Smart city traffic tech revenues will more than double in next four years

New research by Juniper Research has found that smart city traffic technology solutions deployed to ease chronic congestion in cities will generate $4.4 billion in revenue in 2023, up from $2 billion in 2019. 

These solutions typically use sensors in combination with machine learning software algorithms to dynamically alter traffic light phasing according to traffic levels; smoothing urban traffic flows. Juniper’s new research found that these technology-driven traffic solutions, which lower the emissions footprint of cities, will save the equivalent of over 780 billion passenger vehicle miles’ worth of greenhouse gas emissions over the forecast period. 

The research group identified North America alongside Far East and China as major investment regions; driven by strong prevalence for technology deployment over policy-driven solutions to lower traffic congestion. Juniper examined the impact of city traffic innovation on citizens in terms of policy and investment direction, technology impact, agency cohesion and traffic management impact on city air quality. In this context, the leading cities across the globe are Barcelona, San Francisco, Singapore, London, and Portland, Oregon.

Barcelona leads the rankings on account of its investment into smart traffic solutions, electric vehicle charging infrastructure and policy, aimed at improving air quality and lowering private vehicle use. While San Francisco shows strong policy and innovation, the lack of regulation in regard to ride-hailing services has damaged its overall performance, according to Juniper.

The research found that while ride-hailing services are widely blamed for increased congestion, these same companies have an opportunity to capitalize on the road towards future MaaS (Mobility-as-Service) deployments.

“Entities such as Didi capture vast amounts of data in regard to congestion, traffic and passenger flows,” explained research author Steffen Sorrell. “Analysis of this data will be fundamentally useful in optimizing the MaaS traveling salesman problem, and provides an opportunity for smart city data monetization.”