May 2024 - Jun 2024
High Traffic Pattern on I-94
This project explores six years of hourly traffic data from the busiest corridor between Minneapolis and St. Paul, analyzing the impact of time, weather, and holidays. Using Python, it uncovers key traffic patterns and provides actionable insights and visual metrics for planners, commuters, and anyone navigating the city’s congestion.
Overview
This project delves into traffic patterns on westbound I-94 near Minneapolis–St. Paul, where I analyze hourly traffic data from 2012 to 2018. By examining the influence of time, weather, and holidays, the goal was to uncover actionable insights that could inform city planners, transportation agencies, and commuters alike.
Approach
Using Python and Pandas, I began by cleaning and segmenting the dataset, breaking it down by time of day, week, month, and year to spot any emerging trends. I then integrated key weather variables like temperature, precipitation, and conditions to assess how they affected traffic flow. To make the data more interpretable, I visualized patterns through time series, boxplots, and grouped comparisons using Seaborn and Matplotlib.
Key Findings
Time-based factors had the most significant impact on traffic, with weekday rush hours and seasonal variations being the primary drivers of traffic volume.
Surprisingly, traffic volume remained high even in adverse weather conditions, such as light snow and storms.
Holiday traffic generally saw a dip, but there were notable exceptions, like New Year’s Day, which experienced unexpected traffic peaks.
Outcome
This analysis provides a data-driven insights for optimizing traffic management strategies on I-94, which could be used to improve the flow of traffic and the efficiency of transportation infrastructure.
