Not all AI aims are the same. Traffic managers and drivers alike are becoming accustomed to ever-more advanced traffic planning tools, often now with AI architecture. But what happens when the aims of the road operator conflict with the aims of the individual? Professor Nick Reed, founder and CEO of Reed Mobility gives his insight.
It is uncontroversial to state that congestion is bad. Delay, inefficiency, frustration, cost, missed appointments, obstructed emergency services – just some of the negative externalities when demand for road space exceeds supply. Consequently, there are strong incentives to ‘solve’ congestion, but its causes may be simple such as roadworks blocking a lane, or complex – emerging from a mixture of demand, environmental, infrastructure, and human factors that contribute to flow breakdown.
Fortunately, recent years have seen the emergence of a technology that is able to take varied inputs and determine optimal outputs in response. The capabilities of AI are increasingly being recruited to tackle congestion. Traffic signals are dynamically adjusted to respond to flow, predictive models anticipate traffic surges and incidents, while data from sensors, cameras and connected vehicles is being fed into powerful AI algorithms tasked with squeezing every drop of efficiency from the network. The goal for road authorities is clear: optimize the system for collective good.
Drivers also have support from AI assistants. Navigation apps optimize routes not only based on road layout but also integrate live traffic data and dynamically adjust for real-time events to minimize delay. These tools are popular for individuals and fleets because they help users maintain progress, avoid delays, and maximize journey reliability.
However, the goals of the road authority and the individual may not always align. The public authority manages traffic across a city or region, striving for fairness, air quality, and reduced delays at a system level. An individual driver follows decisions made by their device, optimizing only for its user, which may conflict with societal objectives.
“AI systems at the city level may struggle to respond to shifting flows triggered by app-driven rerouting”
This divergence can cause side effects. A recommended route might push traffic onto residential streets not designed for such use, creating new bottlenecks and risks. Meanwhile, AI systems at the city level may struggle to respond to shifting flows triggered by app-driven rerouting. The collective system is constantly chasing a moving target.
From a human factors perspective, drivers can over-trust navigation guidance. Even when a recommendation seems dubious, they may follow it regardless. Conversely, authorities may struggle to convince the public of the value of routes chosen for the collective good.
One answer lies in collaboration over competition. Road authorities can share objectives and data with navigation providers. In turn, navigation systems can inform public agencies how they develop and adapt guidance. This coordination can address traffic congestion, safety, efficiency, and air quality.
Ultimately, traffic management in the age of AI is about aligning digital intentions with real-world impact.
This column first appeared in the August 2025 edition of TTi magazine
