Virtualized Traffic at Metropolitan Scales

Abstract

Few phenomena are more ubiquitous than traffic in urban scenes, and few are more significant economically, socially, or environmentally. Many virtual-reality applications and systems, including virtual globes and immersive multi-player worlds that are often set in a large-scale modern or futuristic setting, feature traffic systems. Virtual-reality models can also aid in addressing the challenges of real-world traffic -the ever-present gridlock and congestion in cities worldwide: traffic engineers and planners can diagnose system instabilities and explore control strategies in virtual worlds reconstructed from available sensor data. To create these VR systems with traffic mimicking real-world conditions, road network models need to be created and represented. Traffic needs to be realistically and efficiently simulated. To analyze real-world scenarios, the traffic conditions need to be estimated and reconstructed. To create virtual scenarios, such as simulated cities, traffic needs to be intelligently and efficiently routed. These applications all require research advances in road network capture and modeling, intelligent traffic routing and simulation, and traffic state estimation and reconstruction. New systems need to be designed that combine these components with visual and analytical infrastructure. In this paper, we present some state-of-the-art approaches for these areas as well as our vision for unified virtual-reality traffic systems that combine and integrate them to achieve virtualized traffic.

Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Network

“…Third, we plan to conduct more experiments in order to understand the shape of the error curve of multi-step prediction. Fourth, we would like to use the predictions of our approach in testing the navigation of autonomous vehicles [55,56] by constructing and utilizing virtual traffic flows [57,58]. Lastly, we are interested in applying our technique to study traffic states under critical conditions such as the pandemic [59,60].…”

Analyzing Effects of The COVID-19 Pandemic on Road Traffic Safety: The Cases of New York City, Los Angeles, and Boston

“…First, we want to explore the characteristics of the hotspot regions in more details. Second, we want to explore whether the use of autonomous vehicles can help ease the delivery of essential goods in such critical conditions [22,23] under realistically simulated, and accurately and robustly estimated traffic conditions [24,25,26,27,28,29,30].…”

Estimating urban traffic states using iterative refinement and Wardrop equilibria

“…In order to study urban traffic patterns, two large‐scale field tests were conducted in Beijing and San Francisco. The estimated results can further enable traffic simulations and animations in various formats [47, 48].…”