Prediction of Transportation Network Based on PageRank Algorithm

DOI

10.2991/icamcs-16.2016.18How to use a DOI?

Keywords

Data Mining, Complex Network Analysis, PageRank Algorithm, Congestion Prediction

Abstract

As a new research field, network science is gaining more and more attention with the networking of human society. Urban traffic is a kind of typical complex network, the analysis of its property is becoming a hot topic. The research of the urban traffic network congestion is presented in this paper. Through in-depth analysis of the urban traffic network features and a variety of circumstances which lead to traffic congestion, we build a complex network model which the nodes represent specific roads. In this model, we get the PageRank algorithm improved which in the field of search engines, and we apply it to a transportation network. Then we predict the situations of urban traffic network congestion with this model. Finally, comprehensive tests are conducted with the data from the monitoring of real roads. The experimental results show that the model proposed in this paper can simulate the change of traffic flow, which has a good forecast effect on the urban traffic congestion and also has a referential value to the planning of urban traffic.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tongbo Zhang
AU  - Guangli Li
AU  - Yue Xu
AU  - Yang Yang
AU  - Shuai Lv
PY  - 2016/06
DA  - 2016/06
TI  - Prediction of Transportation Network Based on PageRank Algorithm
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 88
EP  - 94
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.18
DO  - 10.2991/icamcs-16.2016.18
ID  - Zhang2016/06
ER  -