Analysis of travel demand variability based on social media data: a case study in the city of Fortaleza
Análise da variabilidade da demanda de viagens com base em dados de redes sociais: um estudo de caso na cidade de Fortaleza
Palavras-chave:
Variability, Social media, MobilityResumo
Understanding the variability in citizens' travel behavior within a city is essential for comprehending urban mobility. However, obtaining daily data on these journeys through traditional methods is expensive. Although alternative methods, such as using social media data, are widely employed in the academic community, they have contributed little due to the low representativeness of the data and the lack of efforts to understand user behavior. This study fills this gap by analyzing travel variability in Fortaleza using social media data from platforms like Instagram and Twitter. Employing empirical and parametric statistical techniques, such as box plots and statistical tests (ANOVA, Kruskal-Wallis, and Tukey), hypotheses based on a literature review analysis were validated. The results revealed patterns similar to those observed in daily life and also yielded unexpected conclusions about the phenomenon. Despite limitations regarding data representativeness, important insights were obtained for understanding daily travel patterns in Fortaleza.
Downloads
Referências
Ahas, R.; Aasa, A.; Silm, S.; Tiru, M. Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data. Transportation Research Part C: Emerging Technologies, v. 18, n. 1, p. 45-54, 2010.
Allstrom, A.; Kristoffersson, I.; Susilo, Y. Smartphone based travel diary collection: Experiences from a field trial in Stockholm. Transportation research procedia, v. 26, p. 32-38, 2017.
Bartosiewicz, B.; Pielesiak, I. Spatial patterns of travel behaviour in Poland. Travel Behaviour and Society, 15, 113-122, 2019.
Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), v. 57, n. 1, p. 289-300, 1995.
Dharmowijoyo, D. B.; Susilo, Y. O.; Karlstrom, A.; Adiredja, L. S. Collecting a multi-dimensional three-weeks household time-use and activity diary in the Bandung Metropolitan Area, Indonesia. Transportation Research Part A: Policy and Practice, v. 80, p. 231-246, 2015.
Elango, V. V.; Guensler, R.; Ogle, J. Day-to-day travel variability in the Commute Atlanta, Georgia, study. Transportation research record, n. 1, p. 39-49, 2007.
Hanson, S.; Huff, J. O. Assessing day-to-day variability in complex travel patterns. Transportation Research Record, v. 891, p. 18-24, 1981.
Jones, P.; Clarke, M. The significance and measurement of variability in travel behaviour. Transportation, v. 15, n. 1-2, p. 65-87, 1988.
Koppelman, F. S.; Pas, E. I. Estimation of disaggregate regression models of person trip generation with multiday data. Papers presented during the Ninth International Symposium on Transportation and Traffic Theory held in Delft the Netherlands, p. 11-13, 1984.
Marble, Duane F.; Bowlby, S. R. Shopping alternatives and recurrent travel patterns. Geographic studies of urban transportation and network analysis, p. 42-75, 1968.
Motahari, S.; Zang, H.; Reuther, P. The impact of temporal factors on mobility patterns. In: 2012 45th Hawaii International Conference on System Sciences. IEEE, p. 5659-5668, 2012.
Oliveira, M. V. A natureza dos padrões de variação espaço-temporal do volume veicular em ambiente urbano: estudo de caso em Fortaleza. Programa de Pós-graduação em Engenharia de Transportes–PETRAN. Universidade Federal do Ceará, Fortaleza, 2004.
Oliveira, S. F. C.; Silva, C. A. U. Caracterização de padrões de deslocamentos urbanos em Fortaleza com a utilização de dados de redes sociais georreferenciadas. Revista Transportes (Rio de Janeiro), v. 28, p. 136, 2020.
Pas, E. I. Multiday samples, parameter estimation precision, and data collection costs for least squares regression trip-generation models. Environment and Planning A, v. 18, n. 1, p. 73-87, 1986.
Pas, E. I.; Sundar, S. Intrapersonal variability in daily urban travel behavior: some additional evidence. Transportation, v. 22, n. 2, p. 135-150, 1995.
Pendyala, R. M.; Pas, E. I. Multi-day and multi-period data for travel demand analysis and modeling, 2000.
Raux, C.; MA, T.; Cornelis, E. Variability in daily activity-travel patterns: the case of a one-week travel diary. European transport research review, v. 8, n. 4, p. 26, 2016.
Schlich, R.; Axhausen, K. W. Habitual travel behaviour: evidence from a six-week travel diary. Transportation, v. 30, n. 1, p. 13-36, 2003.
Shapcott, M. Comparison of the use of time in Reading, England with time use in other countries. Transactions of the Martin Centre for Architectural and Urban Studies, v. 3, p. 231-257, 1978.
Stopher, P. R.; Zhang, Y. Repetitiveness of daily travel. Transportation Research Record, v. 2230, n. 1, p. 75-84, 2011.
Susilo, Y. O.; Kitamura, R. Analysis of day-to-day variability in an individual's action space: exploration of 6-week Mobidrive travel diary data. Transportation Research Record, v. 1902, n. 1, p. 124-133, 2005.
Tarigan, Ari, K.; Kitamura, R. Week-to-week leisure trip frequency and its variability. Transportation Research Record, v. 2135, n. 1, p. 43-51, 2009.
Wright, T.; Hu, P.; Young. J.; Lu, A. Variability in Traffic Monitoring Data: Final Summary Report. 1997.
Xianyu, J.; Rasouli, S.; Timmermans, H. Analysis of variability in multi-day GPS imputed activity-travel diaries using multi-dimensional sequence alignment and panel effects regression models. Transportation, v. 44, n. 3, p. 533-553, 2017.
Zhong, C.; Manley, E.; Arisona, S. M.; Batty, M.; Schmitt, G. Measuring variability of mobility patterns from multiday smart-card data. Journal of Computational Science, v. 9, p. 125-130, 2015.