Affiliations: [a] School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China | [b] College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, Sichuan, China
Correspondence:
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Corresponding author: Jun-Song Yin, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, Sichuan, China. Tel./Fax: +86 028 87600165; E-mail: [email protected].
Abstract: This research established an adaptive congestion control model using the knowledge representation and reasoning methods, proposed variable connection weights FPN knowledge representation and reasoning model, and put forward a matrix formal reasoning algorithm. The model comprised a variety of decision variables and diverse control rules, designed an intersection signal control mode with variable phase sequence by considering the upstream and downstream traffic flow, so as to guide and control saturated traffic flow quickly and efficiently. Finally, visualization simulation was used to confirm the validity of the model by VC++.
Keywords: Congestion control, signal control, knowledge representation and reasoning, fuzzy petri net
