts of Cyprus, data was collected through an online survey from 20/11/2023 to 20/02/2024. Data analysis includes descriptive methods and statistical modeling techniques in order to provide insights into the patterns of human mobility. Key findings include insights into travel distances, frequencies, duration and means of transportation, and timelines of mobility activities during typical workdays, as well as during weekends.
Highlights:
- 787 individuals responded to the online questionnaire.
- Human mobility timelines are extracted, depicting the typical location and activity of individuals every two hours during workdays, as well as during weekends.
- Across all age groups, people generally tend to travel short distances up to around 10 kilometers.
- Car usage is the predominant mode of transportation across all categories. Walking is the second most preferred mode and bus rides rank third.
Downloads
References
- Barbosa, H., Barthelemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., Menezes, R., Ramasco, J. J., Simini, F., & Tomasini, M. (2018). Human mobility: Models and applications. Physics Reports, 734, 1–74. https://doi.org/10.1016/j.physrep.2018.01.001
- Bhat, C. R., & Koppelman, F. S. (1999). Activity-Based Modeling of Travel Demand. In R. W. Hall (Ed.), Handbook of Transportation Science (Vol. 23, pp. 35–61). Springer US. https://doi.org/10.1007/978-1-4615-5203-1_3
- Boccaletti, S., Bianconi, G., Criado, R., Del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1–122. https://doi.org/10.1016/j.physrep.2014.07.001
- CEU. MOVE., Univ. Eiffel., TRT., Panteia., GDCC., & STRATEC. (2022). Study on new mobility patterns in European cities: Final report. Task A, EU wide passenger mobility survey. Publications Office. https://data.europa.eu/doi/10.2832/728583
- European Commission. (2014). Special Eurobarometer 406: Attitudes of Europeans towards urban mobility (Version v1.00) [Dataset]. http://data.europa.eu/88u/dataset/S1110_79_4_406
- European Commission. (2015). Special Eurobarometer 420: Passenger Rights (Version v1.00) [Dataset]. http://data.europa.eu/88u/dataset/S2011_82_1_420
- Fabbri, G., Federico, S., Fiaschi, D., & Gozzi, F. (2024). Mobility decisions, economic dynamics and epidemic. Economic Theory, 77(1–2), 495–531. https://doi.org/10.1007/s00199-023-01485-1
- Fayad, P., Hadjipetrou, S., Leventis, G., Kavroudakis, D., & Kyriakidis, P. (2023). Designing an Agent-Based Model for a City-Level Simulation of COVID-19 Spread in Cyprus: Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, 218–224. https://doi.org/10.5220/0012054000003546
- Gallotti, R., & Barthelemy, M. (2014). Anatomy and efficiency of urban multimodal mobility. Scientific Reports, 4(1), 6911. https://doi.org/10.1038/srep06911
- González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782. https://doi.org/10.1038/nature06958
- Hanson, S. (2005). Perspectives on the geographic stability and mobility of people in cities. Proceedings of the National Academy of Sciences, 102(43), 15301–15306. https://doi.org/10.1073/pnas.0507309102
- Hanson, S., & Huff, O. J. (1988). Systematic variability in repetitious travel. Transportation, 15(1–2). https://doi.org/10.1007/BF00167983
- Hunecke, M. (2009). ADD HOME. Mobility Management for housing areas—From car-dependency to free choice. https://trimis.ec.europa.eu/project/mobility-management-housing-areas-car-dependency-free-choice
- Kitamura, R., Chen, C., Pendyala, R. M., & Narayanan, R. (2000). Micro-simulation of daily activity-travel patterns for travel demand forecasting. Transportation, 27(1), 25–51. https://doi.org/10.1023/A:1005259324588
- Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. https://doi.org/10.1093/comnet/cnu016
- Kleinberg, J. (2007). The wireless epidemic. Nature, 449(7160), 287–288. https://doi.org/10.1038/449287a
