Collaborative prediction for bus arrival time based on CPS

Abstract

To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in Shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction.

Keywords

prediction model / cyber-physical system architecture / bus arrival time / collaborative prediction

References

[1] StaackmannM, BorY L, YunD Y Y. Dynamic driving cycle analyses using electric vehicle time-series data [C]. Energy Conversion Engineering Conference. Honolulu, 19972014-2018

[2] VemulapalliP K, DeanA J, BrennanS N. Pitch based vehicle localization using time series subsequence matching with multi-scale extrema features [C]. American Control Conference (ACC). San Francisco, 20112405-2410

[3] JiH-f, XuA-g, SuiX, LiL-yong. The applied research of Kalman in the dynamic travel time prediction [C]. Geoinformatics 18th International Conference. Beijing, 20101-5

[4] LiuZ-g, YangHua. A new vehicle tracking method with region matching based on Kalman forecasting model [C]. Networking, Sensing and Control (ICNSC) International Conference. Chicago, 2010559-563

[5] PanS-h, LinC, LiM-h, ChenL-zhu. Virtual sensor for vehicle sideslip angle based on extended Kalman filter [C]. Measuring Technology and Mechatronics Automation (ICMTMA), 3rd International Conference. Shanghai, 20111131-1134

[6] KeyvanG, SepidehS, MohamedK, FakhriK, FarookS. Vehicle localization in VANETs using data fusion and V2V communication [C]. The Second ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications. New York, 2012123-130

[7] XuJ, FeiJ-tao. Neural network predictive control of vehicle suspension [C]. Information Science and Engineering (ICISE), 2nd International Conference. Hangzhou, 20101319-1322

[8] ChenD-w, ZhangK, LiaoTao. Practical travel time prediction algorithms based on neural network and data fusion for urban expressway [C]. Natural Computation (ICNC), Sixth International Conference. Yantai, 20101754-1758

[9] JungmeP, LiD, MurpheyY L, KristinssonJ, McgeeR, KuangM, PhillipsT. Real time vehicle speed prediction using a neural network traffic model [C]. Neural Networks International Joint Conference. San Jose, 20112991-2996

[10] SunZ-q, FengJ-q, LiuWei. Travel time forecasting based on phase space reconstruction and SVM [C]. Optoelectronics and Image Processing (ICOIP) International Conference. Haiko, 2010692-695

[11] ChenX-m, GongH-b, WangJ-nan. BRT vehicle travel time prediction based on SVM and Kalman filter [J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(4): 29-34

[12] SajalK D, KrishnaK, ZhangNanHandbook on securing cyber-physical critical infrastructure [M], 2012, USA, Morgan Kaufmann: 1-3

[13] WanJ-f, SuoH, YanH-h, LiuJ-qi. A general test platform for cyber-physical systems: Unmanned vehicle with wireless sensor network navigation original research article [J]. Procedia Engineering, 2011, 24(1): 123-127

[14] TanY, GoddardS, PerezLc. A prototype architecture for cyber-physical systems [J]. ACM SIGBED Review, 2008, 5(1): 26

[15] PanS-l, JiangB, ZouN, JiaLei. Average travel speed estimation using multi-type floating car data [C]. The IEEE International Conference on Information and Automation. Shenzhen, 2011192-197

[16] YaoB-z, YuB, YangZ-zhen. Dynamic holding strategy based on bus arrival time prediction at stops [J]. Journal of Beijing University of Technology, 2011, 37(6): 869-874

[17] MeiL T, WilliamH K L. Application of automatic vehicle identification technology for real-time journey time estimation [J]. Information Fusion, 2011, 12(1): 11-19

[18] SinnM, JiW Y, CalabreseF, BouilletE. Predicting arrival times of buses using real-time GPS measurements [C]. Intelligent Transportation Systems (ITSC) 15th International IEEE Conference. Anchorage, 20121227-1232