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A Method for Driving Route Predictions Based on Hidden Markov Model

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  • Ning Ye
  • Zhong-qin Wang
  • Reza Malekian
  • Qiaomin Lin
  • Ru-chuan Wang

Abstract

We present a driving route prediction method that is based on Hidden Markov Model (HMM). This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K -means++ and the add-one (Laplace) smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

Suggested Citation

  • Ning Ye & Zhong-qin Wang & Reza Malekian & Qiaomin Lin & Ru-chuan Wang, 2015. "A Method for Driving Route Predictions Based on Hidden Markov Model," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-12, October.
    • Handle: RePEc:hin:jnlmpe:824532
    DOI: 10.1155/2015/824532
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    Cited by:

    1. Shukun Lai & Hongke Xu & Fumin Zou & Yongyu Luo & Zerong Hu & Huan Zhong, 2024. "Expressway Vehicle Trajectory Prediction Considering Historical Path Dependencies," Sustainability, MDPI, vol. 16(11), pages 1-24, May.

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