new electrosensitive traffic light using fuzzy neural network

Abstract

In the past, when there were few vehicles on the roads, the time-of-day (TOD) traffic signal worked very well. The TOD signal operates on a preset signal-cycling scheme independent of traffic conditions. It cycles on the basis of the number of average passenger cars to the memory device of an electric signal unit. Today, with the increasing traffic and congested roads, the conventional traffic light creates startup-delay time and end-lag time. A 30 to 45% efficiency in traffic handling is lost, as well as added fuel costs, since it is not optimized for today's traffic condition. To solve this problem, an electrosensitive traffic light using neural fuzzy logic will be investigated. This scheme uses an electrosensitive traffic light control, which changes signal based on the passing vehicle's weight, length, and passing area. Through computer simulation, this method has been proven to be much more efficient than fixed time interval signal since the average waiting time, average vehicle speed, and fuel consumption will be improved.Abstract-We remark on an error in the above paper. The purpose of this note is to present the correct formulas for partial derivatives of fuzzy integral-based neural nodes with respect to densities of Sugeno measures.Index Terms-Fuzzy integral, fuzzy measure.

Dynamic Traffic Light Sequence Algorithm Using RFID

“…In the case where other than RFID are used in the system such as image processing and traffic sensor, the algorithm proposed in this work will improve their performance by reducing the size of computational data since the size of RFID tag data only several bytes long and provides proper basis for the estimation assume in normal fuzzy logic analysis procedure discussed in [2,6,7] .…”