Flow-Through Tensors: A Unified Computational Graph Architecture for Multi-Layer Transportation Network Optimization
arXiv:2507.02961v2 Announce Type: replace-cross Abstract: Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, reinforcement learning, classical flow optimization, and demand modeling that have traditionally been developed in isolation. This paper introduces Flow Through Tensors (FTT), a unified computational graph architecture that connects origin destination flows, path probabilities, and link travel times as interconnected tensors. Our framework makes three key contributions: first, it establishes a consist
The increasing complexity of modern transportation systems, integrating diverse methodologies like AI and classical optimization, necessitates unified computational frameworks.
A strategic reader should care as this unified architecture for transportation network optimization could significantly improve efficiency, resource allocation, and predictive capabilities in logistical and urban planning.
Traditional siloed approaches to transportation modeling are being replaced by integrated computational graph architectures, allowing for more holistic and dynamic management of complex networks.
- · Logistics companies
- · Urban planners
- · Public transportation authorities
- · Smart city developers
- · Legacy transportation modeling software
- · Fragmented data analytics providers
More efficient and responsive transportation networks globally, reducing congestion and optimizing resource use.
Potential for new logistical services and business models enabled by real-time, comprehensive network optimization.
Enhanced urban liveability and economic productivity due to seamlessly integrated and adaptive infrastructural systems.
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Read at arXiv cs.AI