Road Segments Traffic Dependencies Study Using Cross-Correlation
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Date
2022-01-24
Journal Title
Journal ISSN
Volume Title
Publisher
University of Eloued جامعة الوادي
Abstract
Traffic Prediction on a urban road network become more complex face
to exponential growth in the volume of traffic, it is necessary to study the
relationship between road segments before the prediction calculation. The
spatial correlation theory has been well developed to interpret the dependency
for understanding how time series are related in multivariate model.
In large scale road network modeled by Multivariate Time Series, the Spatialtemporal
dependencies detection can limit the use of only data collected from
points related to a target point to be predicted. This paper present a Cross-
Correlation as method to dependency analysis between traffic road segments,
Scatterplot of Cross-Correlation is proposed to reveal the dependency, we
provide a comparative analysis between a three correlation coefficients sush
as Spearman, Kendal and Person to conclude the best one. To illustrate our
study, the methodology is applied to the 6th road ring as the most crowded
area of Beijing.
Description
Forum Intervention of Artificial Intelligence and Its Applications. Faculty of Exat science. University of Eloued
Keywords
Intelligent Transportation System, Traffic Forecasting, Time series, Cross-Correlation
Citation
Benabdallah Benarmas, Redouane, Beghdad Bey, Kadda. Road Segments Traffic Dependencies Study Using Cross-Correlation. Forum of Artificial Intelligence and Its Applications. 24-26 Jan 2022. Faculty of Exat science. University of Eloued. [visited in ../../….]. available from [copy the link here]