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

摘要

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, Naieve 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 Naieve 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.