Application of ANFIS model in road traffic and transportation: a literature review from 1993 to 2018
DOI:
https://doi.org/10.31181/oresta19012010140sKeywords:
ANFIS, intelligent transportation systems, light signaling, vehicle routing, prediction, modelingAbstract
The paper is focused on researching the application of the ANFIS (Adaptive Neuro Fuzzy Inference System) model in traffic and transport through a review of relevant papers. ANFIS, as an element of artificial intelligence, is widely used within intelligent transport systems. All collected papers are divided into 7 sub-areas: 1) vehicle routing, 2) traffic control at intersections with light signaling, 3) vehicle steering and control, 4) safety, 5) modeling of fuel consumption, engine performance and exhaust emissions, 6) traffic congestion prediction and 7) other applications. For each sub-area, the analysis of the proposed models was performed with a tabular overview of the input and output variables, and in the third section the discussion of the results was given. It was found that the steering and control of vehicles represent a sub-area with the highest percentage in the total number of examined papers, while security applications are in second place.
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References
Abbas, S., Khan, M. S., Ahmed, K., Abdullah, M., & Farooq, U. (2011). Bio-inspired neuro-fuzzy based dynamic route selection to avoid traffic congestion. International Journal of Scientific and Engineering Research, 2(6), 284-289.
Abiodun, B. A., Amosa, A. A., Olayode, A. O., Morufat, A. T., & Adedayo, S. A. (2014). Traffic Light Control Using Adaptive Network Based Fuzzy Inference System. In Proceedings of 2014 International Conference on Artificial Intelligence & Manufacturing Engineering (ICAIME 2014), Dubai, December 25-26, 2014 (pp. 156-161).
Abdallat, Y., Al-Ghandoor, A., Samhouri, M., Al-Rawashdeh, M., & Qamar, A. (2011). Jordan Transport Energy Demand Modeling: The Application of Adaptive Neuro-Fuzzy Technique. International Review of Mechanical Engineering, 5(7), 1321-1326.
Al-Mayyahi, A., Wang, W., & Birch, P. (2014). Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation. Robotics, 3(4), 349-370.
Araghi, S., Khosravi, A., & Creighton, D. (2014). ANFIS traffic signal controller for an isolated intersection. In Proceedings of the International Conference on Fuzzy Computation Theory and Applications, Rome, Italy, October 22-24 (pp. 175-180). INSTICC Press
Arora, N., & Saini, J. R. (2014). A literature review on recent advances in neuro-fuzzy applications. International Journal of Advanced Networking and Applications, 1, 14-20.
Atmaca, H., Cetisli, B., & Yavuz, H. S. (2001). The comparison of fuzzy inference systems and neural network approaches with ANFIS method for fuel consumption data. In Second International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, November 7-11.
Azadi, S. H., Nedamani, H. R., & Kazemi, R. (2013). Automatic Parking of an Articulated Vehicle Using ANFIS. Global Journal of Science, Engineering and Technology, 14, 93-104.
Billah, M., Waheed, S., Ahmed, K., & Hanifa, A. (2015). Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System. Communications on Applied Electronics, 3(1), 1-5.
Boada, B. L., Boada, M. J. L., Gauchía, A., Olmeda, E., & Díaz, V. (2015). Sideslip angle estimator based on ANFIS for vehicle handling and stability. Journal of Mechanical Science and Technology, 29(4), 1473-1481.
Boada, B. L., Boada, M. J. L., & Diaz, V. (2016). Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm. Mechanical Systems and Signal Processing, 72, 832-845.
Dadula, C., & Dadios, E. (2016). Event Detection Using Adaptive Neuro Fuzzy Inference System for a Public Transport Vehicle. In 11th International Conference of the Eastern Asia Society for Transportation Studies.
Effati, M., Rajabi, M. A., Samadzadegan, F., & Shabani, S. (2014). A geospatial neuro-fuzzy approach for identification of hazardous zones in regional transportation corridors. International Journal of Civil Engineering, 12(3), 289-303.
Eski, İ., & Yıldırım, Ş. (2017). Neural network-based fuzzy inference system for speed control of heavy duty vehicles with electronic throttle control system. Neural Computing and Applications, 28(1), 907-916.
