Smart city traffic tech market to hit $2.9bn in 2019


By Dylan Bushell-Embling
Tuesday, 21 May, 2019



Smart city traffic tech market to hit $2.9bn in 2019

Smart city traffic technologies deployed to ease chronic congestion in cites will generate US$2 billion ($2.91 billion) in revenue this year, growing to US$4.4 billion in 2023, Juniper Research has predicted.

Smart traffic technologies typically use sensors and machine learning software algorithms to dynamically adjust traffic light phasing based on real-time traffic levels to smooth urban traffic flows.

As well as easing congestion, the technologies have the potential to lower cities’ emission footprints. Juniper Research predicts that deployments of these traffic solutions will save the equivalent of over 780 billion passenger vehicle miles’ worth of greenhouse gas emissions over the forecast period.

North America and the Far East and China are driving investment in smart city traffic technologies. But in terms of the impact of smart city initiatives and investments on traffic innovation, Barcelona in Spain is leading the way.

Barcelona topped the leaderboard due to its investment in smart traffic solutions, electric vehicle charging infrastructure and favourable policy environment. San Francisco, Singapore, London and Portland round out the top five.

Meanwhile, according to Juniper Research Principal Analyst Steffen Sorrell, interest in the use of technology to optimise traffic flows could present a significant opportunity for ride-hailing companies such as Uber and Didi, which have a chance to carve out a share of future mobility-as-a-service (MaaS) deployments.

“Entities such as Didi capture vast amounts of data in regard to congestion, traffic and passenger flows,” he said.

“Analysis of this data will be fundamentally useful in optimising the MaaS travelling salesman problem and provides an opportunity for smart city data monetisation.”

The travelling salesman problem is a well-known algorithmic problem involving optimising the routes between various cities to find the shortest possible travel time.

Image credit: ©stock.adobe.com/au/Alexey Brin

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