Journals / Gazi University Journal of Science / 2026 / Volume: 38 Issue: 2

Integration of GPS Based Floating Car Data into GIS Based Transportation Networks: Antalya Ring Road Example

Ersin Aksoy(Republic of Turkey Ministry of Environment, Urbanisation and Climate Change - Infrastructure and Urban Transformation Services, Antalya, Turkey)0000-0002-3428-5740
Kıvanç Ertuğay(AKDENIZ UNIVERSITY, FACULTY OF ARCHITECTURE, DEPARTMENT OF URBAN AND REGIONAL PLANNING)0000-0001-9593-3548
Engin Kepenek(AKDENIZ UNIVERSITY, FACULTY OF ARCHITECTURE, DEPARTMENT OF URBAN AND REGIONAL PLANNING)0000-0001-5551-8008
Pages
—
Publication Date
2025-06-01
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Developed method was applied to a particular segment of a ring road of D400 highway within the borders of Antalya city centre and the whole process is described in a detailed manner in order to demonstrate the efficiency and usability of the proposed methodology.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Although there are many studies on the use of GPS and GIS technologies in transportation planning and accessibility analyses, there is almost few research to reveal the background of the integration process in detail.
📊
Key Findings & Data:Addressing this basic problem, our study attempted to develop a free and transparent method to integrate mobile GPS data into transportation network data on GIS environment which can be used extensively in urban and regional planning research especially in transportation and accessibility analyses.
💡
Conclusion & Impact:Developed method was applied to a particular segment of a ring road of D400 highway within the borders of Antalya city centre and the whole process is described in a detailed manner in order to demonstrate the efficiency and usability of the proposed methodology.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:GPS (Global Positioning System) · GIS (Geographical Information Systems) · Floating Car Data

Abstract

Although there are many studies on the use of GPS and GIS technologies in transportation planning and accessibility analyses, there is almost few research to reveal the background of the integration process in detail. Addressing this basic problem, our study attempted to develop a free and transparent method to integrate mobile GPS data into transportation network data on GIS environment which can be used extensively in urban and regional planning research especially in transportation and accessibility analyses. Developed method was applied to a particular segment of a ring road of D400 highway within the borders of Antalya city centre and the whole process is described in a detailed manner in order to demonstrate the efficiency and usability of the proposed methodology.

🔖

Cite This Article

Aksoy, E., Ertuğay, K., Kepenek, E. (2025). Integration of GPS Based Floating Car Data into GIS Based Transportation Networks: Antalya Ring Road Example. Gazi University Journal of Science, Volume: 38 Issue: 2. https://doi.org/10.35378/gujs.1529123

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (55)

