Road Segments Traffic Dependencies Study Using Cross-Correlation
Benabdallah Benarmas, Redouane, Beghdad Bey, Kadda
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.
Details
| Original language | English |
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| Event | Forum of Artificial Intelligence and Its Applications |
| Publication status | Published — January 2022 |