在物理信息神经网络中融入非局部交通流模型
Incorporating Nonlocal Traffic Flow Model in Physics-informed Neural Networks
- University of Central Florida（中佛罗里达大学）
- University of Nebraska-Lincoln（内布拉斯加大学林肯分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对经典LWR模型难以准确表征真实交通流的问题，本研究提出融入非局部LWR模型的新型物理信息深度学习框架，经多数据集评估验证其性能优于基线方法，可提升交通状态估计效果。
AI中文摘要：
本研究借助物理信息深度学习框架下的非局部LWR模型优势，推动交通状态估计方法的发展。经典LWR模型虽具实用性，但无法准确表征真实交通流；非局部LWR模型通过将速度视为下游交通密度的加权均值来弥补这一局限。本文提出一种融入非局部LWR模型的新型PIDL（物理信息深度学习）框架，引入了固定长度和可变长度核，并推导了所需的数学方法。研究采用NGSIM和CitySim数据集，对所提PIDL框架开展综合评估，涵盖多种卷积核与前瞻窗口设置。结果表明，该方法相比采用局部LWR模型的基线PIDL方法性能更优。研究结果凸显了所提方法提升交通状态估计准确性与可靠性的潜力，可支撑更有效的交通管理策略。
英文摘要：
This research contributes to the advancement of traffic state estimation methods by leveraging the benefits of the nonlocal LWR model within a physics-informed deep learning framework. The classical LWR model, while useful, falls short of accurately representing real-world traffic flows. The nonlocal LWR model addresses this limitation by considering the speed as a weighted mean of the downstream traffic density. In this paper, we propose a novel PIDL framework that incorporates the nonlocal LWR model. We introduce both fixed-length and variable-length kernels and develop the required mathematics. The proposed PIDL framework undergoes a comprehensive evaluation, including various convolutional kernels and look-ahead windows, using data from the NGSIM and CitySim datasets. The results demonstrate improvements over the baseline PIDL approach using the local LWR model. The findings highlight the potential of the proposed approach to enhance the accuracy and reliability of traffic state estimation, enabling more effective traffic management strategies.
