This repository contains the implementation of traffic congestion prediction, focusing on assessing and comparing the effectiveness of different algorithms for long-term traffic congestion prediction.
The primary objective of this project is to evaluate various algorithms in predicting long-term traffic congestion. Two datasets are used: one obtained from Kaggle and another generated from road traffic photos in Tirana. The data undergoes cleaning and preprocessing, incorporating a Convolutional Neural Network (CNN - YOLO V8) model for image processing and car counting. The YOLOv8 model was trained on a labeled car images dataset available at this link using CUDA and PyTorch.
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yolov8-car-detection: This file contains the model responsible for detecting cars in pictures, contributing to the creation of a time series dataset with images from Tirana.
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predictions/ARIMA: Folder containing predictions using the AutoRegressive Integrated Moving Average (ARIMA) model.
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predictions/GRU: Folder containing predictions using the Gated Recurrent Unit (GRU) model.
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predictions/LR_SVR: Folder containing predictions using Linear Regression (LR) and Support Vector Regression (SVR) with various kernels.
- SVR_Kernels:
- Linear Kernel:
- Polynomial Kernel:
- Sigmoid Kernel:
- RBF Kernel:
- Precomputed Kernel:
- Custom Kernel:
- SVR_Kernels:
- Autoregressive Integrated Moving Average (ARIMA)
- Gated Recurrent Unit (GRU)
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Linear Regression (LR)
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Support Vector Regression (SVR) with various kernels.
- Linear Kernel
- Polynomial Kernel
- Sigmoid Kernel
- Radial Basis Function (RBF) Kernel
- Precomputed Kernel
- Custom Kernel
The majority of labels in the output figures are in Albanian, as this was part of my master's dissertation project.