MUSA-5080-Fall-2025

HW5: Your Turn!

For Homework 5, you’ll work either all byyyy yourself or in teams of 2 to:

Part 1: Replicate with Different Quarter (alternately, you could do a longer time-span by merging multiple quarters together. I’m not picky about that)

1. Download data for Q2, Q3, or Q4 2024 from: https://www.rideindego.com/about/data/

2. Adapt this code to work with your quarter:

   • Update date ranges for weather data
   • Check for any data structure changes
   • Create the same 5 models
   • Calculate MAE for each model

3. Compare results to Q1 2025:

   • How do MAE values compare? Why might they differ?
   • Are temporal patterns different (e.g., summer vs. winter)?
   • Which features are most important in your quarter?

Part 2: Error Analysis

Analyze your model’s errors in detail:

1. Spatial patterns:

   • Create error maps
   • Identify neighborhoods with high errors
   • Hypothesize why (missing features? different demand patterns?)

2. Temporal patterns:

   • When are errors highest?
   • Do certain hours/days have systematic under/over-prediction?
   • Are there seasonal patterns?

3. Demographic patterns:

   • Relate errors to census characteristics
   • Are certain communities systematically harder to predict?
   • What are the equity implications?

Part 3: Feature Engineering & model improvement

Based on your error analysis, add 2-3 NEW features to improve the model:

Potential features to consider:

Temporal features:

• Holiday indicators (Memorial Day, July 4th, Labor Day)
• School calendar (Penn, Drexel, Temple in session?)
• Special events (concerts, sports games, conventions)
• Day of month (payday effects?)

Weather features:

• Feels-like temperature (wind chill/heat index)
• “Perfect biking weather” indicator (60-75°F, no rain)
• Precipitation forecast (not just current)
• Weekend + nice weather interaction

Spatial features:

• Distance to Center City
• Distance to nearest university
• Distance to nearest park
• Points of interest nearby (restaurants, offices, bars)
• Station capacity
• Bike lane connectivity

Trip history features:

• Rolling 7-day average demand
• Same hour last week
• Station “type” clustering (residential, commercial, tourist)

Implementation:

• Add your features to the best model
• Compare MAE before and after
• Explain why you chose these features
• Did they improve predictions? Where?

Try a poisson model for count data

• Does this improve model fit?

Part 4: Critical Reflection

Write 1-2 paragraphs addressing:

1. Operational implications:
   • Is your final MAE “good enough” for Indego to use?
   • When do prediction errors cause problems for rebalancing?
   • Would you recommend deploying this system? Under what conditions?
2. Equity considerations:
   • Do prediction errors disproportionately affect certain neighborhoods?
   • Could this system worsen existing disparities in bike access?
   • What safeguards would you recommend?
3. Model limitations:
   • What patterns is your model missing?
   • What assumptions might not hold in real deployment?
   • How would you improve this with more time/data?

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Submission Requirements

What to Submit (per team)

1. qmd file with all your code (commented!)
2. HTML output with results and visualizations

3. Brief report summarizing (with supporting data & visualization):

   • Your quarter and why you chose it
   • Model comparison results
   • Error analysis insights
   • New features you added and why
   • Critical reflection on deployment
4. You only need to submitted once as a team (one submit the link to your portfolio on Canvas)

Tips for Success

1. Start early - data download and processing takes time
2. Work together - pair programming is your friend
3. Test incrementally - don’t wait until the end to run code
4. Document everything - explain your choices
5. Be creative - the best features come from understanding Philly!
6. Think critically - technical sophistication isn’t enough

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