Article Sidebar

Download
PDF
Statistic
Read Counter : 193 Download : 221

Main Article Content

Abstract

Spatio-temporal data modelling is one of the methods in data analysis that uses space (spatial) and time (temporal) approaches. This study used Spatio-temporal statistical modelling to observe the daily activity patterns of people. Spatio-temporal modelling selected for support activity-based transportation demand. This research identifies community mobility patterns that will provide trip production data for transportation demand prediction. Using Spatio-temporal statistical modelling benefit this study because statistical this model can make model components in a physical system appearing to be random. Even if they are not, the models are helpful as they have accurate and precise predictions. In this study, descriptive analysis was used. Incorporating statistical distributions into the model is a natural way to solve the problem. This research collects daily activity data from 400 respondents recorded every 15 minutes. From this data, a pattern of respondents’ daily activities was formed, which was analyzed using R. Software R also visualizes data on daily activities of the community in Spatio-temporal modelling. This research aims to depict the daily activity patterns to predict trip production. This research found three clusters of trip production patterns with specific groups member and specific patterns between workdays and holidays.

Keywords

Daily activity Rstudio Spatio-temporal Trip production pattern

Article Details

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
How to Cite
Willdan, M. ., Ramadhan, R. I., Kresnanto, N. C. ., & Putri, W. H. (2023). Exploring Daily Activity Pattern Using Spatio-Temporal Statistics with R for Predicting Trip Production . Enthusiastic : International Journal of Applied Statistics and Data Science, 3(1), 59–73. https://doi.org/10.20885/enthusiastic.vol3.iss1.art6
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. R.N. Buliung, M.J. Roorda, and T.K. Remmel, “Exploring spatial variety in patterns of activity-travel behavior: Initial results from the Toronto Travel-Activity Panel Survey (TTAPS),” Transportation, vol. 35, no. 6, pp. 697–722, 2008, doi: 10.1007/s11116-008-9178-4.
  2. Y. Li, L. Yang, H. Shen, and Z. Wu, “Modeling Intra-Destination Travel Behavior of Tourists Through Spatio-Temporal Analysis,” Journal of Destination Marketing & Management, vol. 11, pp. 260–269, 2019, doi: 10.1016/j.jdmm.2018.05.002.
  3. K. Hamad, P.T.T. Htun, and L. Obaid, “Characterization of Travel Behavior at a University Campus: A Case Study of Sharjah University City, UAE,” Transportation Research Interdisciplinary Perspectives, vol. 12, pp. 1–14, 2021, doi: 10.1016/j.trip.2021.100488.
  4. R.G. Golledge and T. Gärling, “Spatial Behavior in Transportation Modeling and Planning Reginald,” in Transportation and Engineering Handbook, K. Goulias, Ed. pp. 250–260, 2001.
  5. K. Vrotsou, K. Ellegård, and M. Cooper, “Exploring Time Diaries Using Semi-Automated Activity Pattern Extraction,” Journal of Time Use Research, vol. 6, no. 1, pp. 1–25, 2009, doi: 10.13085/eijtur.6.1.1-25.
  6. M.H. Hafezi, L. Liu, and H. Millward, “A Time-Use Activity-Pattern Recognition Model for Activity-Based Travel Demand Modeling,” Transportation, vol. 46, no. 4, pp. 1369–1394, 2019, doi: 10.1007/s11116-017-9840-9.