George, A. M., Kurian, C. P., George, V. I., & George, M. A. (2015). Hardware in Loop Simulation of an Adaptive Traffic Light Control System. International Journal of Applied Engineering Research, 10(19), 39999-40004.
Gharib, Z., Bozorgi-Amirib, A., Tavakkoli-Moghaddamb, R., & Najaa, E. (2018). A cluster-based emergency vehicle routing problem in disaster with reliability. Scientia Iranica, 25(4), 2312-2330.
Ghaffari, A., Khodayari, A. R., Kamali, A., & Tajdari, F. A (2015). A New Model of Car Following Behavior Based on Lane Change Effects using Anticipation and Evaluation Idea. Iranian Journal of Mechanical Engineering, 16(2), 26-38.
Ghaffari, A., Alimardani, F., Khodayari, A., & Sadati, H. (2011a). ANFIS based modeling for overtaking maneuver trajectory in motorcycles and autos. In Proceedings of IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, November 25-27 (pp. 68-73). IEEE
Ghaffari, A., Khodayari, A., & Arvin, S. (2011). ANFIS based modeling and prediction lane change behavior in real traffic flow. In International Conference on Intelligent Computing and Intelligent Systems, Penang, Malaysia, November 25-27 (pp. 156-161).
Ghaffari, A., Alimardanii, F., & Khodayari, A. (2012). Predicting the future state of a vehicle in a stop&go behavior based on ANFIS models design. In Proceedings of 6th IEEE International Conference Intelligent Systems, Sofia, Bulgaria, September 6-8 (pp. 368-373). IEEE
Ghanbari, M., Najafi, G., Ghobadian, B., Mamat, R., Noor, M. M., & Moosavian, A. (2015). Adaptive neuro-fuzzy inference system (ANFIS) to predict CI engine parameters fueled with nano-particles additive to diesel fuel. In IOP Conference Series: Materials Science and Engineering, Kuantan, Pahang, Malaysia, August 18-19, Volume 100, No. 1 (pp. 1-8). IOP Publishing
Gokdag, M., Hasiloglu, A. S., Karsli, N., Atalay, A., & Akbas, A. (2007). Modeling of vehicle delays at signalized intersection with an adaptive neuro-fuzzy (ANFIS). Journal of Scientific and Industrial Research, 66(9), 736-740.
Hasiloglu, A. S., Gokdag, M., & Karsli, N. (2014). Comparison an artificial intelligence-based model and other models: Signalized intersection delay estimates. International Journal of Engineering and Innovative Technology, 4(3), 220-231.
Hosoz, M., Ertunc, H. M., Karabektas, M., & Ergen, G. (2013). ANFIS modelling of the performance and emissions of a diesel engine using diesel fuel and biodiesel blends. Applied Thermal Engineering, 60(1-2), 24-32.
Hosseinlou, M. H., & Sohrabi, M. (2009). Predicting and identifying traffic hot spots applying neuro-fuzzy systems in intercity roads. International Journal of Environmental Science & Technology, 6(2), 309-314.
Hou, Y., Zhang, J., Zhang, Y., & Chen, L. (2008). Integrated chassis control using ANFIS. In Proceedings of IEEE International Conference on Automation and Logistics, Qingdao, China, September 1-3, (pp. 1625-1630). IEEE
Jomaa, D., Yella, S., & Dougherty, M. (2015). Automatic Trigger Speed for Vehicle Activated Signs using Adaptive Neuro fuzzy system and Classification Regression Trees. In Fourth International Conference on Intelligent Systems and Applications INTELLI 2015, St. Julians, Malta, October 11-16 (138).
Joshi, M., & Hadi, T. H. (2015). A review of network traffic analysis and prediction techniques. arXiv preprint arXiv:1507.05722.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
Jang, J. S. (1996). Input selection for ANFIS learning. In Proceedings of IEEE 5th International Fuzzy Systems, New Orleans, LA, USA, September 11, Volume 2 (pp. 1493-1499). IEEE
Kar, S., Das, S., & Ghosh, P. K. (2014). Applications of neuro fuzzy systems: A brief review and future outline. Applied Soft Computing, 15, 243-259.
Khodayari, A., Kazemi, R., Ghaffari, A., & Manavizadeh, N. (2010). Modeling and intelligent control design of car following behavior in real traffic flow. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, Singapore, June 28-30 (pp. 261-266). IEEE
Korkmaz, E. & Akgüngör A. P. (2016). An application of adaptive neuro-fuzzy inference system in vehicle delay modeling. International Journal of Advances in Mechanical and Civil Engineering, 3(4), 59-62.
