TransWorldNG: Traffic Simulation via Foundation Model
This addresses the problem of accurate and scalable traffic simulation for transportation decision-makers and policymakers, representing a novel approach rather than an incremental improvement.
The paper tackles the challenge of realistic traffic simulation in high-dimensional, heterogeneous environments by introducing TransWorldNG, a simulator that uses data-driven algorithms and graph computing to learn traffic dynamics from real data, resulting in more realistic traffic patterns and linear scalability in computation time.
Traffic simulation is a crucial tool for transportation decision-making and policy development. However, achieving realistic simulations in the face of the high dimensionality and heterogeneity of traffic environments is a longstanding challenge. In this paper, we present TransWordNG, a traffic simulator that uses Data-driven algorithms and Graph Computing techniques to learn traffic dynamics from real data. The functionality and structure of TransWorldNG are introduced, which utilize a foundation model for transportation management and control. The results demonstrate that TransWorldNG can generate more realistic traffic patterns compared to traditional simulators. Additionally, TransWorldNG exhibits better scalability, as it shows linear growth in computation time as the scenario scale increases. To the best of our knowledge, this is the first traffic simulator that can automatically learn traffic patterns from real-world data and efficiently generate accurate and realistic traffic environments.