How to forecast bike dock demand

Q: How to forecast bike dock demand

This question evaluates competency in time-series forecasting and demand prediction, including feature engineering, model selection and evaluation, handling nonstationarity, data leakage, overfitting, and operational failure modes.

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Question

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You operate a shared city-bike system. For a given dock (station), you want to predict demand in the next hour.

Task

Design an approach to predict:

Target: number of bike check-outs (or net bike outflow) from this dock in the next 1 hour .

Data (assume available)

Historical trips: trip_id, start_station_id, end_station_id, start_time, end_time
Station metadata: station_id, lat, lon, capacity
Exogenous signals (optional but common): weather, holidays/events, nearby transit, current dock inventory (bikes available / docks available)

Questions

How would you formulate the prediction problem (regression vs classification vs time series)?
What features would you build (time-based, lagged, seasonality, spatial, inventory constraints, weather)?
What model families would you consider (baselines to advanced), and how would you evaluate them (metrics + train/validation split)?
How would you prevent overfitting in this setting (feature design, regularization, validation strategy, leakage prevention)?
What are key failure modes / edge cases (cold-start stations, special events, missing data, concept drift)?

How to forecast bike dock demand

Quick Overview

Task

Data (assume available)

Questions

Solution

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