Taxi Fare Predictor Game

Educational simulation: Every passenger, route, city zone, and fare is fictional. Results are not real taxi quotations.

Score0

Level1

Accuracy0%

Streak0

Coins0

Best error—

Prediction Result

Your latest estimate appears here before the controls.

Ready for a trip

Predicted fare

—

Actual fare

—

Absolute error

—

Percentage error

—

Generate a journey, enter a prediction, and reveal the fictional fare.

Interactive City Trip

Canvas powered by p5.js. Use Start, Pause, Reset, or keyboard controls.

Progress: 0%

Running meter: $0.00

Route: Fastest

Trip Generator and Fare Controls

Change journey conditions, pricing rules, difficulty, routes, and challenge types.

Game Setup

Prediction mode Difficulty Challenge

Sound effects

Reduced animation

High contrast

Journey Conditions

Distance: 8.0 km Duration: 24 min Traffic: Moderate Demand: 1.20×

Context

Pickup time Day Weather Vehicle type

Route and Zones

Route option Pickup zone Drop-off zone

Extra Charges

Waiting time: 2 min Tolls: $0.00

Booking fee

Airport surcharge

Night surcharge

Surge pricing

Pricing Rules

Base fare Per kilometre Per minute Minimum fare

Prediction Entry

Your fare estimate

Display currency

Round time limit

Round timer

Unlimited

Keyboard: G generate, P predict, R reveal, Space start or pause trip.

Performance Dashboard

Interactive Plotly.js charts update after predictions and model training.

Trip History and Exports

Progress is stored in your browser.

#	Trip	Mode	Distance	Duration	Traffic	Demand	Predicted	Actual	Error	Score
No completed predictions yet.

Formula Used

Fare = max(Minimum Fare,
  (Base Fare + Distance Charge + Duration Charge + Waiting + Tolls + Fixed Fees)
  × Traffic Adjustment × Demand Multiplier × Time Adjustment × Weather Adjustment × Vehicle Adjustment
)

How to Use

Generate a fictional trip or adjust every journey condition manually.
Select a prediction mode, route, challenge, difficulty, and pricing rules.
Enter your estimate, use a formula, or train a browser-based model.
Reveal the actual fictional fare and inspect its complete breakdown.
Use charts, history, badges, and error feedback to improve predictions.

Learning Goals and Safety

Skills Practised

Regression thinking and feature selection.
Effects of traffic, demand, time, and route choices.
Absolute error, percentage error, MAE, and R².
Training, testing, overfitting, and model comparison.

Important Limits

All locations, passengers, prices, and journeys are fictional.
No sensitive passenger data is collected or required.
Actual taxi pricing varies by city, provider, law, and contract.
Never use these results as a real fare quotation.