arXiv (All)
open access
Estimate then Predict: Convex Formulation for Travel Demand Forecasting
Travel demand forecasting is essential for evaluating large-scale infrastructure projects, yet the traditional sequential four-step process can produce inconsistencies across trip distribution, mode choice, and traffic assignment. Although combined models address these inconsistencies, their practic
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ESTIMATING THE COST OF INEFFICIENT DECISION METRICS: SIMPLIFYING THE SHORE INFRASTRUCTURE PRIORITIZATION PROCESS#668723
- High-dimensional point forecast combinations for emergency department demand#147049
- Modeling Decisions for Artificial Intelligence : 23rd International Conference, MDAI 2026, Vic, Spain, September 7–9, 2026, Proceedings / edited by Vicenç Torra, Yasuo Narukawa, Ramon Reig-Bolaño#995763
- Machine learning-aided trajectory optimization and tour design#274572
- Machine learning prediction of increased healthcare utilization in colectomy for inflammatory bowel disease.#1097890
Record · ID 1011633
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive