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Reducing Traffic Through Congestion Pricing Powered by AI in the U.S.

Researchers have proposed tackling both urban traffic jams and economic inequality by using big data and Artificial Intelligence (AI) to tweak congestion pricing on toll roads. Some cities have used congestion pricing for years, but its widespread adoption could make commutes worse for low-income drivers who cannot afford to pay to use the faster roads. The researchers suggested refunding some of the money from highway tolls in a way that ensures equity; this way, lower-income drivers would get more money back than their more affluent counterparts.

A team of researchers, primarily at Stanford’s Autonomous Systems Laboratory, has proposed an approach that could improve economic equity while reducing congestion. The key idea: Refund tolls in a way that redistributes some of the money from rich to poor and ensures all drivers are as well off or better than they were before.

Lower-income drivers would get back more money than they payout. Wealthier drivers might get some money back, but much or most of their compensation would be in the form of time they don’t waste in traffic jams. That’s a potentially significant compensation because affluent people often pay more to save time.

We can achieve both the goals of equity and efficiency by enabling people to trade time for money, These advances have enabled us to design better autonomous vehicles by learning patterns in the behaviour of human drivers. These tools can play a similar role here in calibrating the pricing models to make our proposed schemes work.

– Devansh Jalota, doctoral candidate, Stanford University Autonomous Systems Laboratory

The idea has two objectives: making sure no one ends up paying more than before, after accounting for the financial benefits of saving time in traffic — and designing a system that improves equity outcomes for lower-income drivers. Those drivers, especially those who choose slower or less direct routes, would get back more than they have paid. The wealthier drivers would get lower refunds, and some would not get refunds at all.

The researchers explained that this approach will require a massive amount of data and computation; plus, each city would need its pricing scheme and specific traffic behavioural model. Moreover, travellers in different cities value money and time differently, so Jalota said the next step would require understanding city-specific driver behaviour and parameters for refunds and redistribution.

Marco Pavone, a professor of aeronautics and astronautics and director of the Stanford Autonomous Systems Laboratory, said the result should not only reduce congestion but also put more of the toll revenue toward achieving any city’s equity goals.

As reported by OpenGov Asia, a new report showed that Artificial Intelligence (AI) has reached a critical turning point in its evolution. Substantial advances in language processing, computer vision and pattern recognition mean that AI is touching people’s lives daily—from helping people to choose a movie to aid in medical diagnoses.

With that success, however, comes a renewed urgency to understand and mitigate the risks and downsides of AI-driven systems, such as algorithmic discrimination or the use of AI for deliberate deception. Computer scientists must work with experts in the social sciences and law to assure that the pitfalls of AI are minimised.

The report – Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report – aims to monitor the progress of AI and guide its future development. This new report, the second to be released by the AI100 project, assesses developments in AI between 2016 and 2021.

In terms of AI advances, the panel noted substantial progress across subfields of AI, including speech and language processing, computer vision and other areas. Much of this progress has been driven by advances in machine learning techniques, particularly deep learning systems, which have leapt in recent years from the academic setting to everyday applications.

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