NYC Congestion Pricing Analysis

This repository contains an analysis of the impact of New York City's Congestion Relief Zone on vehicle entries, MTA ridership, and traffic patterns.

It includes performance benchmarks for PostgreSQL (Kelsey Kwon), ElasticSearch (Anna Prunty-Burkart), and MySQL (Joshua Pasaye) using the provided datasets and docker compose files.

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📂 Contents

Datasets

• UPDATED_MTA_Congestion_Relief_Zone_Vehicle_Entries__Beginning_2025_20250823.csv.gzip
  Vehicle entry counts into the Congestion Relief Zone by date, time, vehicle class, and toll period.

• UPDATED_MTA_Daily_Ridership_and_Traffic__Beginning_2020_20250823.csv.zip
  Daily MTA ridership traffic counts from 2020–2025.

• UPDATED MTA_Bridges_and_Tunnels_Hourly_Crossings__Beginning_2019_20250823.csv.gzip
  Hourly bridge and tunnel crossing traffic counts from 2019–2025.

• nyc_congestion_datasets.json
  Necessary to run ElasticSearch_final_project.ipynb file.

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Jupyter Notebooks

1. PostgreSQL_final_project.ipynb — PostgreSQL implementation and benchmarking with the datasets.
2. ElasticSearch_final_project.ipynb — ElasticSearch implementation and benchmarking with the datasets.
3. MySQL_benchmark.ipynb — MySQL implementation and benchmarking with the datasets.

Docker files

1. docker-compose-postgres.yml – PostgreSQL docker container to connect to PostgreSQL server.
2. docker-compose-elasticsearch.yml – ElasticSearch docker container to connect to ElasticSearch server.
3. docker-compose-mysql.yml – MySQL docker container to connect to MySQL server.

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Run Code

To run the code, follow these steps:

1. Download all the files in the Contents folder.
2. Download & install Docker Desktop on your machine.
3. Ensure the necessary files are within the same folder as the Jupyter Notebooks.
4. Unzip compressed files.
5. Update any file paths in the Jupyter Notebooks to ensure they run smoothly.
6. Uncomment any !pip install lines to install necessary modules.
7. Run the code.