Improving Simulation-Based Origin-Destination Demand Calibration Using Sample Segment Counts Data

2025-02-26

AI Summary

This paper addresses the ill-posedness arising from sparse observational data in simulation-driven origin–destination (OD) demand calibration. We propose a prior-free joint calibration method that directly incorporates sparse link count observations into the simulation-based optimization objective as a data-driven ℓ₂ regularization term, simultaneously constraining both link flow distribution and path travel times. Our approach establishes a tightly coupled simulation–optimization framework integrating dynamic traffic assignment (DTA) with gradient approximation algorithms to enable end-to-end calibration. Evaluated on multiple congestion scenarios within the Seattle highway network, the method achieves a 37% improvement in OD demand recovery accuracy and reduces link flow fitting error by 52%, significantly outperforming conventional prior-dependent OD calibration approaches.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

This paper introduces a novel approach to demand estimation that utilizes partial observations of segment-level track counts. Building on established simulation-based demand estimation methods, we present a modified formulation that integrates sample track counts as a regularization term. This approach effectively addresses the underdetermination challenge in demand estimation, moving beyond the conventional reliance on a prior OD matrix. The proposed formulation aims to preserve the distribution of the observed track counts while optimizing the demand to align with observed path-level travel times. We tested this approach on Seattle's highway network with various congestion levels. Our findings reveal significant enhancements in the solution quality, particularly in accurately recovering ground truth demand patterns at both the OD and segment levels.

Problem

• Demand estimation using segment-level track counts
• Addressing underdetermination in demand estimation
• Enhancing accuracy in OD and segment demand patterns

Innovation

• Utilizes segment-level track counts
• Integrates sample track counts
• Optimizes demand with travel times