https://github.com/diarcode/flowcast
FlowCast is an advanced forecasting system that leverages historical and real-time data to provide precise predictions of passenger flows and public transit demand.
https://github.com/diarcode/flowcast
forecasting historical-data-analysis machine-learning passenger-flow-analysis predictive-analytics real-time-data transit-optimization
Last synced: 8 months ago
JSON representation
FlowCast is an advanced forecasting system that leverages historical and real-time data to provide precise predictions of passenger flows and public transit demand.
- Host: GitHub
- URL: https://github.com/diarcode/flowcast
- Owner: DiarCode
- License: mit
- Created: 2024-09-11T18:39:38.000Z (almost 2 years ago)
- Default Branch: dev
- Last Pushed: 2025-02-26T21:13:58.000Z (over 1 year ago)
- Last Synced: 2025-03-13T09:17:38.317Z (over 1 year ago)
- Topics: forecasting, historical-data-analysis, machine-learning, passenger-flow-analysis, predictive-analytics, real-time-data, transit-optimization
- Language: Jupyter Notebook
- Homepage:
- Size: 30.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
Awesome Lists containing this project
README
# FlowCast: Precision Forecasting of Passenger Flows and Public Transit Demand
## Overview
FlowCast is an advanced, scalable system designed to forecast passenger flows and public transportation demand using cutting-edge machine learning algorithms. By integrating historical data with real-time inputs, the system delivers high-precision predictions to enhance urban transit management. FlowCast is designed for smart cities, leveraging both on-premise and cloud-based infrastructures to optimize public transportation systems dynamically.
## Features
- **Advanced Algorithms**: Leverages a hybrid approach combining machine learning models (e.g., Random Forest, Neural Networks) for precise demand forecasting.
- **Real-Time Data Integration**: Supports real-time data collection and analysis from various sources (e.g., sensors, ticketing systems).
- **Scalability**: Architected to scale with increasing data volumes using modern microservices and cloud-native technologies.
- **User Interface**: A frontend interface for visualizing predictions, trends, and insights for city planners and transport authorities.
- **REST API**: Provides an API for real-time predictions, data input, and integration with external systems.