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Large Language Models for Travel Behavior Prediction
Travel behavior prediction is a fundamental task in transportation demand management.
Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Network
To further capture the global efficiency of the route, we propose a new iterative sequence generation algorithm that is used after model training to identify the first stop of a route that yields the lowest operational cost.
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
The relative ranking of the ML and DCM classifiers is highly stable, while the absolute values of the prediction accuracy and computational time have large variations.
Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach
Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns.
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks
However, the two methods are highly complementary because data-driven methods are more predictive but less interpretable and robust, while theory-driven methods are more interpretable and robust but less predictive.
Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions
Overall, this study demonstrates that prior behavioral knowledge could be used to guide the architecture design of DNN, to function as an effective domain-knowledge-based regularization method, and to improve both the interpretability and predictive power of DNN in choice analysis.
