Journal of the Architectural Institute of Korea Planning & Design (대한건축학회논문집:계획계)

Architectural Institute of Korea (대한건축학회)
권호 표지
DOI QR코드

DOI QR Code

Traffic Congestion Prediction System Using the Urban Data and Space Syntax

도시정보와 Space Syntax를 활용한 도로혼잡구간 예측

  • 송유미 (성균관대학교 미래도시융합공학과) ;
  • 김성아 (성균관대학교 건축학과)
  • Received : 2016.09.26
  • Accepted : 2016.12.20
  • Published : 2016.12.30
⟨ Previous Next ⟩

Abstract

Many cars cause the urban problems, such as environmental pollution and safety accidents and traffic congestion. This paper aim to prove that traffic congestion section is predicted by road layout with traffic data. Space syntax is used to find the spatial configuration of road; integration and angular connectivity. And the travel speed and traffic volume data of target road are acquired from public data (open data). Travel speed, traffic volume, integration and angular connectivity are across-correlated. So the relationship among them and the elements which affect the travel speed are founded. And then the regression analysis is implemented to examine that the elements predict the congestion section of road. The result of regression analysis report that vehicle's speed is affected by traffic volume, integration, and angular connectivity. Predicting the traffic congestion using the spatial configuration, people can prepare measures against the traffic congestion of newly constructed road.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. Bafna, S. (2003). Space Syntax A brief Introduction to Its Logic and Analytical Techniques, ENVIRONMENT AND BEHAVIOR, 35(1), 17-29. https://doi.org/10.1177/0013916502238863
  2. DepthmapX, https://varoudis.github.io/depthmapX/ [Visit:2016.08.11]
  3. Jeon, J. (2013). Forecasting method of traffic volume in closed-freeway by using TCS, Thesis, Hanyang University.
  4. Jeon, S. (2012). A Congestion Detection Algorithm based on Expressway DSRC Data, Thesis, Ajou University.
  5. Jeong, D. (2014). (An) algorithm for identifying congested traffic states by traffic big data analytics, Thesis, Pusan National University.
  6. Jiang, B. & Claramunt, C. (2002). Integration of Space Syntax into GIS: New Perspectives for Urban Morphology, Transactions in GIS, 6(3), 295-309. https://doi.org/10.1111/1467-9671.00112
  7. Jiang, B. (2009). Ranking Spaces for Predicting Human Movement in an Urban Environment, International Journal of Geographical Information Science, 23(7), 823-837. https://doi.org/10.1080/13658810802022822
  8. Jiang, B. & Liu, C. (2009). Street‐based topological representations and analyses for predicting traffic flow in GIS, International Journal of Geographical Information Science, 23(9), 1119-1137. https://doi.org/10.1080/13658810701690448
  9. Kim, J. (2012). Short-term prediction of real-time traffic conditions based on fuzzy expert system, Thesis, Hanyang University.
  10. Kim, S. (2006). The Development of Markov-Fuzzy on-line traffic state prediction model for freeway sections, Ph.D. Dissertation, University of Seoul.
  11. Kim, Y. (2003). A Study on the Relationship between Properties of Spatial Configuration and Patterns of Space Use using Space Syntax, Journal of the Korea Planning Association, 38(4), 7-17.
  12. Klarqvist, B. (1993). A Space Syntax Glossary, NORDISKARKITEKTURFORSKNING, 11-12.
  13. Lee, B. & Lee, S. (2005). Accessibility (serviceability) of Hierarchical Bus Network in Seoul, Journal of Korean Society of Transportation, 23(8), 163-170.
  14. Ministry of Land, Transport and Maritime Affairs. (2013). Korea Highway Capacity Manual.
  15. Oh, C., Lim, D., Kim, H. & Park, J. (2012). A Study on the Accessability of the Bikeway Networks in the South of the Han River Using Space Syntax, Korean Society of Road Engineers, 14(3), 97-110.
  16. Open street map, https://www.openstreetmap.org/ [Visit:2016.08.11]
  17. Penn, A. (2003). Space Syntax And Spatial Cognition Or Why the Axial Line?, ENVIRONMENT AND BEHAVIOR, 35(1), 30-65. https://doi.org/10.1177/0013916502238864
  18. Peponis, J., Ross, C. & Rashid, M. (1997). The Structure of Urban Space, Movement and Co-presence: The Case of Atlanta, Geoforum, 28(344), 341-358. https://doi.org/10.1016/S0016-7185(97)00016-X
  19. Ratti, C. (2004). Space Syntax: Some Inconsistencies, Environment and Planning B: Planning and Design, 31(4), 487-499. https://doi.org/10.1068/b3019
  20. Ryu, S., Lee, S. & Ahn, W. (2005). Development of the Multi-Path Finding Model Using Kalman Filter and Space Syntax based on GIS, Journal of Korean Society of Transportation, 23(7), 149-158.
  21. Seo, H. (2012). Prediction of urban congestion using ITS based Data, Thesis, Kyung Hee University.
  22. Thomson, J. (1972). Methods of traffic limitation in urban areas, Organisation for Economic Co-operation and Development.
  23. Turner, A. (2004). Depthmap 4: A Researcher's Handbook, Bartlett School of Graduate Studies, UCL, London.
  24. Turner, A., Penn, A. & Hillier, B. (2005). An algorithmic definition of the axial map, Environment and Planning B: Planning and Design, 32(3), 425-444. https://doi.org/10.1068/b31097
  25. Verbit, G. (1975). The Urban Transportation Problem, University of Pennsylvania Law Review, 124(2), 368-489. https://doi.org/10.2307/3311512