Kothandaraman, R., & Ponnusamy, L. (2012). PSO tuned adaptive neuro-fuzzy controller for vehicle suspension systems. Journal of advances in information technology, 3(1), 57-63.
Kumar, M. K., & Prasad, V. K. (2015). Driver Behavior Analysis and Prediction Models: A Survey. International Journal of Computer Science and Information Technologies, 6(4), 3328-3333.
Kukadapwar, S. R., & Parbat, D. K. (2015). Modeling of traffic congestion on urban road network using fuzzy inference system. American Journal of Engineering Research, 4(12), 143-148.
Lai, G. R., Soh, A., Sarkan, H. M. D., Rahman, R. A., & Hassan, M. K. (2015). Controlling traffic flow in multilane-isolated intersection using ANFIS approach techniques. Journal of Engineering Science and Technology, 10(8), 1009-1034.
Li, X. X., Huang, H., & Liu, C. H. (2007). The application of an ANFIS and BP neural network method in vehicle shift decision. Paper presented at the 12th World Congress in Mechanism and Machine Science, Besançon, France, June 18-21.
Liu, M., & Chen, Y. (2017). Predicting Real-Time Crash Risk for Urban Expressways in China. Mathematical Problems in Engineering, 2017, 1-10.
Massoud, M., Sahebgharani, A. & Ramesht, Z. (2014). Eveloping An Integrated Land Use-Transportation Model (Ilutm) For Analysis And Prediction Of Energy Consumption Through Adaptive Neuro-Fuzzy Inference System (ANFIS). International Journal of Recent Scientific Research, 5(1), 107-109.
Maurya, A. K., & Patel, D. K. (2015). Vehicle Classification Using Adaptive Neuro-Fuzzy Inference System (ANFIS). In K. N. Das, D. Kusum, M. Pant, B. C. Jagdish, N. Atulya (Eds.), Proceedings of Fourth International Conference on Soft Computing for Problem Solving (pp. 137-152). Springer India
Maghsoudi, R., & Moshiri, B. (2017). Applying Adaptive Network-Based Fuzzy Inference System to Predict Travel Time in Highways for Intelligent Transportation Systems. Journal of Advances in computer Research, 8(3), 87-103.
Mohebbi, M., Charkhgard, M., & Farrokhi, M. (2005). Optimal neuro-fuzzy control of parallel hybrid electric vehicles. In Proceedings of IEEE Vehicle Power and Propulsion Conference, Chicago, IL, USA, September 7 (pp. 26-30). IEEE
Nugroho, P. W., Li, W., Du, H., Alici, G., & Yang, J. (2014). An adaptive neuro fuzzy hybrid control strategy for a semiactive suspension with magneto rheological damper. Advances in Mechanical Engineering, 6, 487312.
Ottomanelli, M., Caggiani, L., Iannucci, G., & Sassanelli, D. (2010). An adaptive neuro-fuzzy inference system for simulation of pedestrians behaviour at unsignalized roadway crossings. In Gao XZ., Gaspar-Cunha A., Köppen M., Schaefer G., Wang J. (Eds.), Soft computing in industrial applications (pp. 255-262). Springer, Berlin, Heidelberg
Pamučar, D., Bozanic, D., & Komazec, N. (2016). Managing the hazardous material transportation process using the adaptive neural networks and Dijkstra algorithm. In Proceedings of II International Scientific Conference Safety and Crisis Management–Theory and Practise Safety for the Future, Obrenovac, Serbia (pp. 112-116).
Pamučar, D., Ljubojević, S., Kostadinović, D., & Đorović, B. (2016a). Cost and risk aggregation in multi-objective route planning for hazardous materials transportation—A neuro-fuzzy and artificial bee colony approach. Expert Systems with Applications, 65, 1-15.
Pamučar, D., & Ćirović, G. (2018). Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions. Decision Making: Applications in Management and Engineering, 1(1), 13-37.
Pamučar, D., Lukovac, V., & Pejčić-Tarle, S. (2013). Application of Adaptive Neuro Fuzzy Inference System in the process of transportation support. Asia-Pacific Journal of Operational Research, 30(02), 1250053.
