Intelligent Transportation Scheduling System Based on Fuzzy Control

Abstract:

In this paper, intelligent control system in the intersection was designed by use of the control principles of green light length. The green light time from four groups were controlled by actual traffic flow, which could be detected by use of inductive sensors to auto-adjust the length of time. Secondly, combined with the traffic scale, road, time and seasonal changes situation, turn-left controller was tried to add in the traffic lights system by use of fuzzy control method at the crossroads. The feasibility of control principles from green light length were verified by debug in the PLC program.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 507-513
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.507
• Online since: January 2012
• Authors: Jing Jiang, Xue Song Zhang
• Keywords: Control Principles of Green Light Length, Fuzzy Control, Traffic Scale

References

[1] Kotsialos, M. Papageorgiou. The Importance of Traffic Flow Modeling for Motorway Traffic Control,. Networks and Spatial Economics, 2001, 1(1-2).

[2] LEE C S, JIAN Z W, HUANG L K. A fuzzy ontology andits application to news summarization,. IEEE Transac-tions on Systems, Man, and Cybernetics-Part B: Cyber-netics, 2005, 35 (5) : 859-880.

DOI: 10.1109/tsmcb.2005.845032

[3] LAU R Y K. Fuzzy domain on tology discovery for business knowledge management,. IEEE Intelligent Informatics Bulletin, 2007, 8 (1) : 29-41.

[4] CASTELLS P, FERNANDEZ M, VALLET D. An adaptation of the vectorspace model for ontology based information retrieval,. IEEE Transactions on Knowledge and Data Engineering, 2007, 19 (2) : 261-272.

DOI: 10.1109/tkde.2007.22

[5] Kang Dazhou, Xu Baowen, Lu Jianjiang, Li Yanhui. Description logics for fuzzy ontologies on semantic web,. 2006, (03) : 5.

DOI: 10.1109/compsac.2005.22

[6] Shi Qixin, Lu Huapu. The Conceivability of the ITS Development Strategy in China, . 5th World Congress on ITS. 1998, : 10.

[7] Collins R, Lipton A, Kanade T, et al. A System for Video Surveillance and Monitoring,. Tech. Report CMU-RI-TR-00-12, Robotics Institute, Carnegie Mellon University. 2000, : 5.

[8] Koller. D., Daniilidis. K., Nagel, H. -H. Model-based Object Tracking in Monocular Image Sequences of Road Traffic Scenes,. International Journal of Computer Vision, 1993, 10 (3) : 257~281.

DOI: 10.1007/bf01539538

[9] Dan Middleton, Rick Parker. Vehicle detection workshop participant notebook,. Texas Transportation Institute , The Texas A&M University System, 2000, : 11~50.

[10] M. Y. Siyal, M. Fathy. A neural-vision based approach to measure traffic queue parameters in real-time,. Pattern Recognition Letters, 1999, 20 : 761～770.

DOI: 10.1016/s0167-8655(99)00040-9

[11] Harlow Charles, Peng Shiquan. Automatic vehicle classification system with range sensors, . Transpor tation Research Part C, 2001, (9) : 231~237.

[12] Shi Qixin. The Present situation of Road Transportation and Expectationof ITSResearches in China . 3'd World Congress on ITS. 1996, : 9.