arXiv:2504.02993v2 Announce Type: replace-cross Abstract: In this paper, we aim to mitigate congestion in traffic management systems by guiding travelers along system-optimal (SO) routes. However, we recognize that most theoretical approaches assume perfect driver compliance, which often does not reflect reality, as drivers tend to deviate from recommendations to fulfill their personal objectives. Therefore, we propose a route recommendation framework that explicitly learns partial driver compliance and optimizes traffic flow under realistic adherence. We first compute an SO edge flow through
This paper leverages advanced AI techniques to address a persistent challenge in urban planning, reflecting the increasing sophistication of AI models for complex real-world systems.
Traffic congestion is a significant economic and quality-of-life drain globally, and this research offers a pathway to more effective, AI-driven solutions that account for human behavior.
This research shifts traffic management from theoretical optimal routes to practical, AI-learned partial driver compliance, leading to potentially more effective and implementable recommendations.
- · Smart city technology providers
- · Urban planners
- · Commuters
- · Logistics companies
- · Traditional traffic modeling systems
Traffic flow in urban centers could improve through more intelligent and adaptive recommendation systems.
Reduced congestion may lead to economic benefits through increased productivity and decreased fuel consumption, potentially influencing urban development patterns.
The success of AI in managing dynamic human behavior in traffic could inspire similar 'partial compliance' models in other public infrastructure management domains.
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Read at arXiv cs.LG