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Found 4 papers, 2 papers with code
A Regression Mixture Model to understand the effect of the Covid-19 pandemic on Public Transport Ridership
To understand its effects on rail public transport ridership, we propose a dedicated Regression Mixture Model able to perform both the clustering of public transport stations and the segmentation of time periods, while ignoring variations due to additional variables such as the official lockdowns or non-working days.
When Neural Networks Using Different Sensors Create Similar Features
Multimodal problems are omnipresent in the real world: autonomous driving, robotic grasping, scene understanding, etc... We draw from the well-developed analysis of similarity to provide an example of a problem where neural networks are trained from different sensors, and where the features extracted from these sensors still carry similar information.
The Devil Is in the Details: An Efficient Convolutional Neural Network for Transport Mode Detection
Transport mode detection is a classification problem aiming to design an algorithm that can infer the transport mode of a user given multimodal signals (GPS and/or inertial sensors).
Data Fusion for Deep Learning on Transport Mode Detection: A Case Study
In Transport Mode Detection, a great diversity of methodologies exist according to the choice made on sensors, preprocessing, model used, etc.