Pérez, J., Gajate, A., Milanés, V., Onieva, E., & Santos, M. (2010). Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicles. In Proceedings of International Conference on Fuzzy Systems, Barcelona, Spain, July 18-23 (pp. 1-6). IEEE
Poor Arab Moghadam, M., Pahlavani, P., & Naseralavi, S. (2016). Prediction of car following behavior based on the instantaneous reaction time using an ANFIS-CART based model. International Journal of Transportation Engineering, 4(2), 109-126.
Rai, A. A., Pai, P. S., & Rao, B. S. (2015). Prediction models for performance and emissions of a dual fuel CI engine using ANFIS. Sadhana, 40(2), 515-535.
Rahimi, A. M. (2017). Neuro-fuzzy system modelling for the effects of intelligent transportation on road accident fatalities. Tehnički vjesnik, 24(4), 1165-1171.
Saadeddin, K., Abdel-Hafez, M. F., Jaradat, M. A., & Jarrah, M. A. (2013). Performance enhancement of low-cost, high-accuracy, state estimation for vehicle collision prevention system using ANFIS. Mechanical Systems and Signal Processing, 41(1-2), 239-253.
Saifizul, A. A., Zainon, M. Z., & Osman, N. A. (2006). An ANFIS controller for vision-based lateral vehicle control system. In Proceedings of 9th International Conference on Control, Automation, Robotics and Vision, Singapore, December 5-8 (pp. 1-4). IEEE
Seesara, S. & Gadit, J. (2015). Smart Traffic Control Using Adaptive Neuro-Fuzzy Inference System(ANFIS). International Journal of Advance Engineering and Research Development, 2(5), 295-302.
Selma, B., & Chouraqui, S. (2012). Trajectory estimation and control of vehicle Using neuro-fuzzy technique. International Journal of Advances in Engineering & Technology, 3(2), 97.
Sharma, A., Vijay, R., Bodhe, G. L., & Malik, L. G. (2014). Adoptive neuro-fuzzy inference system for traffic noise prediction. International Journal of Computer Applications, 98(13), 14-19.
Shankar, H., Raju, P. L. N., & Rao, K. R. M. (2012). Multi model criteria for the estimation of road traffic congestion from traffic flow information based on fuzzy logic. Journal of Transportation Technologies, 2(01), 50.
Shuliakov, V., Nikonov, O., & Fastovec, V. (2015). Application of Adaptive Neuro-Fuzzy Regulators in the Controlled System by the Vehicle Suspension. International Journal of Automation, Control and Intelligent Systems, 1(3), 66-72.
Sindhuja, V., & Ranjitham, G. (2014). Regenerative Braking System of Electric Vehicle Driven by BLDC Motor Using Neuro Fuzzy and PID. International Journal of Innovative Research in Science, Engineering and Technology, 3(12), 17847-17854.
Sun, R., Xie, F., Xue, D., Zhang, Y., & Ochieng, W. Y. (2017). A novel rear-end collision detection algorithm based on GNSS fusion and ANFIS. Journal of Advanced Transportation, 2017, 1-10.
Syahputra, R. (2016). Application of Neuro-Fuzzy Method for Prediction of Vehicle Fuel Consumption. Journal of Theoretical & Applied Information Technology, 86(1), 138-150.
Thipparat, T., & Thaseepetch, T. (2012). Application of Neuro-Fuzzy System to Evaluate Sustainability in Highway Design. International Journal of Modern Engineering Research, 2(5), 4153-4158.
Udofia, K. M., Emagbetere, J. O., & Edeko, F. O. (2014). Dynamic traffic signal phase sequencing for an isolated intersection using ANFIS. Automation, Control and Intelligent Systems, 2(2), 21-26.
Wannige, C. T., & Sonnadara, D. U. J. (2008). Traffic signal control based on adaptive neuro-fuzzy inference. In A. Punchihewa, B. Dias (Eds.), Proceedings of 4th International Conference on Information and Automation for Sustainability, Colombo, Sri Lanka, December 12-14 (pp. 301-306). IEEE
Wankhede, S. S., Khanapurkar, M. M., & Bajaj, P. (2011). Intelligent Speed Adaptation System with Hybrid Algorithm. International Journal of Computer Science and Network Security, 11(1), 97.
