arrow
返回

Open-ti: open traffic intelligence with augmented language model

delete2024-05-09
delete1
PRE
AI
L
Longchao Da
K
Kuanru Liou
T
Tiejin Chen
X
Xuesong Zhou
X
Xiangyong Luo
Y
Yezhou Yang
韦
韦华 (Hua Wei) *
DOI:10.1007/s13042-024-02190-8delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
Transportation has greatly benefited the cities' development in the modern civilization process. Intelligent transportation, leveraging advanced computer algorithms, could further increase people's daily commuting efficiency. However, intelligent transportation, as a cross-discipline, often requires practitioners to comprehend complicated algorithms and obscure neural networks, bringing a challenge for the advanced techniques to be trusted and deployed in practical industries. Recognizing the expressiveness of the pre-trained large language models, especially the potential of being augmented with abilities to understand and execute intricate commands, we introduce Open-TI. Serving as a bridge to mitigate the industry-academic gap, Open-TI is an innovative model targeting the goal of Turing Indistinguishable Traffic Intelligence, it is augmented with the capability to harness external traffic analysis packages based on existing conversations. Marking its distinction, Open-TI is the first method capable of conducting exhaustive traffic analysis from scratch-spanning from map data acquisition to the eventual execution in complex simulations. Besides, Open-TI is able to conduct task-specific embodiment like training and adapting the traffic signal control policies (TSC), explore demand optimizations, etc. Furthermore, we explored the viability of LLMs directly serving as control agents, by understanding the expected intentions from Open-TI, we designed an agent-to-agent communication mode to support Open-TI conveying messages to ChatZero (control agent), and then the control agent would choose from the action space to proceed the execution. We eventually provide the formal implementation structure, and the open-ended design invites further community-driven enhancements. A demo video is provided at: https://youtu.be/pZ4-5PXz9Xs.
Keyword:
Large language models
Traffic simulation
Traffic signal control

期刊

International Journal of Machine Learning and Cybernetics 封面图
International Journal of Machine Learning and Cybernetics
IF:
2.7
论文数:
3.2K
被引数:
5.6K

机构

A
Arizona State University
学者数:
2.7W
论文数: 2.5W
被引数: 4.2W
A
arizona state university-tempe
学者数:
1.5W
论文数: 1.2W
被引数: 13
引用论文

引用论文

err分享
err收藏
err分享
err收藏
A Hybrid Submicroscopic-Microscopic Traffic Flow Simulation Framework
err2021-06-01
err31
errOAAI
errMullakkal-Babu, Freddy Antony; Wang, Meng; van Arem, Bart; Shyrokau, Barys; Happee, Riender
err分享
err收藏
err
IF0
err
err0
PREAI
err
err分享
err收藏
The neural correlates of belief-bias inhibition: The impact of logic training
err2014-12-01
err0
PREAI
errJunlong Luo; Xiaochen Tang; Entao Zhang; Edward J.N. Stupple
err分享
err收藏
学者 查看更多内容