GitHub

plot_h01_mode_logit.py

# Load the Optima mode-choice data. database = read_data() # Build the utility functions for the transportation alternatives. utilities = generate_utility_functions() # Construct the log-likelihood of ...
GitHub

plot_b01a_logit.py

1. Import and prepare the data. 2. Define the model parameters. 3. Specify the utility functions and availability conditions. 4. Formulate the log-likelihood function. 5. Estimate the model using ...
IEEE

Focus on Hard Categories and Hard Examples: Remote Sensing Image Scene Classification via Expert Model and Hard Example Mining

Abstract: Deep learning has seen dramatic improvements in remote-sensing image scene classification. However, hard categories and hard examples widely exist in the data sets, due to the intraclass ...
IEEE

A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data

Abstract: Understanding the modal split in freight transportation is a key factor for the successful implementation of innovations. Mode choice models should then be as representative of reality as ...