Zaki, J. F., Ali-Eldin, A. M. T., Hussein, S. E., Saraya, S. F., & Areed, F. F. (2016). Framework for Traffic Congestion Prediction. International Journal of Scientific & Engineering Research, 7(5), 1205-1210.
Özkan, İ. A., Ciniviz, M., & Candan, F. (2015). Estimating Engine Performance and Emission Values Using ANFIS/ANFIS Kullanılarak Motor Performans ve Emisyon Değerleri Tahmini. International Journal of Automotive Engineering and Technologies, 4(1), 63-67.
Abiodun, B. A., Amosa, A. A., Olayode, A. O., Morufat, A. T., & Adedayo, S. A. (2014). Traffic Light Control Using Adaptive Network Based Fuzzy Inference System. In Proceedings of 2014 International Conference on Artificial Intelligence & Manufacturing Engineering (ICAIME 2014), Dubai, December 25-26, 2014 (pp. 156-161).
Abdallat, Y., Al-Ghandoor, A., Samhouri, M., Al-Rawashdeh, M., & Qamar, A. (2011). Jordan Transport Energy Demand Modeling: The Application of Adaptive Neuro-Fuzzy Technique. International Review of Mechanical Engineering, 5(7), 1321-1326.
Al-Mayyahi, A., Wang, W., & Birch, P. (2014). Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation. Robotics, 3(4), 349-370.
Araghi, S., Khosravi, A., & Creighton, D. (2014). ANFIS traffic signal controller for an isolated intersection. In Proceedings of the International Conference on Fuzzy Computation Theory and Applications, Rome, Italy, October 22-24 (pp. 175-180). INSTICC Press
Arora, N., & Saini, J. R. (2014). A literature review on recent advances in neuro-fuzzy applications. International Journal of Advanced Networking and Applications, 1, 14-20.
Atmaca, H., Cetisli, B., & Yavuz, H. S. (2001). The comparison of fuzzy inference systems and neural network approaches with ANFIS method for fuel consumption data. In Second International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, November 7-11.
Azadi, S. H., Nedamani, H. R., & Kazemi, R. (2013). Automatic Parking of an Articulated Vehicle Using ANFIS. Global Journal of Science, Engineering and Technology, 14, 93-104.
Billah, M., Waheed, S., Ahmed, K., & Hanifa, A. (2015). Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System. Communications on Applied Electronics, 3(1), 1-5.
Boada, B. L., Boada, M. J. L., Gauchía, A., Olmeda, E., & Díaz, V. (2015). Sideslip angle estimator based on ANFIS for vehicle handling and stability. Journal of Mechanical Science and Technology, 29(4), 1473-1481.
Boada, B. L., Boada, M. J. L., & Diaz, V. (2016). Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm. Mechanical Systems and Signal Processing, 72, 832-845.
Dadula, C., & Dadios, E. (2016). Event Detection Using Adaptive Neuro Fuzzy Inference System for a Public Transport Vehicle. In 11th International Conference of the Eastern Asia Society for Transportation Studies.
Effati, M., Rajabi, M. A., Samadzadegan, F., & Shabani, S. (2014). A geospatial neuro-fuzzy approach for identification of hazardous zones in regional transportation corridors. International Journal of Civil Engineering, 12(3), 289-303.
Eski, İ., & Yıldırım, Ş. (2017). Neural network-based fuzzy inference system for speed control of heavy duty vehicles with electronic throttle control system. Neural Computing and Applications, 28(1), 907-916.
George, A. M., Kurian, C. P., George, V. I., & George, M. A. (2015). Hardware in Loop Simulation of an Adaptive Traffic Light Control System. International Journal of Applied Engineering Research, 10(19), 39999-40004.
Gharib, Z., Bozorgi-Amirib, A., Tavakkoli-Moghaddamb, R., & Najaa, E. (2018). A cluster-based emergency vehicle routing problem in disaster with reliability. Scientia Iranica, 25(4), 2312-2330.
Ghaffari, A., Khodayari, A. R., Kamali, A., & Tajdari, F. A (2015). A New Model of Car Following Behavior Based on Lane Change Effects using Anticipation and Evaluation Idea. Iranian Journal of Mechanical Engineering, 16(2), 26-38.
