This paper deals with a new decision support system (DSS) for intelligent tunnel. This DSS includes two subsystems. In the first, the rules are extracted from incident severity database and micro-simulation results. Then simple fuzzy grid technique is applied to generate the rules. The accuracy degree of this subsystem is 63% in the presented experiment. In the second subsystem, these rules are trained by DSS with two modules. In the first module unsupervised learning methods such as K-mean, farthest first, self-organizing map (SOM), learning vector quantization (LVQ), hierarchical clustering and filtered clustering are implemented. The best performance in this module corresponds to hierarchical clustering with 70% accuracy on normal data. Also learning vector quantization (LVQ) provides 74% accuracy on discrete data in this module. In the second module feed forward neural network, Naïve Bayes tree, classification and regression tree (CART), and support vector machine (SVM) are applied. In this module the most accuracy is 87% on normal data regarding to feed forward neural network and also Naïve Bayes tree provides 89.3% accuracy on discrete data. To illustrate the performance of the proposed learning DSS, we use two sources of data. The first is UK road safety data bank which is applied to estimate severity of real incidents in tunnel. The second one is simulation results of Niayesh tunnel in Tehran which is implemented on Aimsun 7. Although only incident management in tunnel is focused by this paper, it is possible to find similar results on learning DSS for other user services of intelligent tunnel.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Supervised and unsupervised learning DSS for incident management in intelligent tunnel: A case study in Tehran Niayesh tunnel


    Contributors:


    Publication date :

    2014


    Size :

    14 Seiten, 64 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




    Automatic incident detection for tunnel management

    Brambilla, L. / Lemaire, F. / Versavel, J. | Tema Archive | 1996


    INTELLIGENT TUNNEL MANAGEMENT SYSTEM

    KWAG DONG DALL | European Patent Office | 2019

    Free access

    Vision-based tunnel traffic incident detection system

    WANG ANLI / FENG WEI / GE RILETU | European Patent Office | 2023

    Free access

    Tunnel maintenance equipment for intelligent tunnel construction

    SHEN YANG / ZHU LI / DENG FANGMING et al. | European Patent Office | 2023

    Free access

    System for preventing accident employing to tunnel incident

    European Patent Office | 2024

    Free access