UW Transportation Services Campus Parking Efficiency Project

Analyzed parking transaction records to improve resource allocation and reduce search time

Overview

Analyzed 11M+ parking transaction records using Python, SQL, and Snowflake to uncover usage patterns and weather impacts. Developed predictive models and interactive dashboards (Tableau/Power BI) that cut average parking search time by 27% and improved resource allocation.

The Challenge

The challenge was to analyze a large volume of parking transaction data to identify patterns and optimize resource allocation. We needed to understand how weather and other factors affected parking usage and develop predictive models to guide decision making.

The Solution

I analyzed over 11 million parking transaction records using Python, SQL, and Snowflake to uncover usage patterns and understand the impact of weather on parking behavior. I developed predictive models that could forecast parking demand based on various factors. I also created interactive dashboards using Tableau and Power BI that provided insights to decision makers.

My Thoughts

This project demonstrated the power of data analysis in solving practical problems. By analyzing parking transaction data, we were able to reduce average parking search time by 27% and improve resource allocation. The project reinforced my belief in the value of data-driven decision making in optimizing operations and improving user experience.

Key Achievements

• Analyzed 11M+ parking transaction records using Python, SQL, and Snowflake
• Reduced average parking search time by 27% through predictive modeling
• Improved resource allocation with data-driven insights
• Created interactive dashboards using Tableau and Power BI
• Identified correlations between weather patterns and parking usage