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Found 7 papers, 2 papers with code
A Multi-day Needs-based Modeling Approach for Activity and Travel Demand Analysis
The utility includes both the benefit in the inventory gained and the cost in time, monetary expense as well as maintenance of safety stock.
Adaptive Transit Design: Optimizing Fixed and Demand Responsive Multi-Modal Transportation via Continuous Approximation
Our model allows deciding whether to deploy a FR or a DR feeder, in each sub-region of an urban conurbation and each time of day, and to redesign the line frequencies and the stop spacing of the main trunk service.
Improving the Accuracy and Efficiency of Online Calibration for Simulation-based Dynamic Traffic Assignment
Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control.
Discrete Choice Analysis with Machine Learning Capabilities
This paper discusses capabilities that are essential to models applied in policy analysis settings and the limitations of direct applications of off-the-shelf machine learning methodologies to such settings.
Learning Structure in Nested Logit Models
We formulate the problem of learning an optimal nesting structure from the data as a mixed integer nonlinear programming (MINLP) optimization problem and solve it using a variant of the linear outer approximation algorithm.
A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability
Our formulation consists of two modules: a neural network (TasteNet) that learns taste parameters (e. g., time coefficient) as flexible functions of individual characteristics; and a multinomial logit (MNL) model with utility functions defined with expert knowledge.
Sparse Covariance Estimation in Logit Mixture Models
We propose a new estimator, called MISC, that uses a mixed-integer optimization (MIO) program to find an optimal block diagonal structure specification for the covariance matrix, corresponding to subsets of correlated coefficients, for any desired sparsity level using Markov Chain Monte Carlo (MCMC) posterior draws from the unrestricted full covariance matrix.
