Using big data to solve congestion

Transport

Maps make sense of complex systems

Road networks are complex and require the collection, aggregation, visualisation and analysis of huge and constantly changing data sets. Maps makes understanding the data easier for road users, those charged with keeping the network flowing and people looking to make improvements. Seeing is believing - when policy and decision makers see pictures that make sense, they can make data-driven decisions for the benefit of all road users.

Traffic congestion plagues cities

Congestion contributes to stress levels, pollution, decreased productivity and a huge economic cost. This is only set to get worse as our urban population grows.

Some of the challenges around measuring and modelling traffic flows, road performance, bottlenecks and count are:

  1. The sensors are based on a fixed location so it becomes difficult to see the entire network.
  2. It is expensive to maintain these sensors and during road works, these areas become black spots.
  3. Fleet cars, which are often used for sampling, don’t provide an accurate picture of traffic conditions as they can be skewed depending on sample size, time of day and region.

Transformation through analytics

To transform infrastructure planning we need to identify and target network pinch points so we can increase network capacity.

Keeping road performance up by substantially increasing incident clearance rate keeps traffic flowing. Agencies need to detect incidents and clear them quickly and predict and plan for future traffic conditions using historical analysis and predictive scenario planning.

The ultimate aim is to create a better journey experience for motorists. Accurate information is key to increasing motorist happiness. Instruments such as variable message signs on motorways provide journey times to the nearest exit, letting users make informed decisions, i.e to use public roads or tolls roads.

USING BIG DATA

Google crowdsourced data can dramatically shift the landscape of traffic management by making it more, timely, accurate and cost effective.

Why: The sheer volume of data alone is staggering. We are talking upwards of millions of ‘road sensors’ instead of the traditional static road sensors.

How: Google provides traffic data which is crowdsourced anonymously from the movements of millions of mobile phones. When it comes to the number of road sensors vs the number of mobile phones in a city, the quanta of the sample sizes are  dramatic.

We use Google’s crowdsourced live traffic data to create solutions that help plan for infrastructure investments, delight the road user by giving accurate journey times and use predictive analysis to anticipate network behaviour and reduce congestion. 

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