In a true smart city, traffic flows smoothly, free of congestion. However, intelligent transport planning requires a thorough analysis of the current state of traffic flow. This is where mobile network data can help.
Another day. Another traffic jam. For whatever reason, traffic on this two-lane road in the center of town has ground to a halt – an all-too-familiar occurrence in big cities. But this could be set to change. Intelligent transport planning is now at the very top of the agenda for cities and conurbations looking to relieve congestion.
In fact, many are already attempting to calibrate traffic lights for maximum efficiency. "Current technology allows us to manage traffic through detectors installed in roads," says Martin Schmotz from the Institute of Transport Planning and Road Traffic at TU Dresden (Hall 6, Stand B24) . Intelligent, adaptable management goes one step further. Machine learning algorithms analyze historical traffic data and look to identify patterns within it. "Algorithms can interpret the data to establish short-term forecasts and anticipate future situations," explains Schmotz.
The challenge lies in capturing the data needed to make the analyses. "Collecting data on traffic flow that includes all modes of transport across a city is a complex process," states Dr. Anette Weisbecker, Deputy Director of Fraunhofer IAO (Hall 6, Stand B36) . Current transport planning relies largely on manual recordings from surveys. However, these are expensive and time consuming.
Fraunhofer IAO and telecommunications provider Telefónica (Hall 12, Stand D49) have joined forces to propose an alternative. Together, they have been evaluating mobile network data from Telefónica customers in specific locations and comparing it against traffic flow. Describing the findings of the pilot study in Stuttgart, Germany, Anette Weisbecker revealed: "The data is available in high temporal and spatial resolution and provides new insights into the factors that influence urban transport."
The captured data is made anonymous before analysis to remove any association to the customer. However, it does reveal which means of transport participants use and which routes they take. It also includes input from pedestrians and cyclists.
"We're facing significant challenges in transport planning – especially in urban areas. Used correctly, digital solutions could make a real difference – and in a way that benefits everybody. This calls for us to leverage relevant data, so we're thrilled that a leading research institute has recognized the potential of mobile networks. It gives us the impetus to run further projects based on the smart analysis of anonymous data," says Florian Marquart, Managing Director and Head of Advanced Data Analytics at Telefónica NEXT.
Analysts are able to draw valuable conclusions from this information. For example, data collected in Stuttgart showed that the city experiences large volumes of commuter traffic, divided between road and S-Bahn lines (Germany’s commuter trains). This information can support studies into the influence of events and other external factors. An analysis of data captured during the Cannstatter Frühlingsfest (similar to Oktoberfest but takes place in Stuttgart) and the Stuttgart Weindorf (an annual wine festival) highlights the impact of such festivals on traffic flow.
The main advantage of mobile network data is that obtaining it does not involve a huge amount of effort. In fact, it is available around the clock and on demand – essentially as a byproduct of mobile networking. This means that, unlike with data captured through sensors, no additional infrastructure is required.
In the short term, data from mobile network providers will assist in testing and improving existing traffic models. In the medium term, special algorithms and models will help planners design better traffic flow systems and gain new insights – on public transport, for example. “Mobile network data would make it possible for us to receive continuous updates on general transport demand. We could use this information to supplement our own manual traffic surveys, which we carry out less frequently. This would allow us to reduce costs on occasional surveys – during certain events, for instance,” says Thomas Hachenberger, CEO of the Stuttgart Transit and Tariff Association.
The evaluation will have no impact on mobile network users – other than potentially improving their journey to work.
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