Ghaffari, A., Alimardani, F., Khodayari, A., & Sadati, H. (2011a). ANFIS based modeling for overtaking maneuver trajectory in motorcycles and autos. In Proceedings of IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, November 25-27 (pp. 68-73). IEEE
Ghaffari, A., Khodayari, A., & Arvin, S. (2011). ANFIS based modeling and prediction lane change behavior in real traffic flow. In International Conference on Intelligent Computing and Intelligent Systems, Penang, Malaysia, November 25-27 (pp. 156-161).
Ghaffari, A., Alimardanii, F., & Khodayari, A. (2012). Predicting the future state of a vehicle in a stop&go behavior based on ANFIS models design. In Proceedings of 6th IEEE International Conference Intelligent Systems, Sofia, Bulgaria, September 6-8 (pp. 368-373). IEEE
Ghanbari, M., Najafi, G., Ghobadian, B., Mamat, R., Noor, M. M., & Moosavian, A. (2015). Adaptive neuro-fuzzy inference system (ANFIS) to predict CI engine parameters fueled with nano-particles additive to diesel fuel. In IOP Conference Series: Materials Science and Engineering, Kuantan, Pahang, Malaysia, August 18-19, Volume 100, No. 1 (pp. 1-8). IOP Publishing
Gokdag, M., Hasiloglu, A. S., Karsli, N., Atalay, A., & Akbas, A. (2007). Modeling of vehicle delays at signalized intersection with an adaptive neuro-fuzzy (ANFIS). Journal of Scientific and Industrial Research, 66(9), 736-740.
Hasiloglu, A. S., Gokdag, M., & Karsli, N. (2014). Comparison an artificial intelligence-based model and other models: Signalized intersection delay estimates. International Journal of Engineering and Innovative Technology, 4(3), 220-231.
Hosoz, M., Ertunc, H. M., Karabektas, M., & Ergen, G. (2013). ANFIS modelling of the performance and emissions of a diesel engine using diesel fuel and biodiesel blends. Applied Thermal Engineering, 60(1-2), 24-32.
Hosseinlou, M. H., & Sohrabi, M. (2009). Predicting and identifying traffic hot spots applying neuro-fuzzy systems in intercity roads. International Journal of Environmental Science & Technology, 6(2), 309-314.
Hou, Y., Zhang, J., Zhang, Y., & Chen, L. (2008). Integrated chassis control using ANFIS. In Proceedings of IEEE International Conference on Automation and Logistics, Qingdao, China, September 1-3, (pp. 1625-1630). IEEE
Jomaa, D., Yella, S., & Dougherty, M. (2015). Automatic Trigger Speed for Vehicle Activated Signs using Adaptive Neuro fuzzy system and Classification Regression Trees. In Fourth International Conference on Intelligent Systems and Applications INTELLI 2015, St. Julians, Malta, October 11-16 (138).
Joshi, M., & Hadi, T. H. (2015). A review of network traffic analysis and prediction techniques. arXiv preprint arXiv:1507.05722.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
Jang, J. S. (1996). Input selection for ANFIS learning. In Proceedings of IEEE 5th International Fuzzy Systems, New Orleans, LA, USA, September 11, Volume 2 (pp. 1493-1499). IEEE
Kar, S., Das, S., & Ghosh, P. K. (2014). Applications of neuro fuzzy systems: A brief review and future outline. Applied Soft Computing, 15, 243-259.
Khodayari, A., Kazemi, R., Ghaffari, A., & Manavizadeh, N. (2010). Modeling and intelligent control design of car following behavior in real traffic flow. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, Singapore, June 28-30 (pp. 261-266). IEEE
Korkmaz, E. & Akgüngör A. P. (2016). An application of adaptive neuro-fuzzy inference system in vehicle delay modeling. International Journal of Advances in Mechanical and Civil Engineering, 3(4), 59-62.
Kothandaraman, R., & Ponnusamy, L. (2012). PSO tuned adaptive neuro-fuzzy controller for vehicle suspension systems. Journal of advances in information technology, 3(1), 57-63.
Kumar, M. K., & Prasad, V. K. (2015). Driver Behavior Analysis and Prediction Models: A Survey. International Journal of Computer Science and Information Technologies, 6(4), 3328-3333.
Kukadapwar, S. R., & Parbat, D. K. (2015). Modeling of traffic congestion on urban road network using fuzzy inference system. American Journal of Engineering Research, 4(12), 143-148.
