# Anomalous Behavior Detection in Trajectory Data of Older Drivers

arXiv: https://arxiv.org/abs/2311.17822 · HTML: https://mlsanity.com/paper/2311.17822/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Methods and Tools for Monitoring Driver's Behavior](https://mlsanity.com/paper/2301.12269/) (2023)

## Built on by (most cited first, 5 of 5)

- [Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-based Model](https://mlsanity.com/paper/2408.11253/) (2024)
- [AIoT-based Continuous, Contextualized, and Explainable Driving Assessment for Older Adults](https://mlsanity.com/paper/2603.00691/) (2026)
- [CNN-based Labelled Crack Detection for Image Annotation](https://mlsanity.com/paper/2408.11250/) (2024)
- [Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach](https://mlsanity.com/paper/2408.12838/) (2024)
- [COVID-19 Probability Prediction Using Machine Learning: An Infectious Approach](https://mlsanity.com/paper/2408.12841/) (2024)

## Abstract (arXiv)

Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hardbrakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.