Akash Dubey

Akash Dubey

I've been working in software for over 10 years 😉

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 RepositoryInteractive 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.