arXiv:2310.05753v2 Announce Type: replace Abstract: The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS). It involves adjusting an initial OD matrix by regressing the current observations like traffic counts of road sections (e.g., using least squares). However, the OD estimation problem lacks sufficient constraints and is mathematically underdetermined. To alleviate this problem, some researchers incorporate a prior OD matrix as a target in the regression to provide more structural constraints. However, this approach is highly dependent
The continuous development in deep learning methods allows for new approaches to long-standing, mathematically challenging problems like OD matrix estimation in transportation.
Improved OD matrix estimation using deep learning can significantly enhance the efficiency and planning of intelligent transport systems, benefiting urban development and logistics.
Traditional, underdetermined OD estimation methods can be augmented or replaced by more robust deep learning solutions that incorporate more data and structural constraints.
- · Urban planners
- · Logistics companies
- · Smart city developers
- · Intelligent Transport Systems (ITS) providers
- · Traditional traffic modeling software vendors
- · Organizations relying solely on outdated OD estimation techniques
More accurate traffic flow predictions and infrastructure planning.
Reduced traffic congestion and improved public transport efficiency in urban areas.
Potential for new urban planning paradigms that dynamically adapt to real-time traffic patterns.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI