|Misbahuddin||Department of Electrical Engineering, Faculty of Engineering, University of Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
|Riri Fitri Sari||Department of Electrical Engineering, Faculty of Engineering, University of Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
Providing travelers with accurate bus arrival time is an essential need to plan their traveling and reduce long waiting time for buses. In this paper, we proposed a new approach based on a Bayesian mixture model for the prediction. The Gaussian mixture model (GMM) was used as the joint probability density function of the Bayesian network to formulate the conditional probability. Furthermore, the Expectation maximization (EM) Algorithm was also used to estimate the new parameters of the GMM through an iterative method to obtain the maximum likelihood estimation (MLE) as a convergence of the algorithm. The performance of the prediction model was tested in the bus lanes in the University of Indonesia. The results show that the model can be a potential model to predict effectively the bus arrival time.
Arrival time prediction, Bayesian network, Gaussian mixture model
Chen, G., Yang, X., An, J., Zhang, D., 2012. Bus-arrival-time Prediction Models: Link-based and Section-based. Journal of Transportation Engineering, Volume 138(1), pp. 60–66
Haitao, Yu, Randong, Xiao, Yong, Du, Zhiying, He, 2013. A Bus-arrival Time Prediction Model Based on Historical Traffic Patterns. In: Computer Sciences and Applications (CSA), 2013 International Conference on Wuhan
Jeong, R., Rilett, L.R., 2004. Bus Arrival Time Prediction using Artificial Neural Network Model. In: Intelligent Transportation Systems, 2004. Proceedings. The 7th International IEEE Conference on Washington, D.C.
Jian, D., Lu, Z., Yan, Z., 2013. Mixed Model for Prediction of Bus Arrival Times. In: Evolutionary Computation (CEC), 2013 IEEE Congress on Cacun
Lingli, D., Zhaocheng, H., Renxin, Z., 2013. The Bus Travel Time Prediction based on Bayesian Networks. In: Information Technology and Applications (ITA), 2013 International Conference on Chengdu
Pengfei, Z., Yuanqing, Z., Mo, L., 2014. How Long to Wait? Predicting Bus Arrival Time with Mobile Phone Based Participatory Sensing. Mobile Computing, IEEE Transactions on, Volume 13(6), pp. 1228–1241
Pernkopf, F., Wohlmayr, M., Tschiatschek, S., 2012. Maximum Margin Bayesian Network Classifiers. IEEE Transactions Pattern Analysis and Machine Intelligence, Volume 34(3), pp. 521–532
Roberts, S.J., Husmeier, D., Rezek, I., Penny, W., 1998. Bayesian Approaches to Gaussian Mixture Modeling. IEEE Transactions Pattern Analysis and Machine Intelligence, Volume 20(11), pp. 1133–1142
Shiliang, S., Changshui, Z., Guoqiang, Y., 2006. A Bayesian Network Approach to Traffic Flow Forecasting. IEEE Transactions Intelligent Transportation Systems, Volume 7(1), pp. 124–132
Tao, L., Jihui, M., Wei, G., Yue, S., Hu, N., 2012. Bus Arrival Time Prediction based on the k-Nearest Neighbor Method. In: Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference, Harbin, China
Tongyu, Z., Jian, D., Jian, H., Songsong, P., Bowen, D., 2012. The Bus Arrival Time Service based on Dynamic Traffic Information. In: The Application of Information and Communication Technologies (AICT), 2012 6th International Conference on Tbilisi
Yu, B., Lam, W.H.K., Tam, M.L., 2011. Bus Arrival Time Prediction at Bus Stop with Multiple Routes. Transportation Research Part C: Emerging Technologies, Volume 19(6), pp. 1157–1170