MemeGroup - Project Bus network - Bus Radar

Introduction

This web application helps the Bus Operator:

• View the current state of public bus network traffic (the bus position, the routes)
• Predict the bus traffic congestion ahead of 15 minutes

Team members

No.	Full name	ID	Agile Role	Progress
1	Vu Hoang Tuan Anh	18812	Back-end Developer, Machine Learning	100%
2	Vo Nguyen Duy Bach	16770	Full-stack Developer	100%
3	Tran Kim Hoan	18810	Front-end Developer, Tester	100%
4	Tran Nguyen Minh Quan	17640	Scrum Master, DevOps, Data Engineer	100%

Programming Languages

Databases

Frameworks

Tools

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Web Application

Requirements

• Nodejs v21.4.0 or newer version.
  • Download here. (After downloading, please install immediately)
• Python 3.12.1 or newer version.
  • Download here. (After downloading, please install immediately)
• Kafka 2.13-3.6.0 or newer version:
  • Download here. (After downloading, please leave it and wait for the installation step in the Project Setup section)
• Visual Studio Code IDE with Code Runner extension
  • Download VSCode IDE first here
  • Download Code Runner extension for VSCode from here
• Any Internet Browser:
  • Google Chrome (Recommend), Firefox, Microsoft Edge, Opera, Brave, Safari, etc.

Project Setup

This instructions assume that the environment operating system is Windows (Recommend using Powershell 7 as the main terminal)

1. Clone the project

• using SSH

  git clone git@github.com:VGU-Meme-Group/busnetwork.git

• or using HTTPS:

  https://github.com/VGU-Meme-Group/busnetwork.git

2. Open the project

• Open the project by using VSCode IDE (Recommended for convenience)
  

3. Set up Streaming Data Server

• (Run the Kafka Server) Locate the kafka binary file you just downloaded into your machine. Assume the file name is kafka_2.13-3.6.0.tgz

  • Then copy the file kafka_2.13-3.6.0.tgz to a folder on your machine. Here I copy it to my C:/ drive to avoid the folder name length limit of Kafka. (Now I have C:/kafka_2.13-3.6.0.tgz)

  • On the C:/ folder, open the terminal and then using this command to extract the Kafka folder:

    tar -xzf .\kafka_2.13-3.6.0.tgz

  • Then change directory to kafka_2.13-3.6.0/

    cd kafka_2.13-3.6.0

  • After that, open 2 terminals from the directory C:/kafka_2.13-3.6.0/ and run these following commands:

    • 1st Terminal (execute the ZooKeeper Server):

      .\bin\windows\zookeeper-server-start.bat .\config\zookeeper.properties 

    • 2nd Terminal (executes the Kafka Server):

      .\bin\windows\kafka-server-start.bat .\config\server.properties

• (Run the Kafka Spring Boot Application) Locate the folder streaming_data:

  cd streaming_data

  • From here, we locate the next 3 files in the following paths and then execute them respectively by clicking the "Run ▶️" button of the Code Runner extension in VSCode:
    • 1st: streaming_data/API/src/main/java/com/project/SpringBootAPIApplication.java
    • 2nd: streaming_data\kafka-consumer\src\main\java\com\project\SpringBootConsumerApplication.java
    • 3rd: streaming_data\kafka-producer\src\main\java\com\project\SpringBootProducerApplication.java

4. Set up Back-end Server

• Locate the backend folder:

  cd backend

• Then install the necessary node modules by using these commands (Make sure that Nodejs have been installed on your machine):

  npm install

• Then we run 2 API Services of the backend in 2 terminals:

  • 1st terminal:

    node index.js

  • 2nd terminal:

    node main.js

5. Set up Machine Learning Server

• Locate the Machine Learning folder:

  cd machine_learning

• Make sure that you have installed Python on your machine, after that, using this command to install all necessary packages:

  pip install -r requirements.txt

• Then we run the Machine Learning (Prediction) Server by using this command:

  python Server.py    

6. Set up Front-end

• Locate the frontend folder:

  cd frontend

• Then install the necessary node modules by using these commands (Make sure that Nodejs have been installed on your machine):

  npm install

• Finally, execute this command to enables the GUI of the Web application:

  npm run dev

7. Access the Web Application

• Now, all you need to do is just open your internet browser (E.g: Google Chrome) and then type the address localhost:3000 on your address bar, then press Enter.

  

• Then the GUI will look like this: