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Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

by Wei-Kuang Lai 1,†, Ting-Huan Kuo 1,* and Chi-Hua Chen 2,3,4,5,†
1
Department of Computer Science and Engineering, National Sun Yat-sen University, No. 70, Lienhai Road, Gushan District, Kaohsiung 804, Taiwan
2
Telecommunication Laboratories, Chunghwa Telecom Co., Ltd., No. 99, Dianyan Road, Yangmei District, Taoyuan 326, Taiwan
3
Department of Information Management and Finance, National Chiao Tung University, No. 1001 University Road, East District, Hsinchu 300, Taiwan
4
Department of Communication and Technology, National Chiao Tung University, No. 1001 University Road, East District, Hsinchu 300, Taiwan
5
Department of Electrical and Computer Engineering, National Chiao Tung University, No. 1001 University Road, East District, Hsinchu 300, Taiwan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Antonio Fernández-Caballero
Appl. Sci. 2016, 6(2), 47; https://doi.org/10.3390/app6020047
Received: 9 November 2015 / Revised: 16 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD) are proposed to analyze the signals (e.g., handovers (HOs), call arrivals (CAs), normal location updates (NLUs) and periodic location updates (PLUs)) from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (i.e., the estimated vehicle speeds from CFVD). In the experimental environment, this study adopted the practical traffic information (i.e., traffic flow and vehicle speed) from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS) communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system. View Full-Text
Keywords: vehicle speed estimation; vehicle speed forecasting; cellular floating vehicle data; intelligent transportation system; cellular networks vehicle speed estimation; vehicle speed forecasting; cellular floating vehicle data; intelligent transportation system; cellular networks
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Lai, W.-K.; Kuo, T.-H.; Chen, C.-H. Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data. Appl. Sci. 2016, 6, 47.

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