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The New England Journal of Statistics in Data Science


Detection of Anomalies in Traffic Flows with Large Amounts of Missing Data
Volume 1, Issue 1 (2023), pp. 84–94
Pub. online: 11 January 2023      Type: Methodology Article      Open accessOpen Access
Area: Statistical Methodology
Accepted
4 January 2023
Published
11 January 2023

Abstract

Anomaly detection plays an important role in traffic operations and control. Missingness in spatial-temporal datasets prohibits anomaly detection algorithms from learning characteristic rules and patterns due to the lack of large amounts of data. This paper proposes an anomaly detection scheme for the 2021 Algorithms for Threat Detection (ATD) challenge based on Gaussian process models that generate features used in a logistic regression model which leads to high prediction accuracy for sparse traffic flow data with a large proportion of missingness. The dataset is provided by the National Science Foundation (NSF) in conjunction with the National Geospatial-Intelligence Agency (NGA), and it consists of thousands of labeled traffic flow records for 400 sensors from 2011 to 2020. Each sensor is purposely downsampled by NSF and NGA in order to simulate missing completely at random, and the missing rates are 99%, 98%, 95%, and 90%. Hence, it is challenging to detect anomalies from the sparse traffic flow data. The proposed scheme makes use of traffic patterns at different times of day and on different days of week to recover the complete data. The proposed anomaly detection scheme is computationally efficient by allowing parallel computation on different sensors. The proposed method is one of the two top performing algorithms in the 2021 ATD challenge.

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Copyright
© 2023 New England Statistical Society
Open access article under the CC BY license.

Keywords
Anomaly detection Gaussian process Spatiotemporal High dimensional Missing completely at random

Funding
This research was supported in part by the National Science Foundation grant, DMS-1924792.

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