  1. Zhu, X., Liu, S., “Analysis of the impact of the MRT system on accessibility in Singapore using an integrated GIS tool”, Journal of Transport Geography, 12(2): 89–101, (2004). DOI: https://doi.org/10.1016/j.jtrangeo.2003.10.003
  2. Foth, N., Manaugh, K., and El-Geneidy, A., “Towards equitable transit: Examining transit accessibility and social need in Toronto, Canada”, Journal of Transport Geography, 29: 1–10, (2013). DOI: https://doi.org/10.1016/j.jtrangeo.2012.12.008
  3. Geurs, K. T., Wee, B., “Land-use/transport Interaction Models as Tools for Sustainability Impact Assessment of Transport Investments: Review and Research Perspectives”, European Journal of Transport and Infrastructure Research, 4(3): 333–355, (2004). DOI: http://dx.doi.org/10.18757/ejtir.2004.4.3.4272
  4. Alonso, W., “Location and land use”, First Edition. Massachusetts: Harvard University Press, (1964).
  5. Anas, A., “Residential location markets and urban transportation: Economic theory, econometrics, and policy analysis with discrete choice models”, New York, Academic Press, (1982).
  6. Wegener, M., “Overview of Land Use Transport Models”, Hensher, D.A., Button, K.J., Haynes, K.E. and Stopher, P.R. (Ed.) Handbook of Transport Geography and Spatial Systems, Emerald Group Publishing Limited, Leeds, 5: 127–146, (2004). DOI: https://doi.org/10.1108/9781615832538-009
  7. Gerçek, H., Tekin, İ., “Sürdürülebilir kalkınma ve kentsel ulaşım sistemleri planlaması: İstanbul için bir değerlendirme”, İstanbul 2020 Sempozyumu, İstanbul, 17 April, 139–152, (1996).
  8. Beattie, C., Longhurst, J., “Progress with implementing local air-quality management in rural areas of England”, Journal of Environmental Management, 61(2): 137–147, (2001). DOI: https://doi.org/10.1006/jema.2000.0391
  9. Demirel, H., “An integrated approach to the conceptual data modeling of an entire highway agency Geographic Information System (GIS)”, Verlag der Bayerischen Akademie der Wissenschaften in Kommission bei der C.H. Beck’schen Verlagsbuchhandlung München, (2002). DOI: https://doi.org/10.23689/fidgeo-401
  10. Buliung, R. N., Kanaroglou, P. S., “A GIS toolkit for exploring geographies of household activity/travel behavior”, Journal of Transport Geography, 35–51, (2006). DOI: https://doi.org/10.1016/j.jtrangeo.2004.10.008
  11. Lopez-Ruiz. H. G., Christidis, P., Demirel, H., and Kompil, M., “Quantifying the Effects of Sustainable Urban Mobility Plans”, Publications Office of the European Union, (2013). DOI: https://dx.doi.org/10.2791/21875
  12. Brandi, A., Gori, S., Nigro, M., and Petrelli, M., “Development of an Integrated Transport-Land Use Model for the Activities Relocation in Urban Areas”, Transportation Research Procedia, 3: 374–383, (2014). DOI: https://doi.org/10.1016/j.trpro.2014.10.018
  13. Hawkins, J., Habib, K. N., “Integrated models of land use and transportation for the autonomous vehicle revolution”, Transport Reviews, (2018). DOI: 10.1080/01441647.2018.1449033
  14. Boyce, D. E., “A framework for constructing network equilibrium model of urban location”, Transportation Science, 14: 77–96, (1980). DOI: https://doi.org/10.1287/trsc.14.1.77
  15. Wegener, M., Fuerst, F., “Land-Use Transportation Interaction”, State of the Art, Deliverable 2a of the project TRANSLAND of the 4th RTD Framework Programme of the European Commission, Institut fuer Raumplannung, Universitaet Dortmund, (1999). DOI: http://dx.doi.org/10.2139/ssrn.1434678
  16. Barrera, R., Frank, A., Al-taha, K., “Temporal relations in geographic information systems”, National Center for Geographic Information and Analysis, ACM Sigmod Record, 20: 85–93, (1991).
  17. Langran, G., “Issues of implementing a spatio-temporal system”, International Journal of GIS, 7(4): 305–314, (1993). DOI: https://doi.org/10.1080/02693799308901963
  18. Erwig, M., Guting, R., Schneider, M., Vazirigiannis, M., “Abstract and discrete modelling of spatio-temporal data types”, Geoinformatica, 3(3): 269–296, (1999).
  19. Hornsby, K., Egenhofer, M., “Identity-based change: A foundation for spatio-temporal knowledge representation”, International Journal of Geographical Information Science, 14(3): 207–224, (2000). DOI: https://doi.org/10.1080/136588100240813
  20. Molenaar, M., Cheng, T., “Fuzzy Spatial Objects and Their Dynamics”, ISPRS Journal of Photogrammetry and Remote Sensing, 55(3): 164–175, (2000). DOI: https://doi.org/10.1016/S0924-2716(00)00017-4
