Parking Analytics Infrastructure

Built a real-time parking analytics pipeline and leadership dashboarding system to track garage flow, utilization, and event-day vehicle movement at scale.

Parking analytics dashboard and garage flow visualization

Project Snapshot

Overview

This project started with a new parking structure equipped with camera-based monitoring at every entrance, exit, and parking bay. I helped build the data infrastructure needed to turn those raw camera events into something operationally useful.

The core challenge was not just collecting the data, but stitching together a full vehicle journey: when a car entered the structure, where it parked, when it left its spot, and when it ultimately exited the building. That required automated ingestion, cleaning, entity matching, and transformation logic that could support both historical analysis and live dashboards.

What I Built

Technical Approach

Each event generated a constant stream of records from multiple camera sources. I worked on ingesting those feeds through APIs and loading them into our database quickly enough to support live operational reporting. From there, I transformed the data into a cleaner event-level table by matching camera records using license plate information.

That join logic made it possible to reconstruct a vehicle lifecycle across disconnected systems: entry into the structure, parking activity within a bay, departure from the bay, and final exit. I then saved that transformed table and automated the refresh process so it could run daily through Jenkins using scripts stored in GitHub.

Dashboards & KPIs

On top of the pipeline, I built Power BI dashboards that gave leadership visibility into parking operations in a way they had never had before. Instead of just seeing counts, they could monitor flow, speed, utilization, and post-event clearing behavior.

Business Impact

The biggest impact of the project was that it created a new foundation for operational decision-making. The parking team could now use live and historical data to better understand how traffic moved through the structure and where bottlenecks emerged.

That also opened the door to experimentation and A/B testing. For example, we could start testing different traffic flow patterns by event type, vary which entrances and exits were used, and route vehicles differently depending on projected attendance, expected vehicle volume, and the number of parking zones we wanted to activate. In other words, the project shifted parking from reactive management to a more measurable and optimizable operation.

Why This Project Matters

This project is a good example of the type of work I enjoy most: taking messy, high-volume operational data and turning it into something reliable, scalable, and decision-ready. It combined engineering, analytics, and business understanding in one workflow — from APIs and automation to KPI design and real-time reporting.

Key Takeaways