English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

It is meaningful to detect outliers in traffic data for traffic management.However, this is a massive task for people from large-scale database todistinguish outliers. In this paper, we present two methods: Kernel SmoothingNa\"ive Bayes (NB) method and Gaussian Mixture Model (GMM) method toautomatically detect any hardware errors as well as abnormal traffic events intraffic data collected at a four-arm junction in Hong Kong. Traffic data wasrecorded in a video format, and converted to spatial-temporal (ST) trafficsignals by statistics. The ST signals are then projected to a two-dimensional(2D) (x,y)-coordinate plane by Principal Component Analysis (PCA) for dimensionreduction. We assume that inlier data are normal distributed. As such, the NBand GMM methods are successfully applied in outlier detection (OD) for trafficdata. The kernel smooth NB method assumes the existence of kernel distributionsin traffic data and uses Bayes' Theorem to perform OD. In contrast, the GMMmethod believes the traffic data is formed by the mixture of Gaussiandistributions and exploits confidence region for OD. This paper would addressthe modeling of each method and evaluate their respective performances.Experimental results show that the NB algorithm with Triangle kernel and GMMmethod achieve up to 93.78% and 94.50% accuracies, respectively.

Outlier Detection In Large-scale Traffic Data By Na\"ive Bayes Method and Gaussian Mixture Model Method