- Kyriakidis, P., Kavroudakis, D., Fayad, P., Hadjipetrou, S., Leventis, G., & Papakonstantinou, A. (2021). Promoting the adoption of agent-based modelling for synergistic interventions and decision-making during pandemic outbreaks. AGILE: GIScience Series, 2, 1–5. https://doi.org/10.5194/agile-giss-2-44-2021
- Lessani, M. N., Li, Z., Jing, F., Qiao, S., Zhang, J., Olatosi, B., & Li, X. (2023). Human mobility and the infectious disease transmission: A systematic review. Geo-Spatial Information Science, 1–28. https://doi.org/10.1080/10095020.2023.2275619
- Levinson, D., & Wu, Y. (2005). The rational locator reexamined: Are travel times still stable? Transportation, 32(2), 187–202. https://doi.org/10.1007/s11116-004-5507-4
- Liang, X., Zhao, J., Dong, L., & Xu, K. (2013). Unraveling the origin of exponential law in intra-urban human mobility. Scientific Reports, 3(1), 2983. https://doi.org/10.1038/srep02983
- Litmeyer, M.-L., Gareis, P., Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR), Germany, Hennemann, S., & Department of Geography, Justus Liebig University Giessen, Gießen, Germany. (2023). Comparing student mobility pattern models. Euro-pean Journal of Geography, 14(1), 21–34. https://doi.org/10.48088/ejg.m.lit.14.1.21.34
- Marchetti, C. (1994). Anthropological invariants in travel behavior. Technological Forecasting and Social Change, 47(1), 75–88. https://doi.org/10.1016/0040-1625(94)90041-8
- Nicolaides, C., Cueto-Felgueroso, L., González, M. C., & Juanes, R. (2012). A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network. PLoS ONE, 7(7), e40961. https://doi.org/10.1371/journal.pone.0040961
- Palmer, J. R. B., Espenshade, T. J., Bartumeus, F., Chung, C. Y., Ozgencil, N. E., & Li, K. (2013). New Approaches to Human Mobility: Using Mobile Phones for Demographic Research. Demography, 50(3), 1105–1128. https://doi.org/10.1007/s13524-012-0175-z
- Pillai, A. N., Toh, K. B., Perdomo, D., Bhargava, S., Stoltzfus, A., Longini, I. M., Pearson, C. A. B., & Hladish, T. J. (2024). Agent-based modeling of the COVID-19 pandemic in Florida. Epidemics, 47, 100774. https://doi.org/10.1016/j.epidem.2024.100774
- Republic of Cyprus, Statistical Service. (2019). Census of Population and Housing [Dataset]. https://cystatdb.cystat.gov.cy/pxweb/en.
- Rhee, I., Shin, M., Hong, S., Lee, K., & Chong, S. (2008). On the Levy-Walk Nature of Human Mobility. IEEE INFOCOM 2008 - The 27th Conference on Computer Communications, 924–932. https://doi.org/10.1109/INFOCOM.2008.145
- Schlich, R., & Axhausen, K. W. (2003). Habitual travel behaviour: Evidence from a six-week travel diary. Transportation, 30(1), 13–36. https://doi.org/10.1023/A:1021230507071
- Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., & González, M. C. (2013). Unravelling daily human mobility motifs. Journal of The Royal Society Interface, 10(84), 20130246. https://doi.org/10.1098/rsif.2013.0246
- Simini, F., González, M. C., Maritan, A., & Barabási, A.-L. (2012). A universal model for mobility and migration patterns. Nature, 484(7392), 96–100. https://doi.org/10.1038/nature10856
- Statistical service of the Republic of Cyprus. (2010). Short distance passenger mobility survey 2009. Statistical Service of Cyprus. https://library.cystat.gov.cy/Documents/Publication/PASSENGER_MOBILITY_SURVEY09-120810.pdf
- Ukkusuri, S. V., Mathew, T. V., & Waller, S. T. (2007). Robust Transportation Network Design Under Demand Uncertainty. Computer-Aided Civil and Infrastructure Engineering, 22(1), 6–18. https://doi.org/10.1111/j.1467-8667.2006.00465.x
- Varga, L., Kovács, A., Tóth, G., Papp, I., & Néda, Z. (2016). Further We Travel the Faster We Go. PLOS ONE, 11(2), e0148913. https://doi.org/10.1371/journal.pone.0148913
- Vorel, J. (2023). Strengths and weaknesses of the micro-simulation approach to analysis of residential mobility. European Journal of Geography, 6(2), 69–84. https://www.eurogeojournal.eu/index.php/egj/article/view/415
- Zehavi, Y. (1977). The" UMOT" Model. Urban Projects Department [of] the World Bank.