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Open-ti: open traffic intelligence with augmented language model

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Abstract

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.

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Data availability

All datasets and road networks are publicly available at: https://github.com/DaRL-LibSignal/LibSignalhttps://traffic-signal-control.github.io/dataset.html.

Notes

  1. We have released the code at: https://github.com/DaRL-LibSignal/OpenTI.git.

  2. https://blog.google/products/search/search-language-understanding-bert/.

  3. https://traffic-signal-control.github.io/dataset.html.

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Authors and Affiliations

  1. School of Computing and Augmented Intelligence, Arizona State University, 350 E Lemon St, Tempe, AZ, 85287, USA

    Longchao Da, Kuanru Liou, Tiejin Chen, Yezhou Yang & Hua Wei

  2. School of Sustainable Engineering and the Built Environment, Arizona State University, 350 E Lemon St, Tempe, AZ, 85287, USA

    Xuesong Zhou & Xiangyong Luo

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  1. Longchao Da
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Appendix A

Appendix A

1.1 Detailed explanation of acronyms

See Tables 8, 9.

Table 8 The detailed of Config and corresponding roade netowrk
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Table 9 A glance at the technique terms and abbreviation
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1.2 Thought chain process examples

In this section, we provide more Chain-of-thought (CoT) process examples, as a reflection on given a task, how the Open-TI thinks and proposes the solutions, and how it searches in the augmentation tools to further provide analysis.

We have shown the requests such as: downloading OSM files of specific locations, interpreting the log files, showing areas on a map, filtering assigned lane types from a given map, generating demand files based on a map file, executing multiple simulations like DLSim, SUMO, etc., running LibSignal for traffic signal control (Figs. 14, 15, 16, 17, 18, 19, 20, 21, 22, 23).

Fig. 14
figure 14

Ask Open-TI to get the geographic information of Arizona State University, Tempe Campus

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Fig. 15
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Ask Open-TI to download OSM data of Arizona State University, Tempe Campus

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Fig. 16
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Ask Open-TI to download OSM data of Arizona State University, Tempe Campus without giving Open-TI geographic information

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Fig. 17
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Ask Open-TI to analyze the interested log file in a specific path

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Fig. 18
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Ask Open-TI to generate demand file through downloaded osm data of Arizona State University, Tempe Campus

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Fig. 19
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Ask Open-TI to show the map of Arizona State University, Tempe Campus

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Fig. 20
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Ask Open-TI to show the map of Taipei 101 without giving Open-TI geographic information

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Fig. 21
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Ask Open-TI to filter the bikable area of Arizona State University, Tempe Campus

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Fig. 22
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Ask Open-TI to run the demand file of Arizona State University, Tempe Campus on DLSim

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Fig. 23
figure 23

Ask Open-TI to run Libsignal on CityFlow environment, DQN policy, and episode 10

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1.3 Other interactions with Open-TI examples

This section provides more examples of user interactions, including result interpretation, log file analysis, O-D matrix optimization, etc (Figs. 24, 25, 26, 27).

Fig. 24
figure 24

Ask Open-TI to show the map of interested place, download.osm data of interested place, use the OSM file of target place to filter railway routes, and conduct traffic signal control

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Fig. 25
figure 25

An example of asking Open-TI to generate demand file from OSM file, run simulator on Libsignal in different algorithm and episode, execute simulation on DLSim, understand and response in multi-languages, and analyze logs in a specific path

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Fig. 26
figure 26

An example of asking Open-TI to analyze file with a specific path. In this case, run the simulation on Libsignal using simulator CityFlow, algorithm fixedtime, and episode 5. Additionally, run the simulation on Libsignal using simulator CityFlow, algorithm DQN, and episode 5. Finally, use the logAnalyzer to compare the performance of both algorithms

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Fig. 27
figure 27

The demonstration on how Open-TI is used to optimize the OD-Demand matrix. It first visualizes the defined traffic zone information, and sets the observation point to mimic the real-world data collection process. Then based on the gap between simulation observation and real-world observation (count data), the O-D matrix is optimized to mitigate the observation gap by optimization algorithms (e.g., Genetic Algorithm). After the optimization, the final O-D matrix is simulated again, and the comparison of observation is shown in the end

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1.4 Unexpected use cases and improvement potentials

This section provides the unexpected use cases of Open-TI, including the corner case and multi-step problems (Figs. 28, 29).

Fig. 28
figure 28

The figure illustrates that when the agent is tasked with autoDownloadOpenStreetMapFile for the target location but is provided with incorrect longitude and latitude order, it results in an error being raised and failure to retrieve the data. The geographical information provided does not adhere to the default order of [min_long, min_lat, max_long, max_lat], and does not explicitly specify the geographic meaning of each value. This will require more comprehensive corner case processing and information examination

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Fig. 29
figure 29

The figure illustrates that we asked the agent to display the map of the target place but did not provide geographical information. It requires two steps: first, calling queryAreaRange and then inputting the result into showOnMap. However, the call to queryAreaRange stops prematurely, leading to an API mismatch. The alignment between intermediate output and connected sub-tasks still needs to be explored in the future

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Da, L., Liou, K., Chen, T. et al. Open-ti: open traffic intelligence with augmented language model. Int. J. Mach. Learn. & Cyber. 15, 4761–4786 (2024). https://doi.org/10.1007/s13042-024-02190-8

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