TransWorldNG: Traffic Simulation via Foundation Model
- URL: http://arxiv.org/abs/2305.15743v1
- Date: Thu, 25 May 2023 05:49:30 GMT
- Title: TransWorldNG: Traffic Simulation via Foundation Model
- Authors: Ding Wang, Xuhong Wang, Liang Chen, Shengyue Yao, Ming Jing, Honghai
Li, Li Li, Shiqiang Bao, Fei-Yue Wang, Yilun Lin
- Abstract summary: We present TransWordNG, a traffic simulator that uses Data-driven algorithms and Graph Computing techniques to learn traffic dynamics from real data.
The results demonstrate that TransWorldNG can generate more realistic traffic patterns compared to traditional simulators.
- Score: 23.16553424318004
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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