A Method for Driving Route Predictions Based on Hidden Markov Model
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 ar...
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2015-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/824532 |
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doaj-1a20c90707794c1e8b3ebc7722fa9a802020-11-25T00:59:01ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/824532824532A Method for Driving Route Predictions Based on Hidden Markov ModelNing Ye0Zhong-qin Wang1Reza Malekian2Qiaomin Lin3Ru-chuan Wang4Institute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210003, ChinaInstitute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210003, ChinaDepartment of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South AfricaInstitute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210003, ChinaInstitute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210003, ChinaWe 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.http://dx.doi.org/10.1155/2015/824532 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ning Ye Zhong-qin Wang Reza Malekian Qiaomin Lin Ru-chuan Wang |
spellingShingle |
Ning Ye Zhong-qin Wang Reza Malekian Qiaomin Lin Ru-chuan Wang A Method for Driving Route Predictions Based on Hidden Markov Model Mathematical Problems in Engineering |
author_facet |
Ning Ye Zhong-qin Wang Reza Malekian Qiaomin Lin Ru-chuan Wang |
author_sort |
Ning Ye |
title |
A Method for Driving Route Predictions Based on Hidden Markov Model |
title_short |
A Method for Driving Route Predictions Based on Hidden Markov Model |
title_full |
A Method for Driving Route Predictions Based on Hidden Markov Model |
title_fullStr |
A Method for Driving Route Predictions Based on Hidden Markov Model |
title_full_unstemmed |
A Method for Driving Route Predictions Based on Hidden Markov Model |
title_sort |
method for driving route predictions based on hidden markov model |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2015/824532 |
work_keys_str_mv |
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1725219133979623424 |