NDLI: Research on urban mixed intersections traffic flow base on cellular automaton

Content Provider	IEEE Xplore Digital Library
Author	Liu Run-Jie He Dan-Dan Shen Jin-Yuan Yang Qiu-Chen
Copyright Year	2012
Description	Author affiliation: Schools of Information Engineering, Zhengzhou University, Zhengzhou, 450001 (Liu Run-Jie; He Dan-Dan; Shen Jin-Yuan; Yang Qiu-Chen)
Abstract	The problem of urban traffic intelligent control is a key technology of modern urban construction. An improve ChSch model base on cellular automaton is constructed in order to conform to the actual situation of traffic. The simulation results show that big intersection is the main bottleneck and the synchronous and fixed traffic signal makes the road in serious congestion when car flow is large. This paper presents an adaptive fuzzy neural network to control semaphores method is used to improve road congestion. Based on fuzzy neural network method, we use RBF neural network as fuzzy rules for learning network, the control signal is improved, and the results show that the two level fuzzy neural network control of traffic flow of the city's main thoroughfares are obviously improved, road more clear.
Starting Page	3423
Ending Page	3427
File Size	290889
Page Count	5
File Format	PDF
ISBN	9781467325813
ISSN	21612927
e-ISBN	9789881563811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Fuzzy control fuzzy neural network Roads mixed intersection Automata traffic flow Cities and towns Fuzzy neural networks Educational institutions Electronic mail cellular automaton
Content Type	Text
Resource Type	Article

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