Cities worldwide are plagued by traffic congestion, which not only results in lost productivity but also contributes to increased carbon emissions and noise pollution. To address this issue, congestion pricing has been proposed as a potential solution. Congestion pricing entails charging tolls for the use of busy roads to encourage drivers to avoid crowded areas and rush hours. However, the appropriate tolls for efficiently reducing traffic remain a challenge. The collection of user trip attributes such as origins and destinations for this purpose is difficult and raises privacy concerns.
Researchers at Stanford University have developed an innovative approach to optimize road tolls using artificial intelligence. This method involves dynamically adjusting tolls based on the number of cars traveling on certain roads at specific times to balance roadway supply and driver demand. This approach has the potential to improve congestion pricing systems in various cities worldwide.
Without requiring extra user trip information, the researchers used online learning, a branch of machine learning and artificial intelligence, to modify road tolls based on observations of motorist behavior. By optimizing road tolls, our technique protects user privacy while easing traffic congestion. The researchers found that the only data points required to determine the supply and demand for roads are the total number of cars on the road at any given moment, information that is already available in cities thanks to contemporary sensing technology like loop detectors.
Through the independent acts of choosing one road over another, drivers reveal aggregate preferences, enabling congestion pricing tolls to be increased on congested roads, thereby incentivizing travelers to take alternate routes or other modes of transportation. The online learning-based approach modifies tolls based only on observed aggregate flows on the transportation network’s routes at each time period.
To validate the performance of their approach, the researchers compared it to an all-knowing “oracle” with complete information on users’ trip attributes. Testing the new approach on real-world traffic networks, the researchers observed that it outperformed even several traditional congestion pricing methods.
This research builds on previous work by the lead author and his colleagues, focused on ensuring equity of congestion pricing. That study proposed a redistributive approach where lower-income drivers receive more money back than they pay out in tolls, while wealthier drivers’ compensation is mostly in the form of time not spent in traffic jams.
Moving forward, the researchers aim to combine the equitable approach to congestion pricing developed in the 2021 paper with the learning-based approach used in the new study. They aim to further explore the design of incentive schemes for future mobility systems that consider equity and efficiency while reducing traffic congestion costs to society.
In conclusion, the researchers’ innovative approach to optimizing road tolls using artificial intelligence has promising potential to reduce traffic congestion and improve the efficiency of congestion pricing systems in cities worldwide. This approach preserves user privacy while dynamically adjusting tolls based on observed driver behavior, which could help minimize total traffic congestion costs to society while also considering societal considerations such as equity.
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Cities worldwide are plagued by traffic congestion, which not only results in lost productivity but also contributes to increased carbon emissions and noise pollution. To address this issue, congestion pricing has been proposed as a potential solution. Congestion pricing entails charging tolls for the use of busy roads to encourage drivers to avoid crowded areas
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