Research on Multi-source Traffic Flow Data Fusion Based on Linear Regression: A Case Study in Mega-city

DOI

10.2991/ame-16.2016.210How to use a DOI?

Keywords

Multi-source data fusion, Regression, Traffic parameters, Case study.

Abstract

Data fusion of traffic parameters such as speed and density is a crucial study in intelligent transportation system, yet complicated to formulate mathematically. The aim of this paper is to study and model the multi-source fusion traffic flow data. Based on the data detected in a mega-city, the regression model is developed to solve the defects in the traditional models in this paper. The result demonstrates that the proposed models can pass the effectiveness test and have a favorable fusion effect, whose fusion result can meet the demand for precision. Apart from the accuracy demand, the proposed models can solve the fusion problem briefly and effectively so that it can be used in the practical engineering application. This study concludes that the proposed model has expressed well the data fusion for the practical multi-source data detected in the mega-city, which proves the model has good effectiveness and applicability.

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  - Xiao-Quan Wang
AU  - Chun-Fu Shao
AU  - Ji Xun
AU  - Zong-Jie Liu
AU  - Yuan Yuan
PY  - 2016/06
DA  - 2016/06
TI  - Research on Multi-source Traffic Flow Data Fusion Based on Linear Regression: A Case Study in Mega-city
BT  - Proceedings of the 2nd Annual International Conference on Advanced Material Engineering (AME 2016)
PB  - Atlantis Press
SP  - 1293
EP  - 1298
SN  - 2352-5401
UR  - https://doi.org/10.2991/ame-16.2016.210
DO  - 10.2991/ame-16.2016.210
ID  - Wang2016/06
ER  -