Research map / Learning theory and algorithms / Prediction and decision losses
Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use the linear-regression predictor instead of the other tested predictors | Dynamic prediction equilibrium · Synthetic network | average travel time | Helped |
| use the zero predictor instead of the constant, linear, regularized-linear, or ML predictor | Dynamic prediction equilibrium · Synthetic network | average travel time | Helped |
| use the zero predictor instead of the other tested predictors | Dynamic prediction equilibrium · Sioux Falls and Tokyo road networks | average travel time | Hurt |
| use the ML-based predictor instead of the other non-zero predictors | Dynamic prediction equilibrium · Sioux Falls and Tokyo road networks | average travel time | No clear effect |
Closest papers by meaning
- WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning (ICLR 2025 · similarity 0.86)
- Information Design for Congestion Games with Unknown Demand (AAAI 2024 · similarity 0.83)
- URB - Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles (NeurIPS 2025 · similarity 0.83)
- HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention (CVPR 2024 · similarity 0.83)
- Dynamic Latent Routing (NeurIPS 2026 · similarity 0.83)
- PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization (NeurIPS 2025 · similarity 0.82)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.