  7. N.S. Daisy, H. Millward, and L. Liu, “Trip Chaining and Tour Mode Choice of Non-Workers Grouped by Daily Activity Patterns,” Journal of Transport Geography, vol. 69, pp. 150–162, 2018, doi: 10.1016/j.jtrangeo.2018.04.016.
  8. S. Dasgupta, S. Lall, and D. Wheeler, “Spatiotemporal Analysis of Traffic Congestion, Air Pollution, and Exposure Vulnerability in Tanzania,” Science of The Total Environment, vol. 778, pp. 1–12, 2021, doi: 10.1016/j.scitotenv.2021.147114.
  9. X. Wei, Y. Ren, L. Shen, and T. Shu, “Exploring the Spatiotemporal Pattern of Traffic Congestion Performance of Large Cities in China: A Real-Time Data Based Investigation,” Environmental Impact Assessment Review, vol. 95, pp. 1–16, 2022, doi: 10.1016/j.eiar.2022.106808.
  10. H. Zhang, Y. Liu, B. Shi, J. Jia, W. Wang, and X. Zhao, “Analysis of Spatial-Temporal Characteristics of Operations in Public Transport Networks Based on Multisource Data,” Journal of Advanced Transportation, vol. 2021, pp. 1–15, 2021, doi: 10.1155/2021/6937228.
  11. F. Jiang, K. Thilakarathna, M.A. Kaafar, F. Rosenbaum, and A. Seneviratne, “A Spatio-Temporal Analysis of Mobile Internet Traffic in Public Transportation Systems: A View of Web Browsing from The Bus,” in Proceedings of the 10th ACM MobiCom Workshop on Challenged Networks, 2015, pp. 37–42, doi: 10.1145/2799371.2799379.
  12. L. Wang, Z. Zhao, X. Xue, and Y. Wang, “Spillover Effects of Railway and Road on CO2 Emission in China: A Spatiotemporal Analysis,” Journal of Cleaner Production, vol. 234, pp. 797–809, 2019, doi: 10.1016/j.jclepro.2019.06.278.
  13. W. Li, Z. Pu, Y. Li, and M. Tu, “How Does Ridesplitting Reduce Emissions From Ridesourcing? A Spatiotemporal Analysis in Chengdu, China,” Transportation Research Part D: Transport and Environment, vol. 95, pp. 1–14, 2021, doi: 10.1016/j.trd.2021.102885.
  14. A.D.A. Tasci and Y.J. Ko, “Travel Needs Revisited,” Journal of Vacation Marketing, vol. 23, no. 1, pp. 20–36, 2017, doi: 10.1177/1356766715617499.
  15. M. Kadarisman, “Transportation System and Human Needs in a Family,” Jurnal Manajemen Transportasi dan Logistik, vol. 2, no. 3, pp. 313–331, 2017, doi: 10.25292/j.mtl.v2i3.113.
  16. A. Bull, Traffic Congestion - The Problem and How to Deal with it?. Santiago, Chile: United Nations Economic Commission for Latin America and the Caribbean, 2004.
  17. C.R. Bhat and F.S. Koppelman, “Activity-Based Modeling of Travel Demand,” in Handbook of Transportation Science, R.W. Hall, Ed. Berlin, Germany: Springer Science+Business Media, 1999, pp. 35–61, doi: 10.1007/978-1-4615-5203-1_3.
  18. S. Phithakkitnukoon and R. Shibasaki, “Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data,” Human Behavior Understanding, 2010, pp. 14–25, 2010, doi: 10.1007/978-3-642-14715-9.
  19. C. Vinothini, V. Saravanabavan, and D. Balaji, “Travel Pattern of Health Utilization to Primary Health Care Centres in Madurai District,” International Journal of Geography, Geology and Environment, vol. 3, no. 2, pp. 144–151, 2021.
  20. J.M. Chen, J.F. Petrick, A. Papathanassis, and X. Li, “A Meta-Analysis of the Direct Economic Impacts of Cruise Tourism on Port Communities,” Tourism Management Perspectives, vol. 31, pp. 209–218, 2019, doi: 10.1016/j.tmp.2019.05.005.
  21. L. Zheng et al., “Spatial-Temporal Travel Pattern Mining Using Massive Taxi Trajectory Data,” Physica A: Statistical Mechanics and its Applications, vol. 501, pp. 24–41, 2018, doi: 10.1016/j.physa.2018.02.064.
