DOI QR코드

DOI QR Code

미시적 교통 시뮬레이션을 위한 에이전트 기반 도로 통제 모델 구축 연구

Agent Based Road Control Model for Micro-Level Traffic Simulation

  • 투고 : 2014.03.11
  • 심사 : 2014.04.30
  • 발행 : 2014.04.30

초록

본 연구는 개별 운전자의 행태가 교통 시스템 전체에 미치는 영향을 살펴보기 위하여 도로 통제 정보의 전파 정도가 교통 혼잡에 미치는 영향 정도를 파악하고자 하였다. 이를 위해 에이전트 기반 교통 모델을 구축하였고 GIS 데이터를 교통 모델에 직접 활용할 수 있는 방안과 모의실험 결과의 처리과정을 제시하였다. 도로 통제 정보 제공이 교통 흐름에 미치는 영향을 분석한 결과, 전체 운전자의 30~70%에 해당하는 운전자에게 정보를 제공할 때 평균속도가 저하되지 않는 것으로 나타났다. 이에 반해 20% 이하 또는 80% 이상의 운전자에게 정보가 전달되면 전체 운전자의 평균속도가 저하되었다. 연구 결과를 종합해 볼 때, 도로 통제 정보의 제공은 교통의 흐름에 영향을 미치며 우회차량으로 인해 국지적 정체가 발생할 수 있음을 알 수 있었다. 이 결과는 향후 도로 교통 정책의 방향 설정을 위한 기초자료로 활용 될 수 있을 것으로 판단된다.

This study investigated how much the spread of traffic control information affect the traffic congestion in order to identify the behavior of the individual drivers that impacts on the entire transport system. For this purpose, agent-based transportation model was constructed. GIS data were directly used for the transportation model and the processing steps of the simulation results are presented. The results showed that the average speed was not lowered when the traffic information was provided to 30 to 70% of total drivers. In contrast, the driver's average speed is reduced when he traffic information was provided to less than 20% or 80% or more. In summary, the provision of traffic information to drivers has an influence on the traffic flow and bypassing vehicles can generate local congestion. This results can be used as a basis for the future direction of road transport policy.

키워드

참고문헌

  1. Bamberg S; Ajzen I; Schmidt P. 2003, Choice of Travel Mode in the Theory of Planned Behavior: The Roles of Past Behavior, Habit, and Reasoned Action, Basic and Applied Social Psychology, 25(3):175-187. https://doi.org/10.1207/S15324834BASP2503_01
  2. Choi, K. H; Lee, J. H; Hwang, T. H; Yoo, J. J; Joo, I, H. 2002, Automatic Generation Method of Road Data based on Spatial Information, Journal of Korea Spatial Information Society, 4(2): 55-64.
  3. Hines, J; Hungerford, H; Tomera, A. 1987, Analysis and Synthesis of Research on Responsible Environmental Behavior: A Meta-Analysis, The Journal of Environmental Education, 18(2):1-8.
  4. Lee, Y; Kim, T. S.; Ha, T. W.; Kang, S. H.; Lee, S. H. 2003, Study on the Assessment of Refuge Behavior and the Derivation of Critical Inundation Depth, Journal of Korean Institute of Fire Science & Engineering, 17(4):92-97.
  5. Lee, H. G; Han, J. C; Jung, C. S; Oh, K. J; Han, G. H. 2009, Understanding Human Behavior, Bobmunsa.
  6. Lee, J. H; Jang, Y. H; Kwon, Y. J. 2013, An Efficient Location Based Service based on Mobile Augmented Reality applying Street Data extracted from Digital map, Journal of Korea Spatial Information Society, 21(4): 63-70. https://doi.org/10.12672/ksis.2013.21.4.063
  7. Na, Y; Lee, S; Joh, C. H. 2012, An Analysis of Decision-Making in Extreme Weather using an ABM Approach Application of Mode Choice in Heavy Rain & Heavy Snow, Journal of the Economic Geographical Society of Korea, 15(2): 304-313. https://doi.org/10.23841/egsk.2012.15.2.304
  8. Na, Y. 2014, Analyzing the Effect of Inundation Information for the Traffic Flow Using the Agent-based Model, Kyung Hee University.
  9. O'Connor, R. E; Bord R. J; Fisher A. 1999, Risk Perceptions, General Environmental Beliefs, and Willingness to Address Climate Change, Risk Analysis, 19(3):461-471.
  10. Park, J. H. 2011, Characteristics of Traffic Flow and Delay Model Development using Work Zone Data, Hanbat National University.
  11. Seoul, 2013, Flood Map of Seoul-si, Accessed on July 10. http://hongsu.seoul.go.kr.
  12. Seoul City Transportation Headquarters, 2009, Average Deriving Speed in Seoul, Seoul, Korea.
  13. Seoul City Transportation Headquarters, 2010, Average Deriving Speed in Seoul, Seoul, Korea.
  14. Shin, S. I.; Cho, Y. C. 2006, Improving Transportation Disaster System in Seoul, Seoul Development Institute.
  15. Shin, S. I.; Cho. Y. C; Lee, C. J. 2007, Strategies for Providing Detour Route Information and Traffic Flow Management for Flood Disasters, Journal of Korean Society of Transportation, 25(6):33-42.
  16. The Korea Transport Institute. 2013, Traffic volume data, Accessed on October 31. http://ktdb.go.kr.