Modelling the Gap Acceptance Behavior of Drivers of Two-Wheelers at Unsignalized Intersection in Case of Heterogeneous Traffic Using ANFIS

  • Harsh Jigish Amin
  • Akhilesh Kumar Maurya
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)


The gap acceptance concept is an important theory in the estimation of capacity and delay of the specific moment at unsignalized junctions. Most of analyzes have been carried in advanced countries where traffic form is uniform, and laws of priorities, as well as lane disciplines, are willingly followed. However, in India, priority laws are less honored which consequently create more conflicts at intersections. Modeling of such behavior is complex as it influenced by various traffic features and vehicles’ as well as drivers’ characteristics. A fuzzy model has been broadly accepted theory to investigate similar circumstances. This article defines the utilization of ANFIS to model the crossing performance of through movement vehicles at the four-legged uncontrolled median separated intersection, placed in a semi-urban region of Ahmedabad in the province of Gujarat. Video footage method was implemented, and five video cameras had been employed concurrently to collect the various movements and motorists’, as well as vehicles’ characteristics. An ANFIS model has been developed to estimate the possibilities of acceptance and rejections by drivers of two-wheelers for a particular gap or lag size. Seven input and one output parameters, i.e. the decision of the drivers are considered. Eleven different diverse combination of variables is employed to construct eleven different models and to observe the impact of various attributes on the correct prediction of specific model. 70 % observations are found to prepare the models and residual 30 % is considered for validating the models. The forecasting capability of the model has been matched with those experiential data set and has displayed good ability of replicating the experiential behavior. The forecast by ANFIS model ranges roughly between 77 and 90 %. The models introduced in this study can be implemented in the dynamic evaluation of crossing behavior of drivers.


Adaptive neuro-fuzzy inference system ANFIS Critical gap Uncontrolled intersection Neuro-fuzzy Gap acceptance behavior 


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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia

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