Enhancing Transportation Mode Prediction from GPS Trajectories using Machine Learning and Feature Engineering DOI
Hichame Kabiri, Youssef Ghanou, Hamid Khalifi

et al.

Published: Nov. 22, 2023

This manuscript proposes a comprehensive framework for the automated determination of travel modes based solely on GPS trajectories. To improve prediction accuracy, additional preprocessing features are introduced, including speed, acceleration, jerk, and bearing rate. Our approach employs various machine learning techniques, such as Random Forest, Multilayer Perceptron (MLP), AdaBoost Classifier, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), to achieve notable classification results. Extensive evaluations demonstrate that our surpasses existing state-of-the-art algorithms transport mode prediction. investigation presents promising accurate reliable modes, with potential applications in real-world scenarios..

Language: Английский

Transportation mode detection through spatial attention-based transductive long short-term memory and off-policy feature selection DOI Creative Commons

Mahsa Merikhipour,

Shayan Khanmohammadidoustani,

Muhammad Daud Abbasi

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 267, P. 126196 - 126196

Published: Dec. 25, 2024

Language: Английский

Citations

5
How Reliable Is Google And Facebook Mobility Data? DOI Open Access
Péter Bucsky

Transport and Communications, Journal Year: 2024, Volume and Issue: 12(2), P. 1 - 10

Published: Dec. 30, 2024

Language: Английский

Citations

0
Enhancing Transportation Mode Prediction from GPS Trajectories using Machine Learning and Feature Engineering DOI
Hichame Kabiri, Youssef Ghanou, Hamid Khalifi

et al.

Published: Nov. 22, 2023

This manuscript proposes a comprehensive framework for the automated determination of travel modes based solely on GPS trajectories. To improve prediction accuracy, additional preprocessing features are introduced, including speed, acceleration, jerk, and bearing rate. Our approach employs various machine learning techniques, such as Random Forest, Multilayer Perceptron (MLP), AdaBoost Classifier, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), to achieve notable classification results. Extensive evaluations demonstrate that our surpasses existing state-of-the-art algorithms transport mode prediction. investigation presents promising accurate reliable modes, with potential applications in real-world scenarios..

Language: Английский

Citations

0