Updated 12 October 2026 · By Pete Bromfield, former Head of Maths with IB examining experience

How much does rain reduce bike-share use?

AI SLAI HL Solid Also in: Statistics

Research question

How are daily journeys on London's cycle-hire scheme related to temperature and rainfall, and is the effect of rain significant after allowing for temperature and weekends?

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Why it makes a good exploration

Transport planners need to know how weather affects demand. TfL publishes every hire journey, so you can build a weather model with a huge, real dataset.

The mathematics you'll need

Regression and correlation
Comparing groups (rainy vs dry days) with t-tests
Controlling for a variable by splitting the data
Spearman's rank for non-linear effects

The statistics, step by step

Worked with every number shown, with what examiners look for and the common mistakes: Spearman's rank correlation · t-test: two-sample and paired · Pearson's correlation and regression. Then run the same steps on your own data in Analyse my data, or start from the statistics workflow.

Where the data comes from

Daily journey counts from TfL open data and daily weather from the Met Office or a local weather archive.

Transport for London open data — Timetables, station entry/exit counts, cycle-hire journeys and air quality for London.
Met Office historic station data — Monthly max/min temperature, rainfall, frost days and sunshine for 37 UK stations, some from 1853.

Real data for this idea

Primary source: Transport for London open data: Cycle hire usage data. The usage-stats files list every hire; total the journeys per day. Licence: Check TfL's open data terms; attribution is required.
Also useful: Met Office historic station data: Historic station data. Choose the station nearest you; the table gives monthly maximum and minimum temperature, rainfall and sun hours. Licence: Check the terms on the Met Office page.
Optional tool: Historical weather API. Daily weather for any location: a quick way to get daily values the monthly station tables lack. Data CC BY 4.0; the free service is for non-commercial use only.

Links checked October 2026; we link to the data and never copy it. More free data sets.

A possible outline

Aggregate journeys by day.
Explore temperature and rain separately.
Compare rainy and dry days within similar temperatures.
Consider weekday vs weekend.
Reflect on strikes, events and seasonal effects.

Pitfalls that cost marks

Mixing weekdays and weekends.
Defining a rainy day inconsistently.
Ignoring outliers from tube strikes.

Showing personal engagement

Compare with your own cycling habits.
Choose a question for a city you know.
Predict demand for tomorrow's forecast and check.

See Criterion C: personal engagement for what examiners look for.

Which course is it for?

Course	Fit	Maths to lean on
AA SL	Not a natural fit	The core technique sits in the AI course or at HL; an AA SL student could use it only as clearly explained new mathematics.
AA HL	Not a natural fit	The mathematics is mainly from the AI course; at AA HL the exploration would need an AA-level approach (calculus, proof or probability theory) to reach the top of Criterion E.
AI SL	Good fit	Regression and correlation; Comparing groups (rainy vs dry days) with t-tests
AI HL	Good fit	Regression and correlation; Comparing groups (rainy vs dry days) with t-tests

Level: Solid. Needs some independent work beyond class examples. See how the IA differs between AA and AI, SL and HL.

How this idea reaches the top bands

Reflection (D)

For this idea, start with the weak points: mixing weekdays and weekends; and defining a rainy day inconsistently. Say how each one affects your answer.

Use of mathematics (E)

SL: regression and correlation; comparing groups (rainy vs dry days) with t-tests; controlling for a variable by splitting the data; spearman's rank for non-linear effects, each explained in your own words and carried out correctly.

How environment explorations reach the top bands in general: Environment & climate IA ideas. Guides: A · B · C · D · E · self-grade sheet.

Taking it further

Model journeys with both temperature and rain in a multiple regression (explain the new maths).

Extending it for HL

Add an HL technique the question genuinely needs: what an HL environment exploration adds.

See a complete IA, marked

When is the sea warm enough for our swimming club? A sinusoidal model of sea temperature (AI SL) is a complete environment exploration, marked on every criterion.

Frequently asked questions

Which IB Maths course is this IA idea for?

It works for AI SL and AI HL. We rate it Solid: it needs some independent work beyond class examples.

What mathematics does this IA use?

Regression and correlation, comparing groups (rainy vs dry days) with t-tests, controlling for a variable by splitting the data and spearman's rank for non-linear effects.

Where can I get data for this IA?

Daily journey counts from TfL open data and daily weather from the Met Office or a local weather archive. Checked sources and tools: Transport for London open data and Met Office historic station data.

Turn this idea into your IA

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