The Bus Arrival Time Service Based on Dynamic Traffic Information
Abstract: The bus arrival time (BAT) service is a key service to improve public transport attractiveness by providing users with real-time bus arrival information which can help them to arrange their bus travel schedule intelligently. Thus the technique of real-time bus arrival prediction has become a research hotspot in the community of Intelligent Transport Systems (ITS) nowadays. In this paper, a novel model on bus arrival time prediction is proposed. The model proposes a complete set of programs to solve BAT prediction for large-scale real-time traffic information calculating. It adopts an effective algorithm calculating vehicle’s driving direction real-timely. BAT is calculated based on dynamic traffic information and visual prediction is a way to complement when GPS information is not arrived as expected. Experimental results indicate that the model has considerable efficiency in accuracy (over 85.1%) and computational speed (max 5000 GPS records per second).
文章引用: 董 健 , 王祖云 , 陈智宏 , 庞松松 (2012) 基于动态交通信息的公交到站预测服务。 无线通信， 2， 90-96. doi: 10.12677/hjwc.2012.24017
 W. H. Lin, J. Zeng. An experimental study of real-time bus arrival time prediction with GPS data. Transportation Research Record, 1999, 1666: 101-109.
 Z. Wall, D. J. Dailey. An algorithm for predicting arrival time of mass transit vehicle using automatic vehicle location data. Proceedings of Transportation Research BOARD 78th Annual Meeting, Washington DC, 10-14 January 1999.
 L. Vanajakshi, S. C. Subramanian and R. Sivanandan. Short- term prediction of travel time for Indian traffic conditions using buses as probe vehicles. Transportation Research Board, the 87th Annual Meeting (CD-ROM), Washington DC, 13-17 January 2008: 18.
 R. Jeong, L. R. Rilett. Bus arrival time prediction model for real-time applications. Transportation Research Record: Journal of the Transportation Research Board, 2005, 1927: 195-204.
 M. Chowdhury, A. Sadek, Y. Ma, N. Kanhere and P. Bhavsar. Applications of artificial intelligence paradigms to decision support in real-time traffic management. Transportation Re- search Record: Journal of the Transportation Research Board, 2006, 1968: 92-98.
 J. S. Yang. Travel time prediction using the GPS test vehicle and Kalman filtering techniques. Proceedings of the American Con- trol Conference, Portland, 8-10 June 2005: 2128-2133.
 A. Farhan, A. Shalaby and T. Sayed. Bus travel time prediction using GPS and APC. ASCE 7th International Conference on Applications of Advanced Technology in Transportation, Cam- bridge, 5-7 August 2002.
 F. W. Cathey, D. J. Dailey. A prescription for transit arrival/de- parture prediction using automatic vehicle location data. Trans- portation on Research Part C: Emerging Technologies, 2003, 11(3): 241-264.
 H. Liu, H. J. van Zuylen, H. V. Lint and M. Salomons. Urban arterial travel time prediction with state-space neural networks and Kalman filters. Transportation Research Board, Annual Meeting (CD-ROM), 2006, 1968: 99-108.
 R. P. S. Padmanaban, L. Vanajakshi. Estimation of bus travel time incorporating dwell time for APTS applications. IEEE Intelligent Vehicles Symposium, Xi’an, 3-5 June 2009: 955-959.