I-24 MOTION: An instrument for freeway traffic science

Gloudemans, Derek; Wang, Yanbing; Ji, Junyi; Zachár, Gergely; Barbour, Will; Work, Daniel B.

doi:10.48550/arxiv.2301.11198

Cited by 1 publication

(1 citation statement)

References 71 publications

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“…Nowadays, the development of Internet of Things (IoT) technologies has greatly advanced intelligent monitoring and management of urban transportation systems. Sensor networks including highway surveillance cameras and radar detectors, combined with Web of Things (WoT) technologies, have enabled transportation authorities to intelligently monitor traffic systems at scales and resolutions previously out of reach [17,43,44]. One of the emerging tasks in intelligent traffic management is anomaly detection, since abnormal drivers could have adversarial impact on the smoothness of traffic stream, or even pose safety concerns.…”

Section: Introduction 1motivation and Challengesmentioning

confidence: 99%

Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention Networks

Zhang

Wang

et al. 2023

Proceedings of the ACM Web Conference 2023

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With the rapid development of Internet of Things technologies, the next generation traffic monitoring infrastructures are connected via the web, to aid traffic data collection and intelligent traffic management. One of the most important tasks in traffic is anomaly detection, since abnormal drivers can reduce traffic efficiency and cause safety issues. This work focuses on detecting abnormal driving behaviors from trajectories produced by highway video surveillance systems. Most of the current abnormal driving behavior detection methods focus on a limited category of abnormal behaviors that deal with a single vehicle without considering vehicular interactions. In this work, we consider the problem of detecting a variety of socially abnormal driving behaviors, i.e., behaviors that do not conform to the behavior of other nearby drivers. This task is complicated by the variety of vehicular interactions and the spatial-temporal varying nature of highway traffic. To solve this problem, we propose an autoencoder with a Recurrent Graph Attention Network that can capture the highway driving behaviors contextualized on the surrounding cars, and detect anomalies that deviate from learned patterns. Our model is scalable to large freeways with thousands of cars. Experiments on data generated from traffic simulation software show that our model is the only one that can spot the exact vehicle conducting socially abnormal behaviors, among the state-of-the-art anomaly detection models. We further show the performance on real world HighD traffic dataset, where our model detects vehicles that violate the local driving norms. CCS CONCEPTS• Information systems → Spatial-temporal systems; Data mining.

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Section: Introduction 1motivation and Challengesmentioning

confidence: 99%