  22. R. Safitri and R. Amelia, “Forecasting Travel Patterns During COVID-19 Period Using Community Mobility Report Case study: Bangka Belitung Province,” IOP Conference Series: Earth and Environmental Science, 2021, pp. 1–7, doi: 10.1088/1755-1315/926/1/012055.
  23. P. Christidis and A. Christodoulou, “The Predictive Capacity of Air Travel Patterns During the Global Spread of the COVID-19 Pandemic: Risk, Uncertainty And Randomness,” International Journal of Environmental Research and Public Health, vol. 17, no. 10, pp. 1–15, 2020, doi: 10.3390/ijerph17103356.
  24. C. Henning-smith, A. Evenson, K. Kozhimannil, and I. Moscovice, “Geographic Variation in Transportation Concerns and Adaptations to Travel-Limiting Health Conditions in the United States,” Journal of Transport & Health, vol. 8, pp. 137–145, 2018, doi: 10.1016/j.jth.2017.11.146.
  25. Y. Xu, A. Belyi, I. Bojic, and C. Ratti, “Human Mobility and Socioeconomic Status : Analysis of Singapore and Boston,” Computers , Environment and Urban Systems, vol. 72, pp. 51–67, 2018, doi: 10.1016/j.compenvurbsys.2018.04.001.
  26. I. Politis et al., “Mapping Travel Behavior Changes During the COVID-19 Lock-Down : A Socioeconomic Analysis in Greece,” European Transport Research Review, vol. 13, no. 21, pp. 1–19, 2021, doi: 10.1186/s12544-021-00481-7.
  27. Y. Zhang, N.S. Aslam, J. Lai, and T. Cheng, “You Are How You Travel : A Multi-Task Learning Framework for Geodemographic Inference Using Transit Smart Card Data,” Computers , Environment and Urban Systems, vol. 83, pp. 1–15, 2020, doi: 10.1016/j.compenvurbsys.2020.101517.
  28. A. Frihida, D.J. Marceau, and M. Thériault, “Spatio-Temporal Object-Oriented Data Model for Disaggregate Travel Behavior,” Transactions in GIS, vol. 6, no. 3, pp. 277–294, 2002, doi: 10.1111/1467-9671.00111.
  29. B.Y. Chen, H. Yuan, Q. Li, S.L. Shaw, W.H.K. Lam, and X. Chen, “Spatiotemporal Data Model for Network Time Geographic Analysis in the Era of Big Data,” International Journal of Geographical Information Science, vol. 30, no. 6, pp. 1041–1071, 2016, doi: 10.1080/13658816.2015.1104317.
  30. S. Li, C. Zhuang, Z. Tan, F. Gao, Z. Lai, and Z. Wu, “Inferring the Trip Purposes and Uncovering Spatio-Temporal Activity Patterns from Dockless Shared Bike Dataset in Shenzhen , China,” Journal of Transport Geography, vol. 91, pp. 1–14, 2021, doi: 10.1016/j.jtrangeo.2021.102974.
  31. M. Delafontaine, T. Neutens, and N.V.D. Weghe, “Modelling Potential Movement in Constrained Travel Environments Using Rough Space-Time Prisms,” International Journal of Geographical Information Science, vol. 25, no. 9, pp. 1389–1411, 2011, doi: 10.1080/13658816.2010.518571.
  32. M.P. Kwan, “Interactive Geovisualization of Activity-Travel Patterns Using Three-Dimensional Geographical Information Systems: A Methodological Exploration with a Large Data Set,” Transportation Research Part C: Emerging Technologies, vol. 8, no. 1–6, pp. 185–203, 2000, doi: 10.1016/S0968-090X(00)00017-6.
  33. K.P. Sinaga and M.S. Yang, “Unsupervised K-Means Clustering Algorithm,” IEEE Access, vol. 8, pp. 80716–80727, 2020, doi: 10.1109/ACCESS.2020.2988796.
  34. R.A. Becker, J.M. Chambers, and A.R. Wilks, The New S Language: A Programming Environment for Data Analysis and Graphics. London, England: Chapman & Hall, 1988.
  35. M. Faisal, E.M. Zamzami, and Sutarman, “Comparative Analysis of Inter-Centroid K-Means Performance using Euclidean Distance, Canberra Distance and Manhattan Distance,” Journal of Physics: Conference Series, vol. 1566, no. 1, pp. 1–7, 2020, doi: 10.1088/1742-6596/1566/1/012112.