hpl_api

Welcome to the official repository of the Hyperloop Decision Making Ecosystem (HDME) project. This project is an integral part of Aleksejs Vesjolijs' PhD dissertation and aims to provide an advanced framework for decision-making in Hyperloop projects using ambient Artificial Intelligence (AI).

Overview

The Hyperloop Decision Making Ecosystem (HDME) is a robust platform designed to optimize decision-making processes in Hyperloop systems. The repository contains APIs, simulations, and tools necessary for analyzing, designing, and implementing Hyperloop networks. It leverages state-of-the-art methodologies such as the E(G)TL Model and system dynamics modeling.

Research Publications on HDME

The HDME project has been featured in several peer-reviewed research publications, demonstrating its scientific and practical contributions:

Vesjolijs, A. (2024). The E(G)TL Model: A Novel Approach for Efficient Data Handling and Extraction in Multivariate Systems. Applied System Innovation, Switzerland, 7(5), p.92. DOI: 10.3390/asi7050092
Vesjolijs, A. (2024). Hyperloop Decision Making Ecosystem Empowered by Ambient Artificial Intelligence. Future Tech Conference 2024, London, United Kingdom.
Vesjolijs, A. (2024). Hyperloop Routes Optimization Considering Barren Soil: Case of Latvia. International Conference on Reliability and Statistics in Transportation and Communication 2024.
Vesjolijs, A. (2024). Implementation Framework for Hyperloop Decision-Making Ecosystem. Digital Baltic & Information Systems 2024 (DB&IS 2024). CEUR: Paper 10
Vesjolijs, A. (2024). Overview of Factors and Methods for Analysis of Hyperloop Project. Springer Nature Switzerland, Cham, pp. 281–291. DOI: 10.1007/978-3-031-53598-7_25

Features

System Dynamics Dashboard:
- A dynamic visualization tool for analyzing Hyperloop system metrics and scenarios.
- Built-in functionality for exploring various decision-making parameters.
EGTL Model Integration:
- Implements the E(G)TL Model for data transformation and efficient real-time decision-making.
Simulation Scenarios:
- Simulation scenarios for analyzing key metrics, for example social acceptance (SAC), technological feasibility (TFE), and other.
Database Management:
- Structured table creation DDL scripts for EGTL stores:
  - swf_fusion_store.sql
  - swf_staging_store.sql
  - swf_alliance_store.sql
Unit Testing Framework:
- Comprehensive unit tests for key components of the project.
- Test cases located under tests/unit.

Installation

Prerequisites

Python 3.8+
Dependencies listed in requirements.txt.

Setup Instructions

Clone the repository:

git clone https://github.com/pirrencode/hpl_api.git

Install dependencies:
```
pip install -r requirements.txt
```
Configure your Snowflake connection settings in the application.

Usage

Running the Application

Start the dashboard:
```
python app.py
```
Navigate to the provided URL to access the dashboard.

Running Unit Tests

To execute unit tests, use: ```bash pytest tests/unit/{unit_test_name}.py

Repository Structure

hpl_api/
│
├── app.py                   # Main application file
├── simulation_scenarios.py  # Simulation modeling scenarios
├── criterion_factors_logic.py  # Fusion Store criterion calculation logic
├── requirements.txt         # Python dependencies
├── swf_fusion_store.sql     # SQL for Fusion store creation
├── swf_staging_store.sql    # SQL for Staging store creation
├── swf_alliance_store.sql   # SQL for Alliance store creation
├── tests/
│   ├── unit/                # Unit tests
│   └── integration/         # Integration tests
└── README.md                # Project description
└── CHANGELOG.md                # Version changes
└── BACKLOG.md                # Pending tasks for completion
└── LICENSE                # HDME licensing information
       

# Contributors
Aleksejs Vesjolijs
PhD Candidate, 
Transport and Telecommunication Institute, Riga

pirrencode / hpl_api