Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment
arXiv:2511.06229v3 Announce Type: replace Abstract: This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations. The fundamental challenge of the DODE problem in microscopic simulations stems from the complex temporal dynamics and inherent uncertainty of individual vehicle dynamics. This makes it highly challenging to precisely determine which vehicle traverses which link at any given moment, resulting in intricate and often ambiguous relationships between origin-destination
The increasing complexity of urban traffic systems and the maturation of deep reinforcement learning techniques are converging to address long-standing challenges in simulation accuracy.
Accurate microscopic traffic simulations are critical for urban planning, autonomous vehicle development, and efficient logistics, impacting resource allocation and infrastructure investment.
The application of Deep Reinforcement Learning to dynamic origin-destination matrix estimation improves the fidelity and reliability of traffic models, making them more representative of real-world conditions.
- · Urban planners
- · Autonomous vehicle developers
- · Logistics companies
- · Smart city initiatives
- · Traditional traffic modeling firms relying on static methods
- · Inefficient urban infrastructure projects
More accurate traffic flow predictions lead to better urban transportation planning and reduced congestion.
Improved traffic management could optimize energy consumption in cities and reduce transit-related emissions.
Enhanced simulation capabilities could accelerate the development and deployment of fully autonomous public transportation systems.
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Read at arXiv cs.LG