Traffic Predictor Model using ARIMA

Overview

This project is a Traffic Predictor model that uses the ARIMA (AutoRegressive Integrated Moving Average) algorithm to forecast future traffic counts for various types of vehicles. The model is designed to predict traffic 30 minutes into the future based on historical data.

Features

• Traffic Prediction: Predicts the number of vehicles on the road for a specified period into the future.
• Custom Prediction: Allows users to predict traffic starting from any specific time index and for a specified number of minutes into the future.
• Data Visualization: Plots the actual vs. predicted traffic data for easy comparison and analysis.

Dataset

The model uses a time series dataset that records traffic counts for different types of vehicles at various timestamps. The dataset is stored in a CSV file (cars.csv), which includes a single column with vehicle counts:

Example of CSV Structure

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Car 4 2 1 0 3 ...

The CSV file contains a column named Car with integer values representing the traffic count for each time period.

Installation

1. Clone the repository:

   bash

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   git clone https://github.com/anirudhpk01/traffic-predictor-arima.git cd traffic-predictor-arima

2. Install dependencies:

   bash

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   pip install -r requirements.txt

   Ensure requirements.txt includes necessary libraries such as pandas, numpy, matplotlib, statsmodels, and pmdarima.