• Title/Summary/Keyword: 비정상류통행

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Vine Based Dial Algorithm (덩굴망기반 Dial 알고리즘 연구)

  • Lee, Mee Young;Kim, Jong Hyung;Jung, Dongjae;Shin, Seongil
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.17 no.5
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    • pp.39-47
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    • 2018
  • The Dial Algorithm, based on single link based calculation, is unable to reflect cyclic paths arising in actual urban transportation networks. At the same time, redefining the paths more efficiently can, by strict standards, lead to irrational results stemming from reduction in the size of the network to be analyzed. To solve these two problems of the Dial algorithm, the research herein proposes a vine network method applied to a link based Dial Algorithm, in which the original three step alogrithm is modified into a vine network-based three step process. Also, an analysis of two case study networks show feasible replication of the predicted cyclic path, unrealistic flow, and unsteady transit, as well as alleviation of the problem of irrational path allocation.

Effect of Guidance Information Receiving Ratio on Driver's Route Choice Behavior and Learming Process (교통정보 수신율 변화에 따른 운전자의 경로선택과 학습과정)

  • Do, Myung-Sik;Sheok, Chong-Soo;Chae, Jeung-Hwan
    • Journal of Korean Society of Transportation
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    • v.22 no.5
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    • pp.111-122
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    • 2004
  • The driver's decision making (e.g. route choice) is a typical decision making with an uncertainty. In this paper, we investigate the effect of route guidance information on driver's route choice and learning behavior and analyse the potential of information system in a road network in which traffic flows follow random walk. A Simulation performed focuses on the relationship among the network wide performance, message receiving rates and driver's learning mechanism. We know that at high levels of message receiving rates, the network-wide performance may get worse. However, at low levels of receiving rates, we found that the travel time when guidance information is provided decrease compared to the cases when no pubic information is provided. Also, we found that the learning parameter of the learning mechanism model always changes under nonstationary traffic condition. In addition, learning process of drivers does not converge on any specific value. More investigation is needed to enlarge the scope of the study and to explore more deeply driver's behavior.