Traffic Experiment
Clide Birkner
Traffic that grinds to a halt and then restarts for no apparent reason is one of the biggest causes of frustration for drivers. Now a team of Japanese researchers has recreated the phenomenon on a test-track for the first time.
They asked drivers to cruise steadily at 30 kilometres per hour, and at first the traffic moved freely. But small fluctuations soon appeared in distances between cars, breaking down the free flow, until finally a cluster of several vehicles was forced to stop completely for a moment.
The shockwave jam travelled backwards through the ring of vehicles at roughly 20 km/h, which is the same as the speed of the shockwave jams observed on roads in real life, says lead researcher Yuki Sugiyama, a physicist in the department of complex systems at Nagoya University.
As the drivers traversed their route, researchers collected traffic data from both the vehicles and the I-24 MOTION traffic monitoring system. On Nov. 16 alone, the system recorded a total of 143,010 miles driven and 3,780 hours of driving. The I-24 MOTION system, combined with vehicle energy models developed in the CIRCLES project, will provide an estimation of the fuel consumption of the whole traffic flow during those hours.
The new AI technology goes a step beyond the adaptive cruise control systems that are already on the market. In addition to adjusting the speed of the vehicle in response to local conditions, the technology also incorporates information about traffic conditions and adjusts the speed to help smooth the overall flow of traffic.
The experiment also demonstrated a new feature developed by the CIRCLES team: the ability to simultaneously push collaborative algorithms to different car platforms (Nissan, GM and Toyota). The team is in the process of planning how the technology can be deployed in California.
For more than a decade, Bayen and other members of the CIRCLES consortium have been applying the latest technologies to help improve transportation. In 2008, Bayen and Daniel Work, who was a UC Berkeley graduate student at the time, led the Mobile Millennium project, one of the first demonstrations of how GPS-enabled smartphones can provide real-time information about traffic conditions. In the experiment, the UC Berkeley-based team managed a fleet of 100 vehicles driving a 10-mile route through the San Francisco Bay area, while Nokia phones transmitted speed information from each vehicle to a central server.
Now that smartphones are ubiquitous and real-time traffic information is available at the click of a button, Bayen is excited to show how machine learning can be used to not only monitor traffic but also improve conditions on the road.
After securing a $3.5 million grant from the U.S. Department of Energy (DOE) in 2020, the CIRCLES team began preparations to repeat the experiment on a much larger scale, this time integrating the AI-equipped vehicles into the normal flow of highway traffic.
To develop these speed planners, the team must first must define the mathematical models that describe how traffic behaves. In general, Matin says, the flow of traffic can be modeled using equations similar to those that govern the flow of fluids, but the human element of driving complicates things.
Bayen, Delle Monache, Lee, Matin and AlAnqary were among 18 UC Berkeley students, post-docs, staff, and faculty who traveled to Nashville last week to help conduct the experiment. As drivers took their vehicles on the interstate and activated the AI-powered cruise control system, the team was on hand to analyze the data coming in and address any last-minute technical glitches that arose during the experiment.
CIRCLES Consortium research is supported by the National Science Foundation, the U.S. Department of Transportation and the U.S. Department of Energy. Additional funding was provided by Nissan, Toyota North America, General Motors, the Federal Highway Administration, the Tennessee Department of Transportation, the California Department of Transportation, the Nashville Department of Transportation, Gresham Smith, Siemens, Deutsches Zentrum fr Luft- und Raumfahrt (DLR), Amazon Web Services (AWS), C3.ai Digital Transformation Institute, the UC Berkeley Institute of Transportation Studies, Vanderbilt University, the University of Arizona, Rutgers University, Temple University, Ecole des Ponts ParisTech and the Universit Gustave Eiffel.
The CIRCLES Consortium, consisting of Vanderbilt University and several other universities, in coordination with Nissan North America, Toyota, GM, and the Tennessee Department of Transportation, will test 100 AI-equipped vehicles in an effort to mitigate human-caused traffic jams.
The I-24 MOTION testbed, where the AI-equipped vehicles will travel in the normal flow of traffic, is the only automotive testing environment of its kind in the U.S. It is opening in fall 2022 and is equipped with 300 4K digital sensors that are mounted on poles spaced 600 feet apart. The system generates data on the 260,000,000 vehicle-miles of traffic that occurs annually within the testbed.
In this experiment, researchers from the CIRCLES Consortium will deploy up to 100 vehicles, comprised of Nissan Rogue, Toyota RAV4 and a Cadillac XT5, that each include an AI-equipped adaptive cruise control technology used in an earlier experiment.
As researchers increase the scale of the testing and introduce real world driving conditions, they will investigate whether the improved traffic and fuel-economy outcomes measured in the smaller study continue to hold.
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