A Method of Vehicle Route Prediction Based on Social Network Analysis

A Method of Vehicle Route Prediction Based on Social Network Analysis

A method of vehicle route prediction based on social network analysis is proposed in this paper. The difference from proposed work is that, according to our collected vehicles’ past trips, we build a relationship model between different road segments rather than find the driving regularity of vehicl...

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Main Authors: Ning Ye, Zhong-qin Wang, Reza Malekian, Ying-ya Zhang, Ru-chuan Wang
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Journal of Sensors
Online Access:http://dx.doi.org/10.1155/2015/210298
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spelling doaj-82b53bc52ec841e2b569a029e0b777872020-11-24T22:15:46ZengHindawi LimitedJournal of Sensors1687-725X1687-72682015-01-01201510.1155/2015/210298210298A Method of Vehicle Route Prediction Based on Social Network AnalysisNing Ye0Zhong-qin Wang1Reza Malekian2Ying-ya Zhang3Ru-chuan Wang4Institute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210000, ChinaInstitute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210000, ChinaDepartment of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0028, South AfricaInstitute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210000, ChinaInstitute of Computer Science, Nanjing University of Post and Telecommunications, Nanjing 210000, ChinaA method of vehicle route prediction based on social network analysis is proposed in this paper. The difference from proposed work is that, according to our collected vehicles’ past trips, we build a relationship model between different road segments rather than find the driving regularity of vehicles to predict upcoming routes. In this paper, firstly we depend on graph theory to build an initial road network model and modify related model parameters based on the collected data set. Then we transform the model into a matrix. Secondly, two concepts from social network analysis are introduced to describe the meaning of the matrix and we process it by current software of social network analysis. Thirdly, we design the algorithm of vehicle route prediction based on the above processing results. Finally, we use the leave-one-out approach to verify the efficiency of our algorithm.http://dx.doi.org/10.1155/2015/210298
collection DOAJ
language English
format Article
sources DOAJ
author Ning Ye
Zhong-qin Wang
Reza Malekian
Ying-ya Zhang
Ru-chuan Wang
spellingShingle Ning Ye
Zhong-qin Wang
Reza Malekian
Ying-ya Zhang
Ru-chuan Wang
A Method of Vehicle Route Prediction Based on Social Network Analysis
Journal of Sensors
author_facet Ning Ye
Zhong-qin Wang
Reza Malekian
Ying-ya Zhang
Ru-chuan Wang
author_sort Ning Ye
title A Method of Vehicle Route Prediction Based on Social Network Analysis
title_short A Method of Vehicle Route Prediction Based on Social Network Analysis
title_full A Method of Vehicle Route Prediction Based on Social Network Analysis
title_fullStr A Method of Vehicle Route Prediction Based on Social Network Analysis
title_full_unstemmed A Method of Vehicle Route Prediction Based on Social Network Analysis
title_sort method of vehicle route prediction based on social network analysis
publisher Hindawi Limited
series Journal of Sensors
issn 1687-725X
1687-7268
publishDate 2015-01-01
description A method of vehicle route prediction based on social network analysis is proposed in this paper. The difference from proposed work is that, according to our collected vehicles’ past trips, we build a relationship model between different road segments rather than find the driving regularity of vehicles to predict upcoming routes. In this paper, firstly we depend on graph theory to build an initial road network model and modify related model parameters based on the collected data set. Then we transform the model into a matrix. Secondly, two concepts from social network analysis are introduced to describe the meaning of the matrix and we process it by current software of social network analysis. Thirdly, we design the algorithm of vehicle route prediction based on the above processing results. Finally, we use the leave-one-out approach to verify the efficiency of our algorithm.
url http://dx.doi.org/10.1155/2015/210298
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AT zhongqinwang amethodofvehicleroutepredictionbasedonsocialnetworkanalysis
AT rezamalekian amethodofvehicleroutepredictionbasedonsocialnetworkanalysis
AT yingyazhang amethodofvehicleroutepredictionbasedonsocialnetworkanalysis
AT ruchuanwang amethodofvehicleroutepredictionbasedonsocialnetworkanalysis
AT ningye methodofvehicleroutepredictionbasedonsocialnetworkanalysis
AT zhongqinwang methodofvehicleroutepredictionbasedonsocialnetworkanalysis
AT rezamalekian methodofvehicleroutepredictionbasedonsocialnetworkanalysis
AT yingyazhang methodofvehicleroutepredictionbasedonsocialnetworkanalysis
AT ruchuanwang methodofvehicleroutepredictionbasedonsocialnetworkanalysis
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