TrafficGPT

Installation

TrafficGPT does not require installation, you should just clone the code and run locally.

clone https://github.com/lijlansg/TrafficGPT.git
cd TrafficGPT

The operation of TrafficGPT requires the following software support:

Python
SUMO
PostgreSQL

At the same time, please install the required third-party libraries:

pip install -r requirements

Configuration

LLM Configuration

First, you need to configure OpenAI-Key. Please create a ./config.yaml file in the root directory and add the following content to the file (please modify the content to your own settings):

OPENAI_API_TYPE: 'azure' #'azure'  OR 'openai'
# for 'openai'
OPENAI_KEY: 'sk-xxxxxxxxxxx' # your openai key
# for 'azure'
AZURE_MODEL: 'XXXXX' # your deploment_model_name 
AZURE_API_BASE: https://xxxxxxxx.openai.azure.com/ # your deployment endpoint
AZURE_API_KEY: 'xxxxxx' # your deployment key
AZURE_API_VERSION: '2023-03-15-preview'

Here we recommend using ChatGPT-3.5 to run as LLM. If you want to use your own LLM, please refer to LangChain-Large Language Models to define Your own LLM. In this case, please modify the following sections in ./DataProcessBot.py and ./SimulationProcessBot.py to configure your own LLM.

OPENAI_CONFIG = yaml.load(open('config.yaml'), Loader=yaml.FullLoader)
if OPENAI_CONFIG['OPENAI_API_TYPE'] == 'azure':
    os.environ["OPENAI_API_TYPE"] = OPENAI_CONFIG['OPENAI_API_TYPE']
    os.environ["OPENAI_API_VERSION"] = OPENAI_CONFIG['AZURE_API_VERSION']
    os.environ["OPENAI_API_BASE"] = OPENAI_CONFIG['AZURE_API_BASE']
    os.environ["OPENAI_API_KEY"] = OPENAI_CONFIG['AZURE_API_KEY']
    llm = AzureChatOpenAI(
        deployment_name=OPENAI_CONFIG['AZURE_MODEL'],
        temperature=0,
        max_tokens=1024,
        request_timeout=60
    )
elif OPENAI_CONFIG['OPENAI_API_TYPE'] == 'openai':
    os.environ["OPENAI_API_KEY"] = OPENAI_CONFIG['OPENAI_KEY']
    llm = ChatOpenAI(
        temperature=0,
        model_name='gpt-3.5-turbo-16k-0613',  # or any other model with 8k+ context
        max_tokens=1024,
        request_timeout=60
    )

Fine, now, you can run ./SimulationProcessBot.py.

python ./SimulationProcessBot.py

Database Configuration

Then, in order to run ./DataProcessBot.py, we need to configure the database. Until then, please refer to Github:OpenITS-PG-SUMO Import data into a PostgreSQL database.

Afterwards, your database should contain the following four data tables: topo_centerroad, spatial_ref_sys, zone_roads, the_synthetic_individual_level_trip_dataset. In order to simplify the real-time query operation, two new tables need to be created in the following query sequence: road_level_trip_dataset and road_volume_per_hour.

Create road_level_trip_dataset：

CREATE TABLE road_level_trip_dataset (
  trip_id INT,
  traveller_id VARCHAR(50),
  traveller_type VARCHAR(50),
  departure_time TIMESTAMP,
  time_slot VARCHAR(50),
  o_zone VARCHAR(50),
  d_zone VARCHAR(50),
  path VARCHAR(50),
  duration FLOAT
);

INSERT INTO road_level_trip_dataset
SELECT
  trip_id,
  traveller_id,
  traveller_type,
  departure_time,
  time_slot,
  o_zone,
  d_zone,
  path::VARCHAR, 
  duration
FROM
  (
    WITH split_paths AS (
      SELECT
        trip_id,
        traveller_id,
        traveller_type,
        departure_time,
        time_slot,
        o_zone,
        d_zone,
        unnest(string_to_array(path, '-')) AS path,
        duration
      FROM
        the_synthetic_individual_level_trip_dataset
    )
    SELECT * FROM split_paths
  ) AS split_data;

Create road_volume_per_hour

CREATE TABLE road_volume_per_hour (
  hour_start TIMESTAMP,
  road VARCHAR(50),
  road_count INT
);

INSERT INTO road_volume_per_hour
SELECT
  DATE_TRUNC('hour', departure_time) AS hour_start,
  path AS road, 
  COUNT(*) AS road_count
FROM road_level_trip_dataset
GROUP BY hour_start, road
ORDER BY hour_start, road;

After all data tables are created, please create a ./dbconfig.yaml file in the root directory and write the following content into the file:

username: your_user_name
password: your_password
host: localhost
port: 5432
db_name: OPENITS

Fine, now, you can run ./DataProcessBot.py.

python ./DataProcessBot.py

Demo

Simple Commands Multi-round dialogue

09-50-07.mp4

Fuzzy Instructions and Human Intervention

fuzzy.mp4

Insightfull Assitance

insightful.mp4

Contact

If you have any questions or suggestions about this project, please send me an Issue and PR, or contact us by email: siyaozhang@buaa.edu.cn