Altinkaya, M., & Zontul, M. (2013). Urban bus arrival time prediction: A review of computational models. International Journal of Recent Technology and Engineering (IJRTE), 2(4), 164-169.
-Amita, J., Singh, J. S., & Kumar, G. P. (2015). Prediction of bus travel time using artificial neural network. International Journal for Traffic and Transport Engineering, 5(4), 410-424.
-Balasubramanian, P., & Rao, K. R. (2015). An adaptive long-term bus arrival time prediction model with cyclic variations. Journal of Public Transportation, 18(1), 6.
-Bin, Y., Zhongzhen, Y., & Baozhen, Y. (2006). Bus arrival time prediction using support vector machines. Journal of Intelligent Transportation Systems, 10(4), 151-158.
-Buragohain, M., & Mahanta, C. (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8(1), 609-625.
-Cathey, F., & Dailey, D. J. (2003). A prescription for transit arrival/departure prediction using automatic vehicle location data. Transportation Research Part C: Emerging Technologies, 11(3-4), 241-264.
-Chen, G., Yang, X., An, J., & Zhang, D. (2011). Bus-arrival-time prediction models: Link-based and section-based. Journal of transportation engineering, 138(1), 60-66.
-Chen, M., Liu, X., Xia, J., & Chien, S. I. (2004). A dynamic bus‐arrival time prediction model based on APC data. Computer‐Aided Civil and Infrastructure Engineering, 19(5), 364-376.
-Chien, S. I.-J., Ding, Y., & Wei, C. (2002). Dynamic bus arrival time prediction with artificial neural networks. Journal of transportation engineering, 128(5), 429-438.
-Chien, S. I.-J., & Kuchipudi, C. M. (2003). Dynamic travel time prediction with real-time and historic data. Journal of transportation engineering, 129(6), 608-616.
-Gurmu, Z. K., & Fan, W. D. (2014). Artificial neural network travel time prediction model for buses using only GPS data. Journal of Public Transportation, 17(2), 3.
-Hua, X., Wang, W., Wang, Y., & Ren, M. (2018). Bus arrival time prediction using mixed multi-route arrival time data at previous stop. Transport, 33(2), 543–554-543–554.
Iphar, M., Yavuz, M. & Ak, H. (2008). Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environ Geol 56, 97–107.
-Jang, J. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on systems, man and cybernetics, 23(3), 665-685.
-Jeong, R., & Rilett, L. (2004). The prediction of bus arrival time using AVL data. Paper presented at the 83rd Annual General Meeting, Transportation Research Board, National Research Council, Washington DC, USA.
-Jeong, R., & Rilett, R. (2004). Bus arrival time prediction using artificial neural network model. Paper presented at the Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No. 04TH8749).
-Karayiannis, N. B., & Venetsanopoulos, A. N. (1993). Artificial neural networks: learning algorithms, performance evaluation and applications: Kluwer Academic Publishers Group.
Kademi, N., Rajabi, M., Mohaymani, S.A., and Samadzad, M. (2016). Day-to-day travel time perception modeling using an adaptive-network-based fuzzy inference system (ANFIS). EURO Journal on Transportation and Logistics.Vlo.5. No.1, pp.25-52
-Khamparia, A., & Choudhary, R. (2019). Prediction of Bus Arrival Time Using Intelligent Computing Methods. In Pervasive Computing: A Networking Perspective and Future Directions (pp. 127-143): Springer.
-Khetarpaul, S., Gupta, S., Malhotra, S., & Subramaniam, L. V. (2015). Bus arrival time prediction using a modified amalgamation of fuzzy clustering and neural network on spatio-temporal data. Paper presented at the Australasian Database Conference.
-Kumar, S. V., & Vanajakshi, L. (2012). Application of multiplicative decomposition and exponential smoothing techniques for bus arrival time prediction.
-Kumar, V., Kumar, B. A., Vanajakshi, L., & Subramanian, S. C. (2014). Comparison of model based and machine learning approaches for bus arrival time prediction. Paper presented at the Proceedings of the 93rd Annual Meeting.
-Li, J., Gao, J., Yang, Y., & Wei, H. (2017). Bus arrival time prediction based on mixed model. China Communications, 14(5), 38-47.
-Lin, Y., Yang, X., Zou, N., & Jia, L. (2013). Real-time bus arrival time prediction: Case study for Jinan, China. Journal of transportation engineering, 139(11), 1133-1140.
Moayedi, H., Raftari, M., Sharifi, A. et al.
(2020). Optimization of ANFIS with GA and PSO estimating α ratio in driven piles. Engineering with Computers 36, 227–238
-Nguyen, H. T., Prasad, N. R., Walker, C. L., & Walker, E. A. (2003). A First Course in Fuzzy and Neural Control (Ch. 7, pp.229-247): Chapman & Hall/CRC.
