# Rutgers Bus Analysis

URL: https://akashdubey.me/projects/rutgers-bus-analysis
Date: Tue Dec 10 2024 00:00:00 GMT+0000 (Coordinated Universal Time)
Tags: Data, Research

Summary: A comprehensive data analysis of the Rutgers University bus transit system

Sections: Project Overview | Data Collection Process | Key Visualizations | Key Findings | Technologies Used | Future Improvements
Links: View Repository (https://github.com/AkeBoss-tech/RutgersBusAnalysis) | Interactive Map (https://akeboss-tech.github.io/RutgersBusAnalysis/) | this pull request (https://github.com/athuler/PassioGo/pull/32)

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DataResearch

Rutgers Bus Analysis

A comprehensive data analysis of the Rutgers University bus transit system

View Repository Interactive Map this pull request

Rutgers Bus Analysis

Analysis by Akash Dubey

Group Members: Charles Tang, Nathaniel Correa, Akash Dubey, and Khang Nguyen

Project Overview

For our Honors College Forum Social Impact Plan, our group aimed to improve traffic efficiency at Rutgers New Brunswick. Since the primary form of transportation for students is the Rutgers bus system, we conducted an extensive analysis of bus transit data to identify patterns, optimize routes, and improve service reliability.

View Repository | Interactive Map

Data Collection Process

I developed a custom Python script to collect real-time bus data from the Rutgers PassioGO system:

Polled the PassioGO API every 30 seconds
Contributed to the PassioGO API with this pull request
Hosted on Azure VM for reliable 24/7 operation
Collected over 100MB of structured transit data
Gathered approximately 300,000+ data points over one week

Key Visualizations

Route Load Analysis

Average Load Across Multiple Routes

*Average load patterns across all routes throughout the day*

LX Route Analysis

Average Load of LX over the course of a day

*Detailed analysis of the LX route load patterns*

Route Loop Times

Loop times for different routes

*Calculated loop completion times for each route*

Bus Capacity Analysis

Most filled buses

*Analysis of bus capacity utilization*

Weekly Operations

Total Buses operating during the week

*Total number of buses in operation throughout the week*

Speed Analysis

Average speed of a bus

*Speed patterns of a bus serving multiple routes (LX, H, REXB, EE, and F Routes)*

Interactive Visualization

Bus Movement Animation

*Real-time visualization of bus movements across campus*

Key Findings

Peak wait times correlate with class change periods
Route efficiency varies significantly by time of day
Load patterns follow predictable daily cycles
Identified optimal bus distribution patterns

Technologies Used

Python 3.11
Data Analysis: Pandas, NumPy
Visualization: Matplotlib, Seaborn, Folium
API Integration: Requests
Cloud Infrastructure: Azure VM

Future Improvements

Real-time prediction model for wait times
Interactive web dashboard
iOS and Android apps for real-time tracking
Machine learning for route optimization
Weather impact analysis

This project demonstrates the power of data analysis in improving public transit systems and enhancing the student experience at Rutgers University.

Note: This project is for research and analysis purposes only and is not officially affiliated with Rutgers University.