Lai, G. R., Soh, A., Sarkan, H. M. D., Rahman, R. A., & Hassan, M. K. (2015). Controlling traffic flow in multilane-isolated intersection using ANFIS approach techniques. Journal of Engineering Science and Technology, 10(8), 1009-1034.
Li, X. X., Huang, H., & Liu, C. H. (2007). The application of an ANFIS and BP neural network method in vehicle shift decision. Paper presented at the 12th World Congress in Mechanism and Machine Science, Besançon, France, June 18-21.
Liu, M., & Chen, Y. (2017). Predicting Real-Time Crash Risk for Urban Expressways in China. Mathematical Problems in Engineering, 2017, 1-10.
Massoud, M., Sahebgharani, A. & Ramesht, Z. (2014). Eveloping An Integrated Land Use-Transportation Model (Ilutm) For Analysis And Prediction Of Energy Consumption Through Adaptive Neuro-Fuzzy Inference System (ANFIS). International Journal of Recent Scientific Research, 5(1), 107-109.
Maurya, A. K., & Patel, D. K. (2015). Vehicle Classification Using Adaptive Neuro-Fuzzy Inference System (ANFIS). In K. N. Das, D. Kusum, M. Pant, B. C. Jagdish, N. Atulya (Eds.), Proceedings of Fourth International Conference on Soft Computing for Problem Solving (pp. 137-152). Springer India
Maghsoudi, R., & Moshiri, B. (2017). Applying Adaptive Network-Based Fuzzy Inference System to Predict Travel Time in Highways for Intelligent Transportation Systems. Journal of Advances in computer Research, 8(3), 87-103.
Mohebbi, M., Charkhgard, M., & Farrokhi, M. (2005). Optimal neuro-fuzzy control of parallel hybrid electric vehicles. In Proceedings of IEEE Vehicle Power and Propulsion Conference, Chicago, IL, USA, September 7 (pp. 26-30). IEEE
Nugroho, P. W., Li, W., Du, H., Alici, G., & Yang, J. (2014). An adaptive neuro fuzzy hybrid control strategy for a semiactive suspension with magneto rheological damper. Advances in Mechanical Engineering, 6, 487312.
Ottomanelli, M., Caggiani, L., Iannucci, G., & Sassanelli, D. (2010). An adaptive neuro-fuzzy inference system for simulation of pedestrians behaviour at unsignalized roadway crossings. In Gao XZ., Gaspar-Cunha A., Köppen M., Schaefer G., Wang J. (Eds.), Soft computing in industrial applications (pp. 255-262). Springer, Berlin, Heidelberg
Pamučar, D., Bozanic, D., & Komazec, N. (2016). Managing the hazardous material transportation process using the adaptive neural networks and Dijkstra algorithm. In Proceedings of II International Scientific Conference Safety and Crisis Management–Theory and Practise Safety for the Future, Obrenovac, Serbia (pp. 112-116).
Pamučar, D., Ljubojević, S., Kostadinović, D., & Đorović, B. (2016a). Cost and risk aggregation in multi-objective route planning for hazardous materials transportation—A neuro-fuzzy and artificial bee colony approach. Expert Systems with Applications, 65, 1-15.
Pamučar, D., & Ćirović, G. (2018). Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions. Decision Making: Applications in Management and Engineering, 1(1), 13-37.
Pamučar, D., Lukovac, V., & Pejčić-Tarle, S. (2013). Application of Adaptive Neuro Fuzzy Inference System in the process of transportation support. Asia-Pacific Journal of Operational Research, 30(02), 1250053.
Pérez, J., Gajate, A., Milanés, V., Onieva, E., & Santos, M. (2010). Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicles. In Proceedings of International Conference on Fuzzy Systems, Barcelona, Spain, July 18-23 (pp. 1-6). IEEE
Poor Arab Moghadam, M., Pahlavani, P., & Naseralavi, S. (2016). Prediction of car following behavior based on the instantaneous reaction time using an ANFIS-CART based model. International Journal of Transportation Engineering, 4(2), 109-126.
Rai, A. A., Pai, P. S., & Rao, B. S. (2015). Prediction models for performance and emissions of a dual fuel CI engine using ANFIS. Sadhana, 40(2), 515-535.
