1. The Statistics of Cycling Matthew Arnold Data From Sustrans, a charity that promotes sustainable transport in the UK Responsible for planning and delivering the National Cycle Network 107 automatic counters - #bikes per hour, many operating for over 5 years

2. The Statistics of Cycling Matthew Arnold Usage profiles

3. The Statistics of Cycling Matthew Arnold Clustering Try to find common shapes. How do we assess dissimilarity? Hierarchical clustering

4. The Statistics of Cycling Matthew Arnold Result 4 shapes Schools Commuter Leisure Hybrid

5. The Statistics of Cycling Matthew Arnold Clustering

6. The Statistics of Cycling Matthew Arnold Relate to explanatory variables Responses to Sustrans information about the locality of a counter Baseline category logit model to “predict” classification. Response probabilities

7. The Statistics of Cycling Matthew Arnold Example classification ~ Trafficfreeroute + Lessthan3miles + Lightingno commuter hybrid leisure schools 0.06165034 0.35577006 0.50244144 0.08013816

8. The Statistics of Cycling Matthew Arnold Conclusions Confirmed Sustrans notion of 4 types of usage profiles using data-driven methods. Examined relationship between usage at a counter and the locality of the counter. Experienced problems due to limited data.