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Fuzzy Logic Models of Gap-Acceptance Behavior at Roundabouts

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Computer-based Modelling and Optimization in Transportation

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 262))

Abstract

Gap-acceptance behavior at intersections has been extensively studied in the field of traffic theory and engineering using various methods. An interesting application of gap-acceptance theory regards roundabouts, which differ from ordinary unsignalized intersections in terms of geometry and driving behavior. Several studies on gap-acceptance at roundabouts can be found in the literature, but, to our knowledge, the fuzzy logic approach has never been used to analyze this type of problem. This chapter describes the development of a gap-acceptance model based on fuzzy system theory and specifically applicable to traffic entering a roundabout. As an alternative to probabilistic discrete choice models, fuzzy system based models can be considered to be appropriate for describing gap-acceptance behavior at roundabouts, because they allow to represent the uncertainty and vagueness that characterizes various aspects of the choice situation under study. Possible applications of fuzzy logic models of gap-acceptance behavior include roundabout entry capacity estimation and use in the context of traffic micro-simulation software. The study is based on data derived from on site observations carried out at a roundabout near Venice, Italy. The performance of the model, evaluated using the Receiver Operating Characteristic (ROC) curve analysis, indicates that fuzzy models can be considered an alternative to the use of random utility models.

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Acknowledgments

The authors acknowledge the technical support of Engineer Alberto Sarto, Mr. Stefano Borgato and Mr. Alessandro Giachi.

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Correspondence to Riccardo Rossi .

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Rossi, R., Gastaldi, M., Gecchele, G., Meneguzzer, C. (2014). Fuzzy Logic Models of Gap-Acceptance Behavior at Roundabouts. In: de Sousa, J., Rossi, R. (eds) Computer-based Modelling and Optimization in Transportation. Advances in Intelligent Systems and Computing, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-319-04630-3_21

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  • DOI: https://doi.org/10.1007/978-3-319-04630-3_21

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