-Padmanaban, R., Divakar, K., Vanajakshi, L., & Subramanian, S. C. (2010). Development of a real-time bus arrival prediction system for Indian traffic conditions. IET intelligent transport systems, 4(3), 189-200.
-Pan, J., Dai, X., Xu, X., & Li, Y. (2012). A self-learning algorithm for predicting bus arrival time based on historical data model. Paper presented at the 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems.
-Ramakrishna, Y., Ramakrishna, P., Lakshmanan, V., & Sivanandan, R. (2006). Bus travel time prediction using GPS data. Proceedings Map India.
-Ramkumar, N., & Chaudhari, A. (2019). Urban Bus Arrival Time Prediction Using Linear Regression and Kalman Filter—A Comparison. In Soft Computing and Signal Processing (pp. 279-287): Springer.
Saif, S., Das, P. & Biswas, S. (2021). A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast. J. Inst. Eng. India Ser. B 102, 1123–1136.
-Sapankevych, N. I., & Sankar, R. (2009). Time series prediction using support vector machines: a survey. IEEE Computational Intelligence Magazine, 4(2), 24-38.
-Saxena, N. K., & Kumar, A. (2016). Reactive power control in decentralized hybrid power system with STATCOM using GA, ANN and ANFIS methods. International Journal of Electrical Power & Energy Systems, 83, 175-187.
-Sun, C., Sun, M., & Cui, Q. (2011). Bus arrival time prediction based on error weighted of historical and real-time data. Paper presented at the International Conference on Information Technology and Computer Science, 3rd (ITCS 2011).
-Sun, D., Luo, H., Fu, L., Liu, W., Liao, X., & Zhao, M. (2007). Predicting bus arrival time on the basis of global positioning system data. Transportation Research Record, 2034(1), 62-72.
-Suwardo, W., Napiah, M., & Kamaruddin, I. (2010). ARIMA models for bus travel time prediction. Journal of the institute of engineers Malaysia, 49-58.
-Takagi, T., & Sugeno, M. (1985). Derivation of fuzzy control rules from human operator’s control actions. Paper presented at the Proc. IFAC Symp. Fuzzy Inform., Knowledge Representation and Decision Analysis
-Treethidtaphat, W., Pattara-Atikom, W., & Khaimook, S. (2017). Bus arrival time prediction at any distance of bus route using deep neural network model. Paper presented at the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).
-Vanajakshi, L., Subramanian, S. C., & Sivanandan, R. (2009). Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses. IET intelligent transport systems, 3(1), 1-9.
-Weigang, L., Koendjbiharie, W., de M Juca, R., Yamashita, Y., & MacIver, A. (2002). Algorithms for estimating bus arrival times using GPS data. Paper presented at the Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.
-Xu, H., & Ying, J. (2017). Bus arrival time prediction with real-time and historic data. Cluster Computing, 20(4), 3099-3106.
-Yang, J.-S. (2005). Travel time prediction using the GPS test vehicle and Kalman filtering techniques. Paper presented at the Proceedings of the 2005, American Control Conference, 2005.
-Yin, T., Zhong, G., Zhang, J., He, S., & Ran, B. (2017). A prediction model of bus arrival time at stops with multi-routes. Transportation research procedia, 25, 4623-4636.
-Yu, B., Yang, Z. Z., Chen, K., & Yu, B. (2010). Hybrid model for prediction of bus arrival times at next station. Journal of Advanced Transportation, 44(3), 193-204.
Yu, Bin, William HK Lam, and Mei Lam Tam. Bus arrival time prediction at bus stop with multiple routes. Transportation Research Part C: Emerging Technologies 19.6 (2011): 1157-1170.
-Zaki, M., Ashour, I., Zorkany, M., & Hesham, B. (2013). Online bus arrival time prediction using hybrid neural network and Kalman filter techniques. International Journal of Modern Engineering Research, 3(4), 2035-2041.
-Zheng, C.-J., Zhang, Y.-H., & Feng, X.-J. (2012). Improved iterative prediction for multiple stop arrival time using a support vector machine. Transport, 27(2), 158-164.
-Zhou, X., Peng, C., Song, X., & Yang, X. (2011). Prediction model of dynamic bus arrival time based on the front bus data. Traffic & Transportation, 12, 52-56.
-Zhou, Y., Yao, L., Chen, Y., Gong, Y., & Lai, J. (2017). Bus arrival time calculation model based on smart card data. Transportation Research Part C: Emerging Technologies, 74, 81-96.
-Zhu, T., Ma, F., Ma, T., & Li, C. (2011). The prediction of bus arrival time using global positioning system data and dynamic traffic information. Paper presented at the 2011 4th Joint IFIP Wireless and Mobile Networking Conference (WMNC 2011).