Crowdsourced Insights: Shaping Origin–Destination Matrix Estimation Utilizing Transportation Data on Demand

Abstract

In transportation system modeling, the origin–destination matrix estimation (ODME) is a critical facet that relies on traffic assignment. Extracting origin–destination (O–D) demand matrices from regional travel demand models for subnetworks is common; however, challenges persist in their quality, particularly for dynamic traffic assignment and simulation modeling. The ODME procedures have emerged to estimate O–D demands using a seed matrix and real-world measures, often segment volume counts. Recently, the availability of O–D demand data from private sector vendors has been witnessed, sourced from crowdsourced and automated vehicle identification (AVI) technologies. This paper explores the integration of crowdsourced data, segment-level measures, and demand forecasting model outputs in O–D demand estimation, which compares 12 ODME variations that employ different input variable combinations and weights. This paper aims to enhance the guidance and methodologies for analysts who utilize diverse data sources in O–D demand estimation.