‘Smart’ Traffic Management: A Tale of Two Cities

AI-Facilitated Traffic Flow Proficiency: 
One Common Objective. Two Very Different Methods.

It’s one of the most common and maddening moments of modern life for city dwellers  everywhere: sitting in a car, staring at a red light, while absolutely no traffic crosses the empty intersection.

For over a century, the traffic signal has been a “dumb” and unthinking tyrant, dictating the flow of our cities with simple timers and rudimentary sensors. It’s cost time and fuel, and tested patience.

But progressively-minded cities are increasingly relegating this rigid, one-size-fits-all approach to the pages of analogue history. Forward-thinking traffic management engineers and executives have been asking: What if that light wasn't so dumb? What if it could see the approaching traffic, communicate with the next intersection, and make intelligent decisions in real time?

And some have been leading the way in making that a reality, deploying Artificial Intelligence to transform their traffic control systems from mindless, timer-based apparatuses into responsive, thinking networks.

Two case-studied cities and their two very different approaches within this overarching new paradigm are the subject of this deep-dive: Singapore, and Pittsburgh in the United States.

Their goal has been the same in each case: untangle gridlock and create smarter, more efficient urban arteries. Their methodology and its implementation, however, has not.

This is a tale of two fundamentally different philosophies not just of technology, but of strategic execution: Pittsburgh’s model of rapid, university-born innovation versus Singapore’s masterclass in long-term, state-driven evolution.

Singapore ‘GLIDES’ Into ‘Top-Down’ AI-Facilitated Future for Traffic Management

Singapore’s Land Transport Authority (LTA) has opted to deploy Artificial Intelligence-assisted traffic management according to what might be described as a “top-down” approach.

Key in the masterminding and roll-out of its new AI-driven “GLIDE” (Green Link Determining System) has been a priority focus on system-wide benefits, as opposed to percentage point improvements in any single metric. The system is designed to improve the efficiency and reliability of the entire road network.

It's about making journey times more predictable for everyone by optimising efficiencies in a holistic sense. 

GLIDE’s logic prioritises, for instance, the smooth, predictable flow of the overall system over achieving the absolute fastest travel time on one specific road if that speed comes at the cost of wider network disruption.

To this end, the centralised command-and-control nature of GLIDE is foundational to its role and function within the much larger, nation-wide "Intelligent Transport Systems" (ITS) framework of this densely-populated, operationally efficient city-state, renowned for its long-term planning.

While the foundations of Singapore's system aren't freshly-minted, their relevance today is more potent than ever. In the age of AI, its two-decade head start has created an unmatched strategic asset: a vast trove of historical traffic data. This "data moat" is the fuel for today's sophisticated predictive AI, allowing the city-state to model and manage its network with a maturity other cities can only study. The story of GLIDE, therefore, is less about a single piece of technology and more about the powerful lesson it offers in long-term governmental vision and data governance.

A Strategy of Total Information Awareness

The operational concept behind GLIDE is to achieve network equilibrium.

In this centralised model, a single core processing system at the LTA's Intelligent Transport Systems Centre creates a complete, real-time digital picture of the road network, allowing it to manage traffic proactively rather than reactively. Analogically-speaking, the system acts as a single, all-seeing air traffic controller for the city's entire road network.

The methodology is one of total information awareness. Data from thousands of sensors embedded in the road surface, which detect real-time traffic volume and speed, is fed continuously into this central command hub.

(Source:  ITS International, https://www.itsinternational.com/features/singapore-continues-its-glide-path-smarter-traffic-management.)

GLIDE’s central computational engine analyses a network-wide traffic model, conducting real-time traffic modelling and predictive analyses, to optimise the phasing and timing of traffic signals for the entire network. It then makes strategic adjustments to signal timings to ensure the smoothest possible flow across the entire island.

The goal isn't simply to clear one congested intersection; it's to create the most efficient state for the system as a whole.

Integrated Public Service Capabilities

One of the most significant advantages of this centralised model is its ability to integrate with other public services.

The “holistic” philosophy extends to actively supporting public transport, where giving a bus slight priority can improve the reliability of the entire service, encouraging its use and ultimately reducing total vehicle volume.

GLIDE achieves this through its EMTRAC (Emergency Vehicle and Bus Priority) component system, which can also create uninterrupted passage for emergency vehicles responding to a call.

(Source: Land Transport Authority of Singapore, https://www.lta.gov.sg/content/ltagov/en/industry_innovations/technologies/intelligent_transport_systems.html.)

This top-down control also allows traffic managers to dynamically respond to major incidents, like a highway accident, by proactively re-routing traffic and adjusting signal patterns city-wide to prevent gridlock. It is a strategy of total command-and-control, designed for holistic optimisation and deep integration with a city's operational needs.

Pittsburgh's 'Bottom-Up' Approach

If Singapore's approach is one of a single, central digital commander, Pittsburgh has championed an almost opposite philosophy: a "bottom-up" revolution in traffic control, where the system acts less like a single commander and more like a co-operative team.

The city’s “Surtrac” (Scalable Urban Traffic Control) system, born out of Pittsburgh’s own world-renowned Carnegie Mellon University, shuns the “centralised, city-wide brain” concept in favour of making each individual intersection “smart”. It's a strategy built on the principles of localised intelligence and adaptive co-operation, whereby collective intelligence emerges from simple, local interactions.

Each Surtrac-equipped intersection acts as its own autonomous decision-maker. Using real-time data from its own radar and video feeds, the local AI computer analyses the approaching traffic flow. 

Second-by-Second Predictive Modeling for Optimal Signal Sequence

The ingenious component is how it then develops the optimal plan: using a second-by-second predictive model, it chooses the signal sequence that will result in the minimum aggregate delay for all vehicles it can see. Crucially, it then communicates this plan to its immediate neighbours. It effectively tells the next light down the road, "I'm sending a platoon of cars your way in 15 seconds," creating seamless "green waves" without direction from any central authority. (Source: Rapid Flow Technologies, https://www.rapidflowtech.com/surtrac.)

The results of this localised, adaptive strategy have been significant. The pioneering pilot projects, with results first published in 2012, recorded a more than 25 percent reduction in travel time, a decrease in idling time at lights by an average of 40 percent, and a corresponding 21 percent decrease in vehicle emissions. (Source:  Carnegie Mellon University, https://www.cmu.edu/news/stories/archives/2012/july/july9_trafficsignal.html.)

Indeed, these projects provided such a successful proof-of-concept in demonstrating that a de-centralised approach could offer a nimble and highly scalable alternative to a massive, top-down overhaul, that the Surtrac technology has since been commercialised and is being deployed in other cities across North America.