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

TL;DR

This paper proposes a new demand estimation method that incorporates segment-level track counts as regularization, improving accuracy in recovering demand patterns without relying solely on prior OD matrices.

Contribution

It introduces a modified simulation-based demand calibration approach that integrates sample segment counts to address underdetermination and enhance demand accuracy.

Findings

Improved demand pattern recovery at OD and segment levels.

Enhanced solution quality with partial segment count data.

Effective on Seattle highway network with various congestion levels.

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.