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http://dx.doi.org/10.12672/ksis.2014.22.2.089

Agent Based Road Control Model for Micro-Level Traffic Simulation  

Na, Yu-Gyung (Dept. of Geography, Kyung Hee University)
Choi, Jinmu (Dept. of Geography, Kyung Hee University)
Publication Information
Abstract
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.
Keywords
Road traffic model; Agent model; Traffic simulation; GIS; Traffic flow; Traffic information;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 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.   과학기술학회마을   DOI
2 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.   과학기술학회마을   DOI
3 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.   과학기술학회마을
4 Na, Y. 2014, Analyzing the Effect of Inundation Information for the Traffic Flow Using the Agent-based Model, Kyung Hee University.
5 Seoul City Transportation Headquarters, 2009, Average Deriving Speed in Seoul, Seoul, Korea.
6 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.
7 Park, J. H. 2011, Characteristics of Traffic Flow and Delay Model Development using Work Zone Data, Hanbat National University.
8 Seoul, 2013, Flood Map of Seoul-si, Accessed on July 10. http://hongsu.seoul.go.kr.
9 Seoul City Transportation Headquarters, 2010, Average Deriving Speed in Seoul, Seoul, Korea.
10 Shin, S. I.; Cho, Y. C. 2006, Improving Transportation Disaster System in Seoul, Seoul Development Institute.
11 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.   과학기술학회마을
12 The Korea Transport Institute. 2013, Traffic volume data, Accessed on October 31. http://ktdb.go.kr.
13 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.
14 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.   과학기술학회마을
15 Lee, H. G; Han, J. C; Jung, C. S; Oh, K. J; Han, G. H. 2009, Understanding Human Behavior, Bobmunsa.
16 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.   DOI   ScienceOn