Pivotal Data Science Transport Demo

This demo predicts the duration of unexpected road traffic incidents in London.

These 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. The data is stored in a Pivotal Greenplum Database and the models are predicted using a combination of SQL, MADlib and Python routines. This website is running on the Pivotal CF hosted instance of Cloud Foundry.

Find out more on the Analysis, Models and Technology pages.

Active Incidents and Predicted Durations

Transport for London Data Predictions
Start Time Location of Incident # Streets Affected Type of Incident Total Duration Time Remaining

Map of current incidents

What next?

Learn more about the traffic incidents and how these predictions were created in the Analysis and Models sections.

What technology powers these predictions? Find out in the Technology section.

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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.