Optimizing CTA Ad Placements

Inspiration

The project was inspired by the realization that while advertising is a key non-fare revenue source for the Chicago Transit Authority (CTA), ad placements were not necessarily optimized for maximum rider exposure. We saw an opportunity to use data-driven analysis to better align ad strategy with real-world rider behavior, helping CTA unlock more revenue without major operational changes.

What it does

This project identifies opportunities for the Chicago Transit Authority (CTA) to increase advertising revenue by reallocating ad placements to areas with the highest rider exposure. By analyzing foot traffic, dwell time, and demographic data, we created an Exposure Score that ranks stations and routes based on their potential advertising value. The project highlights underutilized high-traffic areas and recommends strategic reallocation of advertising inventory to maximize impressions and revenue.

How we built it

Data Collection: Gathered public datasets on street-level traffic volume, speed data, and ZIP code aggregation. Mapping and Visualization: Created heatmaps and bubble maps to visualize high-dwell and high-traffic zones. Analysis: Calculated an "Quality Score" by combining foot traffic intensity, dwell time, and demographic bonuses. Optimization: Identified mismatches between current ad placements and high-exposure areas, and modeled potential revenue gains from reallocation.

Challenges we ran into

Data Gaps: Some areas lacked recent or complete traffic or dwell time data, requiring careful interpolation. Granularity Mismatch: Merging datasets at different spatial scales (street-level vs. ZIP code) required additional processing and validation. Balancing Simplicity and Accuracy: Building a model that was robust yet understandable for decision-makers posed a design challenge.

Accomplishments that we're proud of

Built a comprehensive exposure model combining multiple datasets (traffic volume, speed/dwell time, demographics). Identified clear mismatches between current ad placements and optimal locations. Modeled a potential revenue uplift with simple operational adjustments, without the need for major infrastructure investments. Produced actionable, data-driven recommendations that could directly benefit CTA's financial performance.

What we learned

How to integrate multiple urban datasets (traffic volume, dwell time, demographics) to model human movement patterns. Techniques for creating exposure scoring systems combining foot traffic, dwell time, and location factors. How small operational adjustments (like reallocating ad spaces) can have large financial impacts.

What's next for Optimizing CTA Ad Placements

Expand analysis to include seasonal and time-of-day variations to further refine ad targeting. Incorporate real-time data (e.g., smart card taps, mobile location data) for dynamic exposure scoring. Pilot dynamic digital ads in top-ranked stations and corridors to test real-world revenue impacts. Explore partnerships with advertisers to match ad content with rider demographics for even greater engagement.