Vol 6, No 6 (2015) > Electrical, Electronics and Computer Engineering >

Bayesian Mixture Model for Prediction of Bus Arrival Time

Misbahuddin Misbahuddin, Riri Fitri Sari

 

Abstract:

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.

Keywords: Arrival time prediction; Bayesian network; Gaussian mixture model

Full PDF Download

References


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