A flexible and scalable single-level framework for OD matrix inference using multiple sources of transport information

Understand

This study proposes a flexible and scalable single-level framework for origin-destination matrix (ODM) inference using data from IoT (Internet of Things) and other sources.

• The framework allows the analyst to integrate information from multiple data sources, while controlling for differences in data quality across sources.
• We assess the effectiveness of the framework through a real-world experiment in Greater Adelaide (GA), Australia.
• We infer car OD flows within the region using four separate data sources: site-level traffic counts from loop detectors, vehicle trajectories recorded by roadside Bluetooth sensors, partial OD flows based on data from in-vehicle navigation systems, and journey-to-work data collected by the Australian Census.