Report
WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning
| العنوان: | WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning |
|---|---|
| المؤلفون: | Jungel, Kai, Paccagnan, Dario, Parmentier, Axel, Schiffer, Maximilian |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | When optimizing transportation systems, anticipating traffic flows is a central element. Yet, computing such traffic equilibria remains computationally expensive. Against this background, we introduce a novel combinatorial optimization augmented neural network architecture that allows for fast and accurate traffic flow predictions. We propose WardropNet, a neural network that combines classical layers with a subsequent equilibrium layer: the first ones inform the latter by predicting the parameterization of the equilibrium problem's latency functions. Using supervised learning we minimize the difference between the actual traffic flow and the predicted output. We show how to leverage a Bregman divergence fitting the geometry of the equilibria, which allows for end-to-end learning. WardropNet outperforms pure learning-based approaches in predicting traffic equilibria for realistic and stylized traffic scenarios. On realistic scenarios, WardropNet improves on average for time-invariant predictions by up to 72% and for time-variant predictions by up to 23% over pure learning-based approaches. Comment: 40 pages, 15 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2410.06656 |
| رقم الانضمام: | edsarx.2410.06656 |
| قاعدة البيانات: | arXiv |
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1 |
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edsarx arXiv edsarx.2410.06656 1128 3 Report report 1128.01770019531 |
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https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2410.06656&custid=s6537998&authtype=sso |
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