Using 120 Billion IoT Sensor Readings to Improve London’s Traffic for TfL

Transport for London (TfL) wanted to explore innovative uses for traffic data to find new ways to reduce road congestion. To make this happen, our team developed a Deep Learning model capable of accurately predicting traffic conditions 40 minutes into the future.

Our Impact

Gathered insightful data from 120 billion different data points

Created an impactful LSTM prediction model

Developed a model capable of accurate traffic predictions 40 minutes into the future

The Challenge

Designed to gather data from all over London, TfL’s Urban Traffic Control (UTC) system collects car activity records via 14,000 individual road sensors located throughout the city. Datatonic was given 3-months worth of these records to create our traffic prediction model — totalling over 120 billion data points.

The Solution

We began by building a live visualisation of driver activity in the city, converting TfL’s raw sensor data into common traffic engineering metrics — occupancy and flow, and using them to infer the volume, frequency, and location of traffic throughout the city, at any given time.

Next, we designed a Deep Learning model capable of accurately predicting traffic conditions 40 minutes into the future. We did this by identifying the traffic conditions associated with congestion and using Machine Learning to understand and identify patterns in the vast dataset.

In the end, we built a robust model to predict road congestion independent of road network layout, enabling proactive identification (and potentially prevention) of congestion, quickly respond to road incidents, and better coordinate the flow of rush-hour traffic on a daily basis — with the potential of saving countless travel hours for drivers all over London!

For more details on our solution, check out our blog post about the project.

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