  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.
