KNOWLEDGE BASED SOFTWARE TO MANAGE TUNNEL FOR ONLINE CONDITIONS

Document Type : Original Article

Authors

1 PhD Student, Department of Mathematics and Computer Science, Amirkabir University of Technology.

2 Assistant Professor, Department of Mathematics and Computer Science and Research Institute for Traffic Engineering and Transportation Focused on Intelligent Transportation Systems, Amirkabir University of Technology.

Abstract

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.

Keywords

References

 [1] .     Shah, N. et. al (2012). Optimization models for assessing the peak capacity utilization of intelligent transportation systems. European Journal of Operational Research, VOL. 216 No.1, 239-251
[2] .    Trullols, O. et. al (2010). Planning roadside infrastructure for information dissemination in intelligent transportation systems. Computer Communications, VOL. 33 NO.4, 432-442
[3] .     Ma, Zh. Et. Al (2009), Characteristics of traffic accidents in Chinese freeway tunnels. Tunnelling and Underground Space Technology, VOL. 24, 350- 355.
[4] .      Xu,Q. et. al (2013). Preliminary Study on Exhaust Efficiency of Smoke Management System in Tunnel Fires. Procedia Engineering, VOL. 52, 514- 519.
[5] .    Alvear, D. et. Al (2013).Decision support system for emergency management: Road tunnels. Tunnelling and Underground Space Technology, VOL. 34, 13- 21.
[6] .    Vashitz,G. et. Al (2008). In-vehicle information systems to improve traffic safety in road tunnels. Transportation Research Part F: Traffic Psychology and Behaviour, VOL. 11, 61-74.
[7] .    Ülengin,F., Topcu, I.,(2000). Knowledge-Based Decision Support Systems Techniques and Their Application in Transportation Planning Systems. Knowledge-Based Systems. VOL. 4, 1403- 1429.
[8] .    Darmoul, S., Elkosantini, S., (2014). Artificial immunity to control disturbances in public transportation systems: Concepts, mechanisms and a prototype implementation of a knowledge based decision support system. Knowledge-Based Systems. VOL. 68. 58- 76.
[9] .    Sagong, M.,( 2006). Induction of tunnel reinforcement selection rules by using decision tree technique. Tunnelling and Underground Space Technology. VOL. 21, 389.
[10] . Mahdevari, S., Torabi, S.R., (2012). Prediction of tunnel convergence using Artificial. Neural Networks. Tunnelling and Underground Space Technology. VOL. 28, 218–228.
[11] . Leo, S., Chen, Ch., Chang, Sh., (2001). Data mining for tunnel support stability: neural network approach. Automation in Construction, VOL. 10 NO. 4, 429–441.
[12] . Elvic, R. (2008). The predictive validity of empirical Bayes estimates of road safety. Accident Analysis & Prevention. VOL. 40, NO. 6, 1964- 1969.
[13] . Abpeykar, Sh., Ghatee, M., (2014). Supervised and unsupervised learning DSS for incident management in intelligent tunnel: A case study in Tehran Niayesh tunnel. Tunneling and Underground Space Technology. VOL. 42, 293- 306.
[14] . Demiral, C., Celikoglu, H., (2011). Application of ALINEA ramp control algorithm to freeway traffic flow on approaches to Bosphorus strait crossing bridges, Procedia - Social and Behavioral Sciences, VOL. 20, 364- 371.
[15] . Zhao, Y., Zhang, Y., (2008). Comparison of decision tree methods for finding active objects. Advances in Space Research. VOL. 41. 1955- 1959.
[16] . Zhang, H., Su, J., (2004). Naive Bayesian Classifiers for Ranking. Machine Learning: ECML 2004 Lecture Notes in Computer Science. VOL. 3201, 501- 512.
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