Skip to main content

2016 | OriginalPaper | Buchkapitel

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

verfasst von : Harsh Jigish Amin, Akhilesh Kumar Maurya

Erschienen in: Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics

Verlag: Springer India

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat 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)CrossRef 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)CrossRef
2.
Zurück zum Zitat 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)CrossRef 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)CrossRef
3.
Zurück zum Zitat 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) 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)
4.
Zurück zum Zitat 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) 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)
5.
Zurück zum Zitat 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) 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)
6.
Zurück zum Zitat 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)CrossRef 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)CrossRef
7.
Zurück zum Zitat 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) 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)
8.
Zurück zum Zitat 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)CrossRef 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)CrossRef
9.
Zurück zum Zitat 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) 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)
10.
Zurück zum Zitat 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) 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)
11.
Zurück zum Zitat 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) 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)
Metadaten
Titel
Modelling the Gap Acceptance Behavior of Drivers of Two-Wheelers at Unsignalized Intersection in Case of Heterogeneous Traffic Using ANFIS
verfasst von
Harsh Jigish Amin
Akhilesh Kumar Maurya
Copyright-Jahr
2016
Verlag
Springer India
DOI
https://doi.org/10.1007/978-81-322-2538-6_55