An Improvement of the Arrival Time Estimation of an EV System Using Hybrid Approach with ANN

Settha Tangkawanit, Sahakorn Buangam, Surachet Kanprachar


In this research, an approach for estimating the travelling time used by an electric vehicle and selecting an updating period of such vehicle to a particular location are proposed. The real-time based and historical data based techniques are used with Artificial Neural Network (ANN) as a process for memorizing the offset for estimating the vehicle velocity and updating period in the following round. The route of the vehicle, the time of the day, and the day of the week are taken into account. The proposed approach is analyzed and compared to the conventional approach by testing with the data (time and position of the vehicle) collected from running the vehicle around Naresuan University campus. The data was recorded every 1 second for 3 months using the wireless transmitter installed in the vehicle. From the results, it is found that, using the proposed approach, the bandwidth utilization of the network and the error of the displayed time are improved by 75%. With this significant improvement, if the proposed approach is further developed or utilized, the public vehicle service’s reliability could be increased; thus, less number of private vehicles utilized; resulting in a good environment saving.


Electric Vehicle; Arrival Time; Updating Time; Real Time Monitoring System; Artificial Neural Network.;

Full Text:



S. Tangkawanit, K. Wataniyanon and S Kanprachar, “An Improvement of Electric Vehicle Data Updating Time using Fuzzy Logic,” International Electrical Engineering Congress 2014 (iEECON 2014), Pattaya, Thailand, March 19 – 21, 2014.

D. Chen, K. Zhang and T. Liao, “Practical travel time prediction algorithms based on neural network and data fusion for urban expressway,” Natural Computation (ICNC), 2010 Sixth International Conference, Yantai, Shandong, China, August 10 – 12, 2010.

J. Gong, M. Liu and S. Zhang, “Hybrid dynamic prediction model of bus arrival time based on weighted of historical and real-time GPS data,” Control and Decision Conference (CCDC), 2013 25th Chinese, Guiyang, China, May 25 – 27, 2013.

H. Niu, W. Guan and J.Ma, “Design and Implementation of Bus Monitoring System Based on GPS for Beijing Olympics,” Computer Science and Information Engineering, 2009 WRI World Congress, Los Angeles, CA, March 31 - April 2 2009.

A. M. Mustapha, MA Hannan, A. Hussain and H. Basri, “UKM Campus Bus Identification and Monitoring Using RFID and GIS,” Research and development (SCOReD), 2009 IEEE Student Conference, Serdang, Malaysia, November 16 – 18, 2009.

M A Hannan, A. M. Mustapha, A. Hussain and H. Basri, “Communication Technologies for an Intelligent Bus Monitoring system,” Sustainable technologies (WCST), 2011 World Congress, London, UK, November 7 - 11, 2011.

M H. Feng, L. Lulu, Y. Heng and H. Xia, “Bus Monitoring System Based On ZigBee And GPRS,” Computer Distributed Control and Intelligent Environmental Monitoring (CDCIEM), 2012 International Conference, Hunan, China, March 5 - 6, 2012.

P. Chen, Z. Lu and J. Gu, “Vehicle Travel Time Prediction Algorithm Based on Historical Data and Shared Location,” INC, IMS and IDC, 2009. NCM'09. Fifth International Joint Conference, Seoul, South Korea, August 25 – 27, 2009.

H. Yu, R. Xiao, Y. Du, Z. He, “A Bus-Arrival Time Prediction Model Based on Historical Traffic Patterns,” Computer Sciences and Applications (CSA), 2013 International Conference, Wuhan, China, December 14 – 15, 2013.

O. Cats and G. Loutos, “Real- Time Bus Arrival Information System -an Empirical Evaluation,” Intelligent Transportation Systems -(ITSC), 2013 16th International IEEE Conference, The Hague, Netherland, October 6 – 9, 2013.

M. F. Oshyani1 and O. Cats, “Real-Time Bus Departure Time Predictions: Vehicle Trajectory and Countdown Display Analysis,” Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference, Qingdao, China, October 8 – 11, 2014.

R. Jeong and L. R. Rilett, “Bus Arrival Time Prediction Using Artificial Neural Network Model,” Intelligent Transportation Systems, 2004. Proceedings.The 7th International IEEE Conference, Washington, WA, USA, October 3 – 6, 2004.