Rahimi, A. M. (2017). Neuro-fuzzy system modelling for the effects of intelligent transportation on road accident fatalities. Tehnički vjesnik, 24(4), 1165-1171.
Saadeddin, K., Abdel-Hafez, M. F., Jaradat, M. A., & Jarrah, M. A. (2013). Performance enhancement of low-cost, high-accuracy, state estimation for vehicle collision prevention system using ANFIS. Mechanical Systems and Signal Processing, 41(1-2), 239-253.
Saifizul, A. A., Zainon, M. Z., & Osman, N. A. (2006). An ANFIS controller for vision-based lateral vehicle control system. In Proceedings of 9th International Conference on Control, Automation, Robotics and Vision, Singapore, December 5-8 (pp. 1-4). IEEE
Seesara, S. & Gadit, J. (2015). Smart Traffic Control Using Adaptive Neuro-Fuzzy Inference System(ANFIS). International Journal of Advance Engineering and Research Development, 2(5), 295-302.
Selma, B., & Chouraqui, S. (2012). Trajectory estimation and control of vehicle Using neuro-fuzzy technique. International Journal of Advances in Engineering & Technology, 3(2), 97.
Sharma, A., Vijay, R., Bodhe, G. L., & Malik, L. G. (2014). Adoptive neuro-fuzzy inference system for traffic noise prediction. International Journal of Computer Applications, 98(13), 14-19.
Shankar, H., Raju, P. L. N., & Rao, K. R. M. (2012). Multi model criteria for the estimation of road traffic congestion from traffic flow information based on fuzzy logic. Journal of Transportation Technologies, 2(01), 50.
Shuliakov, V., Nikonov, O., & Fastovec, V. (2015). Application of Adaptive Neuro-Fuzzy Regulators in the Controlled System by the Vehicle Suspension. International Journal of Automation, Control and Intelligent Systems, 1(3), 66-72.
Sindhuja, V., & Ranjitham, G. (2014). Regenerative Braking System of Electric Vehicle Driven by BLDC Motor Using Neuro Fuzzy and PID. International Journal of Innovative Research in Science, Engineering and Technology, 3(12), 17847-17854.
Sun, R., Xie, F., Xue, D., Zhang, Y., & Ochieng, W. Y. (2017). A novel rear-end collision detection algorithm based on GNSS fusion and ANFIS. Journal of Advanced Transportation, 2017, 1-10.
Syahputra, R. (2016). Application of Neuro-Fuzzy Method for Prediction of Vehicle Fuel Consumption. Journal of Theoretical & Applied Information Technology, 86(1), 138-150.
Thipparat, T., & Thaseepetch, T. (2012). Application of Neuro-Fuzzy System to Evaluate Sustainability in Highway Design. International Journal of Modern Engineering Research, 2(5), 4153-4158.
Udofia, K. M., Emagbetere, J. O., & Edeko, F. O. (2014). Dynamic traffic signal phase sequencing for an isolated intersection using ANFIS. Automation, Control and Intelligent Systems, 2(2), 21-26.
Wannige, C. T., & Sonnadara, D. U. J. (2008). Traffic signal control based on adaptive neuro-fuzzy inference. In A. Punchihewa, B. Dias (Eds.), Proceedings of 4th International Conference on Information and Automation for Sustainability, Colombo, Sri Lanka, December 12-14 (pp. 301-306). IEEE
Wankhede, S. S., Khanapurkar, M. M., & Bajaj, P. (2011). Intelligent Speed Adaptation System with Hybrid Algorithm. International Journal of Computer Science and Network Security, 11(1), 97.
Zaki, J. F., Ali-Eldin, A. M. T., Hussein, S. E., Saraya, S. F., & Areed, F. F. (2016). Framework for Traffic Congestion Prediction. International Journal of Scientific & Engineering Research, 7(5), 1205-1210.
Özkan, İ. A., Ciniviz, M., & Candan, F. (2015). Estimating Engine Performance and Emission Values Using ANFIS/ANFIS Kullanılarak Motor Performans ve Emisyon Değerleri Tahmini. International Journal of Automotive Engineering and Technologies, 4(1), 63-67.
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2018-12-19
How to Cite
Stojčić, M. (2018). Application of ANFIS model in road traffic and transportation: a literature review from 1993 to 2018. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 40–61. https://doi.org/10.31181/oresta19012010140s
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