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- W628920723 abstract "Traffic flow and occupancy are two important inputs for estimating dynamically changing road link travel time. Link length, maximum speed limit, detector location, and green signal time effect free flow travel time. Since the free flow travel time is assumed constant for a specific link, only the delay time needs to be modeled for different flow and occupancy values. In this paper, fuzzy sets are employed at the interface level and neural networks at the processing level to estimate arterial link travel times/delays from loop detector data. The idea is to eliminate the fuzzy rule based system and replace it with a neural network. This approach results in significant savings in time and effort as obtaining human expert's knowledge in terms of fuzzy rules is very difficult. Based on this approach, the neural network is only given a set of training data. The neural network itself figures out all the fuzzy rules relating input and output patterns based on the given data. The fuzzy logic techniques at the interface levels may be viewed as a form of data compression. This improves the neural network training process and also helps in interpreting the neural network outputs. The accuracy of travel time estimation using different approaches have been compared. The fuzzy neural network estimates travel times more accurately for both flow and occupancy values than any other approach. It gives 87 percent more accurate estimated travel times than the fuzzy expert system." @default.
- W628920723 created "2016-06-24" @default.
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- W628920723 date "1995-11-01" @default.
- W628920723 modified "2023-09-24" @default.
- W628920723 title "ON-LINE TRAVEL TIME ESTIMATION USING FUZZY NEURAL NETWORK" @default.
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