Subway Traffic Analysis

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Table of Contents

1. Project Overview
2. Actions
3. Results
4. Summary of insights
4. GithubRepo-RawCode

1. Project Overview

The goal of our project was to provide actionable data that will help optimize the placement of WTWY Organizations street teams, such that they can gather the most signatures, ideally from those who will attend the event and contribute to their cause.

2. Actions

Used MTA Turnstyle Data for April 2015-2017 (http://web.mta.info/developers/turnstile.html)

  1. Identified stations with highest traffic volume.

  2. Executed Time Analysis.

  3. Identified stations with highest traffic volume.

  4. Identified Individual stations with highest potential for awareness and fundraising.

Tools Used: Python, Pandas, Matplotlib, Seaborn, Folium, ZipCode API

3. Results

Location Recommendation

  1. Recommended locations in green.

  2. Traffic received at each station is proportional to the circle around the point.

Map

Day of the Week Recommendation:

Daily

Top 15 Recommendations:

Top15

Time/Hour Recommendation:

Hourly

4. Summary of insights

  1. Top 15 Stations (3%) cover 13.5% of foot traffic
  2. Stations near Universities and Tech hubs present opportunity for outreach and awareness.
  3. Stations classified as high per capita income present opportunities for fundraising.
  4. Weekdays mornings not recommended.
  5. Overall stations are better targeted between 4-8pm
Written on April 19, 2018