Time consideration in machine learning models for train comfort prediction using LSTM networks

Martínez-Llop, Pablo Garrido; Bobi, Juan de Dios Sanz; Ortega, Manuel Olmedo

doi:10.1016/j.engappai.2023.106303

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Cited by 8 publications

References 24 publications

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Hannan Quinn Quantum Grasshopper Optimization and Attention Deep Intelligent Train Status Prediction

Singh,

Dhanaraj,

Kumar

et al. 2024

Multimed Tools Appl

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Hannan Quinn Quantum Grasshopper Optimization and Attention Deep Intelligent Train Status Prediction

Singh,

Dhanaraj,

Kumar

et al. 2024

Multimed Tools Appl

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A monitoring framework for urban road ride quality using smartphone sensing technology

Guo,

Zhang,

Cao

et al. 2024

Measurement

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Win Your Race Goal: A Generalized Approach to Prediction of Running Performance

Dash

2024

Sports Med Int Open

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We introduce a novel approach for predicting running performance, designed to apply across a wide range of race distances (from marathons to ultras), elevation gains, and runner types (front-pack to back of the pack). To achieve this, the entire running logs of 15 runners, encompassing a total of 15,686 runs, were analyzed using two approaches: (1) regression and (2) time series regression (TSR). First, the prediction accuracy of a long short-term memory (LSTM) network was compared using both approaches. The regression approach demonstrated superior performance, achieving an accuracy of 89.13% in contrast, the TSR approach reached an accuracy of 85.21%. Both methods were evaluated using a test dataset that included the last 15 runs from each running log. Secondly, the performance of the LSTM model was compared against two benchmark models: Riegel formula and UltraSignup formula for a total of 60 races. The Riegel formula achieves an accuracy of 80%, UltraSignup 87.5%, and the LSTM model exhibits 90.4% accuracy. This work holds potential for integration into popular running apps and wearables, offering runners data-driven insights during their race preparations.

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Time consideration in machine learning models for train comfort prediction using LSTM networks

Cited by 8 publications

References 24 publications

Hannan Quinn Quantum Grasshopper Optimization and Attention Deep Intelligent Train Status Prediction

Hannan Quinn Quantum Grasshopper Optimization and Attention Deep Intelligent Train Status Prediction

A monitoring framework for urban road ride quality using smartphone sensing technology

Win Your Race Goal: A Generalized Approach to Prediction of Running Performance

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