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After careful observation, the following strategies have been adopted for modeling the gap acceptance behavior under mixed traffic condition using a logit model.
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Rossi and Meneguzzer [22] modeled the gap acceptance behavior at priority intersections using neuro-fuzzy system.
Maze [6] modeled the gap acceptance behavior using a logit model which closely approximates the probit model.
The results indicate that the ANFIS can be used to model the gap acceptance behavior at partially controlled intersections.
The primary focus of this paper is to model the gap acceptance behavior of vehicles at partially controlled intersections in India with the application of ANFIS.
The objective of this study is to analyze and model the gap acceptance behavior of minor street drivers at uncontrolled T-intersections considering their aggressive nature.
Sangole et al. [16] used an adaptive neuro-fuzzy interface system (ANFIS) approach to model the gap acceptance behavior of two-wheelers at uncontrolled intersections.
Considering the capabilities of ANFIS and some of its applications to traffic flow modeling, we adopted the ANFIS proposed by Jang [28] to model the gap acceptance behavior of right-turning vehicles at partially controlled T-intersections in this study.
We model the gap gene network using the connectionist gene circuit formalism developed by Mjolsness et al. [ 9].
PRANK+F is a novel attempt to model the gap insertion required by the alignment process in a phylogenetically meaningful way.
Sangole et al. [11] used neuro-fuzzy technique to model the gap-acceptance behavior of right turning two wheelers (TWs) at three-legged intersections in India.
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