Who might encounter hard-braking while speeding? Analysis for regular speeders using low-frequency taxi trajectories on arterial roads and explainable AI

Zhou, Yue; Fu, Chuanyun; Jiang, Xinguo; Yu, Qiong; Liu, Haiyue

doi:10.1016/j.aap.2023.107382

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2024

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Cited by 2 publications

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“…To better understand the results of our models, we utilized the Tree SHapley Additive exPlanations (SHAP) method to calculate the importance of each feature [31]. This approach provides a more comprehensive interpretation of the results, allowing for greater insight and understanding of the underlying data.…”

Section: Classificationmentioning

confidence: 99%

Classifying Motorcyclist Behaviour with XGBoost Based on IMU Data

Navratil,

Giannopoulos

2024

Sensors

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Human behaviour detection is relevant in many fields. During navigational tasks it is an indicator for environmental conditions. Therefore, monitoring people while they move along the street network provides insights on the environment. This is especially true for motorcyclists, who have to observe aspects such as road surface conditions or traffic very careful. We thus performed an experiment to check whether IMU data is sufficient to classify motorcyclist behaviour as a data source for later spatial and temporal analysis. The classification was done using XGBoost and proved successful for four out of originally five different types of behaviour. A classification accuracy of approximately 80% was achieved. Only overtake manoeuvrers were not identified reliably.

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Section: Classificationmentioning

confidence: 99%