Inspiration: After recently moving into a dorm, I started enjoying Seattle's taco food trucks and wanted to explore how technology could solve a real logistical challenge for small vendors. Food trucks operate in fast-paced environments with limited staff and little time for proactive marketing. I was interested in how accessible technology could help them make smarter decisions using real-time environmental data.
What it does: This project explores how wind conditions could help food truck operators make more informed decisions about attracting nearby customers. Wind speed and direction can influence how food aromas travel, potentially affecting how effectively a vendor attracts foot traffic. I built initial code that retrieves real-time weather data through an API, establishing the technical foundation for a tool that could eventually translate changing environmental conditions into actionable recommendations for food truck operators.
How I built it: I used Python to build code that connects to a weather API and retrieves weather data relevant to the project's core concept. This established an initial data pipeline for exploring how real-time wind conditions could inform decisions for small food vendors. The project focuses on using accessible technology and live environmental data to address a practical problem in a fast-paced, resource-constrained setting.
Challenges I ran into: One challenge was identifying a practical starting point for a concept that could incorporate multiple data sources and automation features. Weather data provides a useful foundation, but translating environmental conditions into reliable, actionable recommendations requires careful consideration of how the data relates to real-world conditions around individual food trucks.
What I learned: This project explored how real-time API data can serve as the foundation for a practical decision-support tool. It also highlighted the potential for environmental data to be applied to an unconventional problem: helping small food vendors make better use of their surroundings without relying on expensive marketing infrastructure.
What's next: Future development could extend the initial weather API integration by analyzing wind speed and direction to generate recommendations about when food aromas are most likely to reach nearby foot traffic. Additional features could include camera-based pedestrian counting, automated iMessage notifications, and a dashboard for visualizing environmental trends and planning food truck operations. Integrating local events, transit arrivals, and historical pedestrian patterns could further improve the recommendations.
