Traffic Model
Traffic modeling aims to create accurate representations of vehicular movement to optimize transportation systems and improve safety. Current research emphasizes data-driven approaches, employing deep learning architectures like transformers and neural networks (including physics-informed and generative adversarial networks) to capture complex spatiotemporal dynamics and driver behavior, often incorporating macroscopic and microscopic models. These advancements are crucial for applications such as autonomous vehicle development, traffic control optimization, and the development of more realistic and controllable driving simulators, ultimately leading to improved efficiency and safety in transportation networks.
23papers
Papers
May 16, 2025
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
Yuan-Zheng Lei, Yaobang Gong, Dianwei Chen, Yao Cheng, Xianfeng Terry YangUniversity of MarylandLearning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment
Oskar Bohn Lassen, Serio Agriesti, Mohamed Eldafrawi, Daniele Gammelli, Guido Cantelmo, Guido Gentile, Francisco Camara PereiraTechnical University of Denmark●Sapienza University of Rome●Stanford University
September 25, 2024
May 2, 2024
An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions
Leon Eisemann, Mirjam Fehling-Kaschek, Henrik Gommel, David Hermann, Marvin Klemp, Martin Lauer, Benjamin Lickert, Florian Luettner+8MTDT: A Multi-Task Deep Learning Digital Twin
Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati, Anand Rangarajan, Sanjay Ranka
January 22, 2024
December 7, 2023
December 5, 2023
November 30, 2023
September 1, 2023
August 22, 2023