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Found 8 papers, 3 papers with code
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests.
Attitudes and Latent Class Choice Models using Machine learning
Latent Class Choice Models (LCCM) are extensions of discrete choice models (DCMs) that capture unobserved heterogeneity in the choice process by segmenting the population based on the assumption of preference similarities.
Open vs Closed-ended questions in attitudinal surveys -- comparing, combining, and interpreting using natural language processing
So, we propose a modeling framework that allows respondents to use their preferred questionnaire type to answer the survey and enable analysts to use the modeling frameworks of their choice to predict behavior.
Transfer learning for cross-modal demand prediction of bike-share and public transit
The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist.
Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management
Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility.
Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach
The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification with the aim of comparing the two approaches on various measures including prediction accuracy and representation of heterogeneity in the choice process.
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.
Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network
Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas.
