Title page for etd-0627117-142742


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URN etd-0627117-142742
Author Ting-Huan Kuo
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2016
Semester 2
Degree Ph.D.
Type of Document
Language English
Title Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data for Express Roads
Date of Defense 2017-06-09
Page Count 91
Keyword
  • intelligent transportation system
  • cellular networks
  • cellular floating vehicle data
  • vehicle speed estimation
  • vehicle speed forecasting
  • Abstract In recent years, cellular floating vehicle data (CFVD) technology has been a popular traffic information estimation technique to analyze cellular network data and to provide real-time traffic information with the higher coverage and the lower cost. Therefore, this study proposes vehicle positioning and speed estimation methods to capture CFVD and to track mobile stations (MSs) for intelligent transportation system (ITS). Three features of CFVD which include the IDs, sequence, and cell dwell time of connected cells from the signals of MS’s communication are extracted and analyzed. The feature of sequence can be used to judge express road direction, and the feature of cell dwell time can be applied to discriminate proximal express roads. Furthermore, 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, the experiment results showed the accuracy of the proposed vehicle positioning method which was 100% better than other popular machine learning methods (e.g., logistic regression , and back-propagation neural network). Furthermore, 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 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.
    Advisory Committee
  • Huang Chung-Ming - chair
  • LChung-Nan - co-chair
  • Li Shu-Min - co-chair
  • Wang You-Chiun - co-chair
  • Tong Sheau-Ru - co-chair
  • Tsai Chun-Yi - co-chair
  • Kung Hsu-Yang - co-chair
  • Lai Wei-Kuang - advisor
  • Files
  • etd-0627117-142742.pdf
  • Indicate in-campus at 2 year and off-campus access at 3 year.
    Date of Submission 2017-07-27

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