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Less traffic, better infrastructure: How urban mobility will advance

As prediction technology advances and algorithms are refined, smart cities worldwide are set to make big strides with improving urban mobility in the years to come. Radim Cmar, Solution Architect for Smart Cities at navigation app Sygic, looks ahead to how things could develop.

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About 2.5 billion more people will be living in cities by 2050, according to a recent UN report. That means that while today 55 percent of the world’s population lives in urban areas, this number is expected to grow to 68 percent by the middle of the century. Further, the report predicts that the world will have 43 megacities by 2030 - that is, cities with more than 10 million inhabitants. It’s an increase from the 31 we have today.

 

This influx will strain urban areas, says the UN, noting that countries will be challenged to meet the needs of their growing populations. And yes, this includes mobility needs. While traffic already plagues many cities worldwide, it could get a lot worse in the years to come.

 

But hopefully we won’t get to that point. Cities have the opportunity to beat congestion by leveraging big data and smart tech solutions, but also make roads safer and improve city processes with the data they hold. It’s already happening in cities like London and Stockholm. As prediction technology advances and algorithms are refined, smart cities worldwide are set to make big strides in the years to come.

 

Here’s how we should expect mobility to advance:

 

GPS technology could warn us about dangerous vehicles on the road

Car accidents occur every day, and many of them are fatal. According to the National Safety Council, more than 40,000 people died in US traffic incidents in 2017 - with distracted driving and higher speed limits often to blame. Today, GPS technology shows us where accidents have occurred - but what if in the future, it could help us to avoid accidents all together?

 

It is a possibility. If safety detectors are embedded into cars, people could then be alerted of their own dangerous driving and the fact they’re putting others at risk. When combined with GPS technology, other drivers on the road could also be alerted if there’s a dangerous car nearby, and be warned to stay away.

 

However, a few things need to happen before this becomes a reality. For one, car manufacturers need to embed these safety detectors on a major scale. Secondly, a 5G network would need to be developed so that the GPS systems could communicate with other cars on the road.

 

Not only would a solution like this help to save lives, it would also work to reduce traffic jams in cities as populations rise. With less accidents, traffic can operate at a more normal pace.

 

Predicting traffic jams with multiple data sources

 

It’s common for GPS applications to show traffic state and predictions, usually represented by either green, orange or red lines to communicate where there’s congestion, where there will be, or where there is not. These are calculated by navigation companies after collecting statistical information about the average speed of drivers, called speed profiles.

 

Speed profile data forms only a first-generation prediction model, though, and it breaks down in the event of a traffic incident, road blocking, or significant weather change. Precise prediction, on the other hand, requires more fine-grained data, mined from local sources. That means observing how various events, such as accidents, have historically affected roadways and congestion levels on a city scale. But it also entails using data from existing road sensors, camera systems, public transportation vehicles’ positions, and meteo data. The solution’s algorithm is then refined using this data to make robust predictions.

 

Cities like Madrid, for example, heavily invest in road monitoring technologies, and their traffic control centres aim to cover nearly 100% of incident detections. The city’s partnered with technology company Kapsch to develop 100+ traffic counting stations equipped with artificial vision sensors, as well as stations for pedestrians and cyclists. The information collected can help in the search of a suitable traffic mitigation scheme.

 

While the solution in Madrid is primarily deployed for monitoring and analysis purposes, one can see the potential there for a powerful, traffic prediction solution for drivers.

 

Using data to improve city systems and infrastructure

Intelligent algorithms in combination with proper data can help cities to predict and reduce traffic, as well as keep drivers safe. But intelligent algorithms can also convert input data into other valuable data sets. In having prediction data available in real-time, cities would have the power to immediately mitigate upcoming congestion by adapting traffic signals, for example.

 

The benefit for cities would be more than just reduced traffic. The data produced from smart, data driven solutions could be used to further improve city infrastructure and services. Think changes to transportation infrastructure - such as building a roundabout at an accident-prone intersection, or planning construction projects at times that least affect drivers during the day.

 

New York City, for example, partnered with Datakind back in 2015. The non-profit provides data science services to social causes, and has been working on building a tool to see how engineering impacts the safety of New York streets. The goal? To see how the street widths, crash rates, signal timings, and bus and bike lanes, etc. have an impact on crash rates. The model is still being adjusted to find the ‘holy grail’.

 

Making it all work together

 

Despite all the possibilities data holds, smart cities everywhere still need to ensure they leverage the data correctly. For one, they need to have a long-term vision of how these solutions will work simultaneously to improve the lives of citizens. And secondly, they need to ensure they – and not a tech company – own the data being created.

 

If cities leave solutions to develop randomly, tech companies looking to fulfill their own agenda will end up prevailing. Think of it like this: a city might aim to reduce cars, but Uber drivers flooding the roads certainly won’t fix the problem, either.

 

Urban areas will certainly feel strained as populations rise but isn’t that what technology is meant to combat? Cities that implement AI and smart tech solutions will certainly improve mobility within their walls. However, in leveraging the data obtained from these solutions, they’ll also be empowered to develop the right infrastructure – and implement the right service – to improve mobility for the next 50 years to come.

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