Peak Hour Analysis with Waiting Time and Normal Distribution

This project aims to identify peak hours based on waiting time data extracted from a CSV file. It utilizes Python and the concept of normal distribution to analyze the waiting times and determine periods of high traffic or activity.

Overview

The core functionality of this project involves:

1. Data Loading: Reading waiting time data from a CSV file into a Python data structure.
2. Data Processing: Cleaning and preparing the waiting time data for analysis.
3. Normal Distribution Analysis:
   • Calculating the mean and standard deviation of the waiting times.
   • Using the normal distribution to identify outliers or periods of significantly higher waiting times.
   • Identifying time periods where waiting times exceed a certain threshold (e.g., above a certain number of standard deviations from the mean).
4. Peak Hour Identification: Determining the time ranges that correspond to the identified periods of high waiting times, representing peak hours.
5. Basic Output: displaying the identified peak hours.

Basic Implementation Details

• Python: The project is implemented in Python, leveraging libraries like csv for data loading and statistics (or numpy for more advanced statistical calculations) for normal distribution analysis.
• CSV Input: The project expects waiting time data in a CSV file, with a column containing the waiting time values and a column containing the time of the waiting time.
• Normal Distribution: A basic normal distribution assumption is used to identify outliers. More advanced statistical methods can be implemented in future versions.
• Output: The script will output the identified peak hours based on the waiting time analysis.

Getting Started

1. Prerequisites:
   • Python 3.x installed.
   • A CSV file containing waiting time data.
2. Installation:
   • No external libraries are required for the basic version, if you use the statistics module. If you decide to use numpy, you will have to install it: pip install numpy
3. Usage:
   • Place your CSV file in the same directory as the Python script.
   • Run the Python script.
   • The script will print the identified peak hours to the console.

Future Enhancements

• Implement more sophisticated statistical methods for peak hour detection.
• Add support for different data formats.
• Visualize the waiting time data and peak hours using graphs.
• Allow users to configure parameters such as the threshold for peak hour detection.
• Add more robust error handling.
• Add the ability to save the results to a file.