Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Laberge, J.C., Creaser, J.I., Rakauskas, M.E., Ward, N.J.: Design of an intersection decision support (IDS) interface to reduce crashes at rural stop-controlled intersection. Transp. Res. Part C: Emerg. Technol. 14, 36–56 (2006)
Alexander, J., Barham, P., Black, I.: Factors influencing the probability of an incident at a junction: results from an interactive driving simulator. Accid. Anal. Prev. 34(6), 779–792 (2002)
Amin, H.J., Desai, R.N., Patel, P.S.: Modelling the crossing behavior of pedestrian at uncontrolled intersection in case of mixed traffic using adaptive neuro fuzzy inference system. J. Traffic Logistic Eng. 2(4), 263–270 (2014)
Ottomanelli, M., Caggiani, L., Iannucci, G., Sassanelli, D.: An adaptive neuro-fuzzy inference system for simulation of pedestrians behaviour at unsignalized roadway crossings. In: Softcomputing in Industrial Applications. Netherlands (2010)
Valdés-Vela, M., Toledo-Moreo, R., Terroso-Sáenz, F., Zamora-Izquierdo, M.A.: An application of a fuzzy classifier extracted from data for collision avoidance support in road vehicles. Eng Appl. Artif. Intell. 26(1), 173–183 (2013)
Keyarsalan, M., Ali Montazer, G.: Designing an intelligent ontological system for traffic light control in isolated intersections. Eng. Appl. Artif. Intell. 24(8), 1328–1339 (2011)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence. Prentice Hall, NJ (1997)
Rossi, R., Massimiliano, G., Gregorio, G., Claudio, M.: Comparative analysis of random utility models and fuzzy logic models for representing gap-acceptance behavior using data from driving simulator experiments. Procedia-Soc Behav. Sci. Elsevier 54, 834–844 (2012)
Rossi, R., Meneguzzer, C.: The effect of crisp variables on fuzzy models of gap-acceptance behaviour. In: Proceedings of the 13th Mini-EURO Conference: Handling Uncertainty in the Analysis of Traffic and Transportation Systems, pp. 240–246 (2002)
Ghomsheh, V.S., Shoorehdeli, M.A., Teshnehlab, M.: Training anfis structure with modified pso algorithm. In: Proceeding of the 15th Mediterranean Conference on Control & Automation, Athens–Greece (2007)
Mehrabi, M., Pesteei, S.M.: An adaptive neuro-fuzzy inference system (anfis) modelling of oil retention in a carbon dioxide air-conditioning system. In: International Refrigeration and Air Conditioning Conference, Iran (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Amin, H.J., Maurya, A.K. (2016). Modelling the Gap Acceptance Behavior of Drivers of Two-Wheelers at Unsignalized Intersection in Case of Heterogeneous Traffic Using ANFIS. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_55
Download citation
DOI: https://doi.org/10.1007/978-81-322-2538-6_55
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2537-9
Online ISBN: 978-81-322-2538-6
eBook Packages: EngineeringEngineering (R0)