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Data

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. Usage profiles. Clustering. Try to find common shapes .

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Data

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  1. 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. Usage profiles

  3. Clustering • Try to find common shapes. • How do we assess dissimilarity? • Hierarchical clustering

  4. Result • 4 shapes • Schools • Commuter • Leisure • Hybrid

  5. Clustering

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

  7. Example classification ~ Trafficfreeroute + Lessthan3miles + Lightingno commuter hybrid leisure schools 0.06165034 0.35577006 0.50244144 0.08013816

  8. 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.

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