  21. Demirel, H., “A dynamic multi-dimensional conceptual data model proposal for the transportation applications”, ISPRS Journal of Photogrammetry and Remote Sensing, 58(5-6): 301–314, (2004). DOI: https://doi.org/10.1016/j.isprsjprs.2004.02.002
  22. Yuan, M., Mark, D. M., Egenhofer, M. J., and Peuquet, D. J., “Extensions to geographic representations”, A Research Agenda for Geographic Information Science, Editors: McMaster, R. B. & Usery, E. L. & CRC Press. New York, (2004). DOI: http://dx.doi.org/10.1201/9781420038330.ch5
  23. Horner, M. W., Schleith, D., “Analyzing temporal changes in land-use–transportation relationships: A LEHD-based approach”, Applied Geography, 35(1-2): 491–498, (2012). DOI: https://doi.org/10.1016/j.apgeog.2012.09.006
  24. Curtis, C., Scheurer, J., “Planning for Public Transport Accessibility”, An International Sourcebook, First Edition, Routledge, New York, (2016).
  25. Gonzales, M. C., Hidalgo, C. A., and Barabasi, A., “Understanding individual human mobility patterns”, Nature, 453: 779–782, (2008). DOI: https://doi.org/10.1038/nature06958
  26. Pappalardo, L., Rinzivillo, S., Qu, Z., Pedreschi, D., and Giannotti, F., “Understanding the patterns of car travel”, European Physical Journal Special Topics, 215(1): 61–73, (2013). DOI: https://doi.org/10.1140/epjst/e2013-01715-5
  27. Spinsanti, L., Berlingerio, M., and Pappalardo, L., “Mobility and geo-social networks”, Mobility data modeling, management, and understanding, Published online by Cambridge University Press: 315–333, (2013). DOI: http://dx.doi.org/10.1017/CBO9781139128926.017
  28. Gao, S., “Spatio-Temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics in the Mobile Age”, Spatial Cognition & Computation, 15(2): 86–114, (2015). DOI: http://dx.doi.org/10.1080/13875868.2014.984300
  29. Barbosa, H., Barthelemy, M., Ghoshal, G., James C. R., Lenormand, M., Louail, T., Menezes, R., Ramasco, J. J., Simini, F., and Tomasini, M., “Human mobility: models and applications”, Physics Reports, 734: 1–74, (2017). DOI: https://doi.org/10.1016/j.physrep.2018.01.001
  30. Zhang, K., Sun, D., Shen, S., and Zhu, Y., “Analysing spatiotemporal congestion pattern on urban roads based on taxi GPS data”, The Journal of Transport and Land Use, 10(1): 675–694, (2017). DOI: https://doi.org/10.5198/jtlu.2017.954
  31. Faroqi, H., Mesbah, M., and Kim, J., “Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data”, Journal of Advanced Transportation, 2017(1): 1–14, (2017). DOI: https://doi.org/10.1155/2017/1318945
  32. Kim, M., Kim, S., and Sohn, H., “Relationship between Spatio-Temporal Travel Patterns Derived from Smart-Card Data and Local Environmental Characteristics of Seoul, Korea”, Sustainability, 10(3): 787, (2018). DOI: http://dx.doi.org/10.3390/su10030787
  33. Nguyen, L. N., Scherer, W., “Imputation Techniques to Account for Missing Data in Support of Intelligent Transportation Systems Applications”, Research Report No. UVACTS-13-0-78, Center for Transportation Studies, University of Virginia, 1–124, (2003).
  34. Shiode, N., Shiode, S., “Street-level Spatial Interpolation Using Network-based IDW and Ordinary Kriging”, Transactions in GIS, 15(4): 457–477, (2011). DOI: http://dx.doi.org/10.1111/j.1467-9671.2011.01278.x
  35. Zou, H., Yue, Y., Li, Q., and Yeh, A. G., “An improved distance metric for the interpolation of link based traffic data using kriging: a case study of a large-scale urban road network”, International Journal of Geographical Information Science, 26(4): 667–689, (2012). DOI: https://doi.org/10.1080/13658816.2011.609488
  36. Menga, Q., Liu, Z., Borders, B. E., “Assessment of regression kriging for spatial interpolation – comparisons of seven GIS interpolation methods”, Cartography and Geographic Information Science, 40(1): 28–39, (2013). DOI: http://dx.doi.org/10.1080/15230406.2013.762138
  37. Shamo, B., Asa, E., and Membah, J., “Linear Spatial Interpolation and Analysis of Annual Average Daily Traffic Data”, Journal of Computing in Civil Engineering, 29(1): 04014022-1/8, (2012). DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000281
  38. Eom, J., Park, M., Heo, T. Y., and Huntsinger, L., “Improving the Prediction of Annual Average Daily Traffic for Nonfreeway Facilities by Applying a Spatial Statistical Method”, Transportation Research Record: Journal of the Transportation Research Board, 20–29, (2006). DOI: https://doi.org/10.1177/0361198106196800103
