Pivotal Data Science Transport Demo

Technical Information

This demo predicts the duration of currently active road traffic incidents in London. Incidents are unscheduled disruptions caused by collisions, surface damage, burst water mains etc, and do not include planned roadworks or other scheduled disruptions.

The data for this demo is taken from the Transport for London (TfL) Traffic Information Management Service feed. Additional weather data for London is provided by the Weather Underground API.

All the data is stored in a Pivotal Greenplum Database. The analysis and modelling is run using MADlib, Python and Scikit-Learn every time the feed is updated. The graphs are produced using NVD3 and D3. This website is running on the Pivotal CF hosted instance of Cloud Foundry.

Process

What next?

Head back to the predictions, analysis or details of the models.

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Created by Ian Huston | Twitter | LinkedIn | Website

Thank you to the whole Data Science team for their help in producing this demo, especially Noelle and Vatsan.