shadi abpeykar

Under dynamic conditions on bridges, we need a real-time management. To this end, this paper presents a rule-based decision support system in which the necessary rules are extracted from simulation results made by Aimsun traffic micro-simulation software. Then, these rules are generalized by the aid of fuzzy rule generation algorithms. Then, they are trained by a set of supervised and the unsupervised learning algorithms to get an ability to make decision in real cases. As a pilot case study, Nasr Bridge in Tehran is simulated in Aimsun and WEKA data mining software is used to execute the learning algorithms. Based on this experiment, the accuracy of the supervised algorithms to generalize the rules is greater than 80%. In addition, CART decision tree and sequential minimal optimization (SMO) provides 100% accuracy for normal data and these algorithms are so reliable for crisis management on bridge. This means that, it is possible to use such machine learning methods to manage bridges in the real-time conditions.

Optimal usage of transportation equipments are very important issue in many countries which use ITS. Tunnel is one of the transportation structures which has many transportation systems as Jet fans، LED، CCTV،… so there is a need to have a control room in tunnel، which helps using these sensors، and make decisions in different critical situations rapidly. In this paper operation of control room to manage tunnel intelligent transportation system، is mentioned and then based on knowledge engineering rule extractions are discussed. These rules are used as a database for expert system. Then they used for tunnel management. In knowledge engineering، learnable algorithms are run، and near extracting of rules، they made a report for tunnel administrator. As a result Naive bayes decision tree with lower process time and higher accuracy made a best result for Niayesh tunnel in Tehran.