3 papers
cs.LG2026
T-STAR: A Context-Aware Transformer Framework for Short-Term Probabilistic Demand Forecasting in Dock-Based Shared Micro-Mobility
Jingyi Cheng, Gonçalo Homem de Almeida Correia, Oded Cats +1
Reliable short-term demand forecasting is essential for managing shared micro-mobility services and ensuring responsive, user-centered operations. This study introduces T-STAR (Two…
cs.CY2025
A Short-Term Predict-Then-Cluster Framework for Meal Delivery Services
Jingyi Cheng, Shadi Sharif Azadeh
Micro-delivery services offer promising solutions for on-demand city logistics, but their success relies on efficient real-time delivery operations and fleet management. On-demand…
eess.SY2025
Real-Time Integrated Dispatching and Idle Fleet Steering with Deep Reinforcement Learning for A Meal Delivery Platform
Jingyi Cheng, Shadi Sharif Azadeh
To achieve high service quality and profitability, meal delivery platforms like Uber Eats and Grubhub must strategically operate their fleets to ensure timely deliveries for curren…