We present a Godunov type numerical scheme for a class of scalar conservation laws with non-local flux arising for example in traffic flow models. The proposed scheme delivers more accurate solutions than the widely used Lax-Friedrichs type scheme. In contrast to other approaches, we consider a non-local mean velocity instead of a mean density and provide $L^\infty$ and bounded variation estimates for the sequence of approximate solutions. Together with a discrete entropy inequality, we also show the well-posedness of the considered class of scalar conservation laws. The better accuracy of the Godunov type scheme in comparison to Lax-Friedrichs is proved by a variety of numerical examples.
Sensitivity analysis of the LWR model for traffic forecast on large networks using Wasserstein distance
Istituto per le Applicazioni del Calcolo “M. Picone”, Consiglio Nazionale delle Ricerche（意大利国家研究委员会计算应用研究所）；Dipartimento di Scienze di Base e Applicate per l’Ingegneria, Sapienza – Università di Roma（罗马大学基础与应用工程科学系）
A hybrid numerical method for a microscopic and macroscopic traffic flow model
Department of mathematics and statistics, Ningbo University（宁波大学数学与统计学院）；School of Statistics and Data Science, Ningbo University of Technology（宁波工业大学统计与数据科学学院）
GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation Laws
Modelling Engineering Risk and Complexity, Scuola Superiore Meridionale（建模工程风险与复杂性，南方高级学校）；Dept. of Electrical Engineering and Information Technology, University of Naples Federico II（电气工程与信息技术系，那不勒斯费德里科二世大学）；Institute of Science and Technology for Energy and Sustainable Mobility, Consiglio Nazionale delle Ricerche（能源与可持续交通科学与技术研究所，意大利国家研究委员会）
