How big data is driving more intelligent transport

Jim Scott considers the potential of big data to make our journeys a little bit smoother

Throughout history, one of humanity's great preoccupations has been getting from A to B. From horses, railways and boats to cars and planes, we've continuously developed new methods of transport to do so more quickly. However, none of them ever seem to get us to our destination fast enough.

But big data could change all this. Building systems that can recognise traffic flows and respond quickly without human intervention could dramatically improve the transport experience.

Data-driven traffic lights

Consider the humble traffic light. Not only does it keep us safe, but also it gets us where we're going by directing traffic flow in an orderly way. However, as any driver knows, people aren't quite as orderly. Many traffic light systems are programmed in isolation according to an engineer's expectation of "normal" traffic. But a sporting event or concert can massively disrupt this, with fans stuck in bumper-to-bumper traffic.

A big data-driven system could eliminate such headaches. By monitoring the flow of traffic, adjustments could be made based on real-time conditions. In low traffic, for instance, lights could be set using standard timings, while in heavier traffic lights could be set to stay green longer. This type of synchronised traffic light system is already in use in Los Angeles to improve traffic flow and minimise congestion. Meanwhile, in Boston city officials combine street camera footage with data from Waze and Uber in order to ease traffic congestion.

Connected cars as a data source

Connected cars are dramatically changing the automotive industry. By 2020, it's estimated that 90 per cent of cars will be connected to the Internet, compared to just 10 per cent in 2012. These cars can provide a steady stream of data on vehicle and engine behaviour, which can be sent directly to mechanics as part of a vehicle manufacturer's preventative maintenance programme.

These connected cars could feed into smart traffic control systems. By querying the car, the system could tell if the engine is idle, accelerating or braking repeatedly. If all these things are occurring in a short time frame, the system would recognise the vehicle is stuck in a traffic jam and adjust the traffic light timings accordingly. These changes could occur without needing an engineer to monitor the road and estimate the optimal light timings. Instead, traffic control systems could be automatically adjusted in real-time.

Better planning through pollution monitoring

Keeping the air as clean as possible is of massive importance. Some environmental authorities have installed sensors to help track air quality in certain areas. While these air quality monitors can provide insight into what is going on in the atmosphere, like traffic lights, they are isolated from one another. By bringing together this information, a big data system could track the most heavily polluted areas to flag officials to take action.

How big data is driving more intelligent transport

Jim Scott considers the potential of big data to make our journeys a little bit smoother

When there is a high level of pollution in one particular area, it often means that a lot of people are driving, which may indicate that the area isn't served by enough public transport options. Alternatively, high pollution levels may be the result of congestion due to inadequate road infrastructure. This information can be used to help convince local governments to upgrade their roads or for bus companies to add additional routes.

Spotting patterns

From traffic light changes to pollution monitors, the advantage of big data technologies is the ability to sift through billions of data points and spot trends more easily and efficiently than humans.

Transport planners can leverage big data to provide a more comprehensive view of entire transportation systems, including trains, buses, light rail, cars and even pedestrians. And by seeing who's going where and by which method, they'll be able to plan for greater capacity, and tell when more people could be using public transport. On days when particularly high levels of pollution are detected, they could reduce bus and train fares to encourage their use. Alternatively, if a specific route is choked with commuters, they could analyse car data to determine common routes and propose alternate transit systems or arteries to help relieve some of the traffic load.

Or, like in the city of San Diego, agencies could use big data to track who is using smart cards on its light rail system and buses to help reduce fare evasion and understand passenger routes. This data could then help city planners know exactly where to build a new road or transit stop, while also protecting commuters' interests by ensuring that new transit projects are determined by the actual need shown by the data, rather than political pull.

Conclusion

Big data has the potential to make our experience on the roads much easier and faster by consolidating traffic pattern data, vehicle data, pollution data and other data sources into a converged view. Thanks to the Internet of Things' transformation of the automotive industry, and the ability to improve traffic management with real-time data analytics, transport planners and automotive manufacturers alike have the tools to dramatically improve our transport experience.

Jim Scott is director of enterprise strategy and architecture at MapR Technologies

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