  39. Selbya, B., Kockelman, K. M., “Spatial prediction of traffic levels in unmeasured locations: applications of universal kriging and geographically weighted regression”, Journal of Transport Geography, 29(1): 24–32, (2013). DOI: 10.1016/j.jtrangeo.2012.12.009
  40. Wang, XG., Kostyniuk, L., and Barnes, M., “Built environment and driving outcomes: the case for an integrated GIS/GPS approach”, International Journal of Applied Geospatial Research, 5(2): 11–29. (2014). DOI: 10.4018/ijagr.2014040102
  41. Yang, Z., Franz, ML., Zhu, S., Mahmoudi, J., Nasri, A., and Zhang, L., “Analysis of Washington, DC taxi demand using GPS and land-use data”, Journal of Transport Geography, 66: 35–44, (2018). DOI: https://doi.org/10.1016/j.jtrangeo.2017.10.021
  42. Schamel, J., Job, H., “National Parks and demographic change - Modelling the effects of ageing hikers on mountain landscape intra-area accessibility”, Landscape And Urban Planning, 163: 32–43, (2017). DOI: https://doi.org/10.1016/j.landurbplan.2017.03.001
  43. Wang, J., Kwan, MP., and Chai, YW., “An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago”, International Journal of Environmental Research and Public Health, 15(4): 703, (2018). DOI: https://doi.org/10.3390/ijerph15040703
  44. Matthew, J. B., Darren, M. S., and Antonio, P., “A spatial modeling approach to estimating bike share traffic volume from GPS data”, Sustainable Cities and Society, 76: 103401, (2022). DOI: http://dx.doi.org/10.1016/j.scs.2021.103401
  45. Mehrdad, H., Sharareh, P., and Mazlin, Bin M., “GIS-based modeling for location planning of jetties in coastal towns”, Ocean & Coastal Management, 56: 17–25, ISSN 0964-5691, (2012). DOI: https://doi.org/10.1016/j.ocecoaman.2011.10.010
  46. Arias-Molinares, D., García-Palomares, J.C., Romanillos, G., and Javier, G., “Uncovering spatiotemporal micromobility patterns through the lens of space–time cubes and GIS tools”. Journal of Geographical Systems, 25: 403–427, (2023). DOI: https://doi.org/10.1007/s10109-023-00418-9
  47. Liu, D; Kan, ZH., Mei-Po, K., and Tang, LL., “Space-Time Analysis of Refueling Patterns of Alternative Fuel Vehicles Using GPS Trajectory Data and Machine Learning”, Transactions In Gis, (Early Access), (2024). DOI: https://doi.org/10.1111/tgis.13258
  48. Gary, H., Renee, Z., Jonathan, C., Mitchel, L., and Richard, F., “Modelling spatial access to General Practitioner surgeries: Does public transport availability matter?”, Journal of Transport & Health, 6: 143–154, ISSN 2214-1405, (2017). DOI: https://doi.org/10.1016/j.jth.2017.05.361
  49. Campos-Sánchez, FS., Valenzuela-Montes, LM., and Abarca-Alvarez, FJ., “Evidence of Green Areas, Cycle Infrastructure and Attractive Destinations Working Together in Development on Urban Cycling”, Sustainability, 11(17): 4730, (2019). DOI: https://doi.org/10.3390/su11174730
  50. Vergel-Tovar, C. E., Goldwyn, E., and Leape, J., “Digital traces: Mapping Bogotá’s unmapped transit network using smartphones and networked databases”. Environment and Planning B: Urban Analytics and City Science, 50(2): 469–486, (2023). DOI: https://doi.org/10.1177/23998083221117831
  51. Desley, V., Laurie, B., and Rosemary, A., “The use of amenities in high density neighbourhoods by older urban Australian residents”, Landscape and Urban Planning, 107(2): 159–171, ISSN 0169-2046, (2012). DOI: https://doi.org/10.1016/j.landurbplan.2012.05.013.
  52. Tianyi, W., Yan, L., I-Ting, C., Weijie, Q., Jing, J., and Lee, B., “Evaluating the 15-minute city paradigm across urban districts: A mobility-based approach in Hamilton, New Zealand”, Cities, 151, 105147, ISSN 0264-2751, (2024). DOI: https://doi.org/10.1016/j.cities.2024.105147
  53. Sun, Y., Ren, Y., and Sun, X., "Uber Movement Data: A Proxy for Average One-way Commuting Times by Car" ISPRS International Journal of Geo-Information, 9(3), 184, (2020). DOI: https://doi.org/10.3390/ijgi9030184
  54. Zou, H., Yue, Y., Li, Q., and Yeh, A. G, O. “An improved distance metric for the interpolation of link-based traffic data using kriging: a case study of a large-scale urban road network”. International Journal of Geographical Information Science, 26(4): 667–689, (2011). DOI: https://doi.org/10.1080/13658816.2011.609488
  55. Ertugay, K., Duzgun, S., “GIS-based stochastic modeling of physical accessibility using GPS-based floating car data and Monte Carlo simulation”, International Journal of Geographical Information Science, 25(9): 1491–1506, (2011). DOI: https://doi.org/10.1080/13658816.2010.528419