A Novel Adversarial Framework for Urban Traffic Congestion Analysis: A Supply-Demand Perspective
DOI:
https://doi.org/10.55640/Keywords:
Traffic Congestion, Traffic Forecasting, Graph Neural NetworksAbstract
Introduction: Urban traffic congestion poses a significant challenge to modern transportation systems. While deep learning models, particularly Graph Neural Networks (GNNs), have shown promise in traffic forecasting, they often focus on predicting future states based on historical patterns. This approach fails to provide a comprehensive understanding of network vulnerabilities when faced with sudden, unexpected disruptions, such as a traffic accident.
Methods: We propose a novel, adversarially-inspired framework called ATraffic to analyze urban traffic congestion. Drawing an analogy from Word Sense Disambiguation (WSD), which resolves ambiguity by analyzing context, our framework utilizes a "traffic attacker" to simulate a targeted, localized disruption to the network's capacity. This attacker reduces the "supply" of a specific road segment, allowing us to observe how the ensuing congestion propagates and impacts the overall "supply-demand" balance. Our model integrates a spatio-temporal GNN architecture to capture the dynamic dependencies of the road network, while the adversarial module systematically identifies and "attacks" critical nodes.
Results: Our experiments demonstrate that the proposed framework can effectively simulate the ripple effects of a localized disruption. We show that a minor, simulated attack can lead to a significant increase in total network travel time and can identify specific, vulnerable network segments where the supply-demand balance is most critically affected. The model's predictions align with established principles of congestion propagation, highlighting its utility as an analytical tool for urban planners.
Discussion: This research presents a new paradigm for studying traffic congestion by treating it as a dynamic response to a deliberate shock on the network's supply side. Our findings confirm that understanding and mitigating congestion requires not only predictive capabilities but also an understanding of system resilience. The "traffic attacker" framework offers a valuable tool for stress-testing road networks, revealing hidden bottlenecks and guiding strategic infrastructure improvements.
Conclusion: The adversarial, supply-shock approach provides a robust method for analyzing urban traffic congestion. By simulating disruptions, we can gain deeper insights into the complex dynamics of traffic flow and develop more resilient and sustainable transportation systems.
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