V. Turchenko and V. Demchuk, “Neural-Based Vehicle Travel Time Prediction Noised by Different Influence Factors,” Modern Problems of Radio Engineering, Telecommunications, and Computer Science, 2006. TCSET 2006. International Conference, Lviv-Slavsko, February 28 - March 4, 2006.

J. Park, D. Li, Y. L. Murphey, J. Kristinsson, R. McGee, M. Kuang, and T. Phillips, “Real Time Vehicle Speed Prediction using a Neural Network Traffic Model,” neural Networks (IJCNN), The 2011 International Joint Conference, San Jose, CA, July 31 - August 5, 2011.

T. Liu, J. Ma, W. Guan, Y. Song and H. Niu, “Bus Arrival Time Prediction Based on the k-Nearest Neighbor Method,” Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference, Harbin, China, June 23 – 26, 2012.

Z. He, H. Yu, Y. Du, and J. Wang, “SVM based Multi-index Evaluation for Bus Arrival Time Prediction,” ICT Convergence (ICTC), 2013 International Conference, Jeju, South Korea, October 14 – 16, 2013.

J. Zhang, L. Yan, Y. Han, and JJ. Zhang, “Study on the Prediction Model of Bus Arrival Time,” Management and Service Science, 2009. MASS'09. International Conference, Wuhan, September 20 -22, 2009.

S.V. Kumar, L. Vanajakshi, and S. C. Subramanian, “A Model Based Approach to Predict Stream Travel Time using Public Transit as Probes,” Intelligent Vehicles Symposium (IV), 2011 IEEE, BadenBaden, Germany, June 5 - 9, 2011.

H. Ji, A. Xu, X. Sui and L. Li, “The Applied Research of Kalman in the Dynamic Travel Time Prediction,” Geoinformatics, 2010 18th International Conference, Beijing, China, June 18 - 20, 2010.

J.S. Yang, “Travel Time Prediction Using the GPS Test Vehicle and Kalman Filtering Techniques,” American Control Conference, 2005. Proceedings of the 2005, Portland, OR, USA, June 8 - 10, 2005.

T. Yamaguchi, S. Inagaki, T. Suzuki, A. Ito, M. Fujita, and J. Kanamori, “Maximum Likelihood Estimation of Departure and Travel Time of Individual Vehicle using Statistics and Dynamic Programming,” Intelligent Transportation Systems - (ITSC), 2013 16th International IEEE Conference, The Hague, Netherland, October 6 – 9, 2013.

J. Dong, L. Zou and Y. Zhang, “Mixed Model For Prediction of Bus Arrival Times,” Evolutionary Computation (CEC), 2013 IEEE Congress, Cancun, Mexico, May June 20 - 23, 2013.

S. Maiti, A. Pal, A.Pal, T Chattopadhyay, and A. Mukherjee, “Historical Data based Real Time Prediction of Vehicle Arrival Time,” Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference, Qingdao, China, October 8 - 11, 2014.

Sanjit K. Mitra., (2011) Digital Signal Processing: A Computer-Based Approach (Fourth Edition) New York , USA : The McGraw-Hill companies., Pages 145 - 153.

Vinay K. Ingle and John G. Proakis., (2012) Essential of Digital Signal Processing using MATLAB (Second Edition) Canada: Cengage Learning, Page 52.

Kenton Wiliston., (2009) Digital Signal Processing: World Class Design (First Edition) Burlington, USA: The Newnes - Elsevier, Pages 131 - 133.

Daniel Graupe, (2007) Principles of Artificial Neural Networks: Advanced Series on Circuit and Systems Vol-6 (Second Edition) Singapore: World Scientific Publishing companies, Pages 1 - 94. [28] Jose’ C. Principe, Neil R. Euliano, and W. Curt Lefebvre, (2000) Neural & Adaptive Systems: Fundamentals Through Simulations (First Edition) New York, USA: John Wiley & Sons Inc., Pages 101 -159.

Simon Haykin., (2000) Neural Networks: A Comprehensive Foundation (First Edition) UK: Prentice – Hall Inc., Pages 156 - 202.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131