一种灵活、自适应交通网络仿真的在线参数估计

Elvira Thonhofer, Elisabeth Luchini, Andreas Kuhn, S. Jakubek
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引用次数: 4

摘要

本文研究了适合于复杂道路形态实时仿真的宏观交通建模和在线参数标定。介绍了一种适用于任意形状基本图和初始条件分段可微的非线性双曲输运偏微分方程的数值求解方法。在道路入口和出口(交通灯信号)处实现合适的边界条件。此外,我们提出了一种方法,通过聚合交通传感器数据来识别底层基本图的参数,并利用Fisher信息矩阵来优化交通传感器的放置。通过与基于车辆跟随模型的微观交通仿真对比,验证了仿真结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online parameter estimation for a flexible, adaptive traffic network simulation
This paper deals with macroscopic traffic modeling and online parameter calibration suitable for real-time simulation of complex road configurations. A numerical solver for the nonlinear hyperbolic transport partial differential equation is introduced that works with fundamental diagrams of arbitrary shape and piecewise differentiable initial conditions. Suitable boundary conditions at road inlets and outlets (traffic light signals) are realized. Furthermore, we present a method to identify parameters of the underlying fundamental diagram via aggregated traffic sensor data and utilize the Fisher Information Matrix to optimize traffic sensor placement. The results are validated through comparison with microscopic traffic simulation based on a car-following model.
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