基于动态交通信息的公交到站预测服务
The Bus Arrival Time Service Based on Dynamic Traffic Information
DOI: 10.12677/hjwc.2012.24017, PDF, HTML, 下载: 3,715  浏览: 10,009 
作者: 董 健, 王祖云, 陈智宏, 庞松松：北京航空航天大学软件环境国家重点实验室
关键词: 公交到站预测；GPS数据；动态交通信息；Bus Arrival Time Prediction; GPS Data; Dynamic Traffic Information
摘要: 公交到站时间(BAT)服务是提高公众交通吸引力的关键服务之一。服务给使用者提供实时车辆到站信息能够使用户更好的安排自己的公共交通行程。因此，实时公交到站时间预测技术在智能交通领域已经成为研究的热点。在本文中采用了新的公交到站时间预测模型。该模型提出了一套完整的算法来解决BAT预测中大规模的实时交通信息的计算，采用了高效的算法来实时纠正车辆的行驶方向。动态交通信息作为数据基础支撑BAT的计算。同时，采用虚拟预测来处理GPS数据丢失的情况保证了服务提供的稳定性。实验结果表明模型拥有较高的准确率(超过85.1%)和计算速度(最高处理5000条每秒)。
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. 基于动态交通信息的公交到站预测服务[J]. 无线通信, 2012, 2(4): 90-96. http://dx.doi.org/10.12677/hjwc.2012.24017

参考文献

[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.