Investigating High Traffic Indicators on Westbound I-94

This project analyzes hourly traffic data collected between 2012 and 2018 from I-94 West between Minneapolis and St. Paul. By uncovering key indicators of high traffic congestion, this project aims to support better planning, safety, and efficiency on this critical highway.

Table of Contents

1. Introduction
2. Objective
3. Stakeholders
4. Use Case
5. Why I-94?
6. Dataset Overview
7. Key Insights
   • Time-Based Indicators
   • Weather Indicators
   • Holiday Patterns
8. Conclusion
9. Future Directions
10. How to Run

Introduction

Interstate 94 (I-94) is a major transportation artery through the American Midwest, stretching 1,585 miles from Billings, Montana, to Port Huron, Michigan. The stretch between Minneapolis and St. Paul is particularly known for its heavy traffic.

Objective

To identify key factors that influence high traffic volume on westbound I-94 using data from 2012 to 2018. This includes analyzing temporal trends, seasonal factors, weather conditions, and holiday impacts on how traffic patterns vary.

Potential Application

• Transportation agencies – to implement data-driven traffic mitigation strategies.
• Urban planners – to optimize future infrastructure design.
• Logistics companies – to streamline delivery routes.
• Commuters – to better anticipate peak congestion times and plan commutes accordingly.

Why I-94?

• Strategic Connectivity: Links major Midwestern cities like Minneapolis, Milwaukee, and Chicago.
• Economic Impact: Facilitates high-volume freight movement.
• Congestion Hotspot: The Minneapolis–St. Paul segment is notorious for westbound traffic congestion.

Dataset Overview

• Source: Metro Interstate Traffic Volume dataset by John Hogue (UCI ML Repository).
• Timeframe: 2012–2018
• Scope: Hourly westbound traffic volumes near Minneapolis-St. Paul
• Features Include:
  • Time (hour, day of week, month)
  • Weather (temperature, precipitation, weather type)
  • Holiday indicator

Key Insights

Time-Based Indicators

• Daytime traffic is significantly higher than nighttime.

• Peak hours: Morning (6 AM) and evening (5 PM), especially on weekdays.

• Fridays show particularly high traffic volume.

• Weekend traffic is more evenly distributed, peaking around midday.

• Seasonal patterns: Highest traffic occurs from March to October.

  

Weather Indicators

• Overall weak correlation between weather and traffic volume.
• Certain adverse weather types (e.g., light snow, thunderstorms) surprisingly coincide with high traffic averages, indicating that traffic continues despite poor conditions.

Holiday Patterns

• Lower average traffic on most U.S. holidays.
• New Year’s Day is an exception, which represents the highest average traffic volume.
• Memorial Day and Independence Day show elevated traffic but with more variability.
• Thanksgiving and Christmas see relatively lower volumes, possibly due to early travel or fewer commuters.

Conclusion

This analysis highlights key indicators of high traffic on I-94:

• Temporal factors (hour, day, month) have a strong influence on traffic patterns.
• Weather plays a lesser role but shows surprising exceptions during certain severe conditions.
• Holiday traffic tends to dip except for New Year's Day and a few other key holidays.

These insights provide a data-driven foundation for improving traffic flow, enhancing infrastructure planning, and supporting daily commute decisions.

Future Directions

My goal for this project is to take it a step further by applying Linear Regression to model and predict traffic volume. To build on this work, future analyses could explore:

• Real-time data integration for live traffic predictions
• Incorporating accident reports and roadwork data to improve the accuracy of traffic volume estimates

How to Run

To review the project locally:

1. Clone the repository:

   git clone https://github.com/shree131/high-traffic-indicators_interstate_94.git
   cd high-traffic-indicators_interstate_94
   

---

© 2024 All rights are reserved.