Real-Time Traffic Monitoring (RTTM) System

Table of Contents

• About the Project
• Built With
• Getting Started
  • Prerequisites
  • Installation
    • Quick Setup Guide
    • Database Setup
    • Environment Preparation
    • Running the Scripts/Datapoints Server
    • Running the 2D Camera Server
    • Running the Application
• Features
• Contributing

About the Project

Vehicle detection is crucial for analyzing traffic flow data to enhance planning in intelligent transportation systems. Machine Learning technology has increasingly been utilized for vehicle detection in both 2D real-time traffic flow videos and 3D point clouds. However, adverse weather conditions such as fog, rain, snow, extreme wind, and others pose challenges for 2D vehicle detection. 3D LiDAR point clouds offer more resistance to these conditions. While much research on 3D vehicle detection has focused on autonomous driving with LiDAR cameras on vehicles, there is a research gap in real-time vehicle detection for intelligent transportation with stationary LiDAR cameras on highways/freeways. This project aims to develop a system that collects real-time traffic flow data through 3D LiDAR cameras, processes the data for vehicle detection and classification, and provides a web-based service that enables real-time vehicle tracking, classification, and statistical traffic flow data visualization.

Built With

• React
• Bootstrap

Getting Started

Prerequisites

• Node.js
• Firebase

Installation

Quick Setup Guide

This guide is designed to help developers quickly set up their project environment and database. Ensure all prerequisites listed in the "Getting Started" section are completed before proceeding.

Prerequisites

• Python 3
• PostgreSQL
• Command line access

Database Setup

This section provides a detailed guide for setting up the PostgreSQL database for the RTTM system.

Prerequisites

• PostgreSQL 14.12 or higher (installed via Homebrew on macOS)
• Command line access
• User account with sudo privileges

Steps

1. Start PostgreSQL Ensure your PostgreSQL server is running. On macOS with Homebrew installation, use:

   brew services start postgresql@14
   

   If it's already running and you need to restart:

   brew services restart postgresql@14
   

2. Create the RTTM Database Connect to PostgreSQL as the superuser. On macOS, this is typically your system username:

   psql postgres
   

   Once connected, create the RTTM database:

   CREATE DATABASE "RTTM";

3. Create and Configure User Still in the PostgreSQL prompt, create the 'jim2' user if it doesn't exist and grant necessary privileges:

   CREATE USER jim2 WITH PASSWORD 'rttm';
   ALTER USER jim2 WITH CREATEDB;
   GRANT ALL PRIVILEGES ON DATABASE "RTTM" TO jim2;

4. Connect to the RTTM Database Exit the current session and connect to the RTTM database as jim2:

   \q
   psql -U jim2 -h localhost -d RTTM
   

   Enter the password ('rttm') when prompted.

5. Initialize Database Schema Once connected to the RTTM database, run the initialization script:

   \i website/scriptsServer/init-db.sql
   

   Note: Ensure you're in the correct directory where the init-db.sql file is located, or provide the full path to the file.

6. Verify Database Creation After running the initialization script, you can verify the table creation:

   \dt
   

   This should display a list of tables including:

   • FramePrediction
   • HistoricalData
   • HistoricalData_LaneVehicleCount
   • HistoricalData_VehicleTypeCount
   • Lane
   • ModifiedVehicle
   • Real_Time_Traffic_Data
   • User
   • VehicleDetectionEvent
   • VehicleHistory
   • VehicleType
   • Weather

Troubleshooting

• If you encounter permission issues, ensure the 'jim2' user has been created and granted appropriate permissions.
• If the init-db.sql file is not found, check your current directory and file path.
• For any connection issues, verify that PostgreSQL is running and that you're using the correct host, port, and credentials.

Remember to keep your database credentials secure and never commit them to version control.

Environment Preparation

1. Navigate to the website directory:

cd website

2. Execute scripts to set up the environment:

• Generate the initial data frame:

python 3DGenerateDataFrameMod.py

• Populate the database:

Run the following scripts in sequence:

python ProcessAggregatedData.py
python ProcessPerSecondData.py
python ProcessPerMinuteData.py
python ProcessPerHourData.py
python ProcessPerDayData.py
python ProcessPerWeekData.py
python ProcessPerMonthData.py
python ProcessPerYearData.py

After running these scripts, proceed with the server setup.

3. Start the server:

Navigate to the scriptsServer directory and run the server:

cd scriptsServer
node server.js

4. Start the frontend:

Open a new terminal, navigate to the website directory, and run:

npm start

Running the Scripts/Datapoints Server

To start the scripts server, follow these steps:

1. Navigate to the server directory:

cd website/scriptsServer

2. Install dependencies using Yarn:

yarn add express dotenv bcrypt cors express-validator jsonwebtoken pg

3. Start the server:

node server.js

Running the 2D Camera Server

1. Navigate to the server directory:

cd website/scriptsServer

2. Start the camera server (ensure the camera is attached):

python server2DCamera.py

Running the Application

1. Clone the repository:

git clone https://github.com/CalTransProject/rttm.git

2. Install packages:

npm install

3. Navigate into the website directory:

cd website

4. Start the web server:

npm start

Default Login Credentials

username: rttm45304@gmail.com
password: 1234abcd

Features

• Traffic Camera Management
• Real-Time Data Processing
• Predictive Traffic Analysis
• Historical Data Visualization
• User-friendly Interface and Responsive Design
• User Management

Contributing

Interested in contributing to the RTTM project? Here's how you can help:

1. Fork the Project

2. Create a feature branch:

git checkout -b feature/ANewFeature

3. Commit your changes:

git commit -m 'Added ANewFeature'

4. Push to the branch:

git push origin feature/ANewFeature

5. Open a Pull Request