AI solves traffic problems to get you where you're going safely

AI solves traffic problems to get you where you’re going safely

“I haven’t met anyone who really likes traffic,” says Karina Ricks of the Federal Transit Administration.

Except, perhaps, professionals like her who are in charge of reducing it.

Ricks has made his career caring about traffic patterns. Prior to her current role as Associate Administrator for Research, Innovation and Demonstration at the FTA, she was Director of Mobility and Infrastructure for the City of Pittsburgh in Pennsylvania. She spent countless hours thinking about cars, public transport, roads and pedestrians, and how to make it all smoother.

“When you’re at peak travel times, when the system is so full, it only takes one small disruption to cause really big problems,” says Ricks. “The job is to quickly flag these disruptions and quickly retool the system to work around them.”

What Ricks aims to optimize affects anyone moving from point A to point B, especially in cities. She explained that congestion is the number one traffic problem and a common occurrence in metropolitan areas. Add to that the number of variables at any given time, including human vehicle operators and geography, and it makes for a mind-boggling puzzle to even attempt to solve.

If there was a simple way to reduce traffic, it would have been implemented over the past 50 years, she said. Instead, she, government organizations, and space startups, such as Lyt, are all looking at an immense amount of available traffic data — from traffic sensors to ride-sharing data and even bike data and smartphones – and use them to inform decisions about how to get people to work, home and the grocery store safely and quickly.

This solution involves artificial intelligence and machine learning.

“There are tasks that humans are just not good at machines, and that’s pattern recognition,” says Tim Menard, founder and CEO of Lyt, a software technology platform providing mobility solutions. for cities. “AI is a great technology to use because you’re looking at all parts of the system. You can start feeding it different information, and you can put it into a system that can make operational changes. »

Menard launched Lyt after studying intelligent transportation systems for over 13 years. His company uses vehicle data to solve traffic problems, especially when it comes to the efficiency of public transport options. For Menard, the end goal is to “make more cities fair by making public transit reliable, predictable, and faster.”

Both Ricks and Menard believe the way to reduce traffic is to get more people to use public transportation, such as buses, subways, and light rail systems. Public transportation is the safest mode of surface transportation, with fewer injuries and fatalities. It’s also a faster way to move more people.

Ricks explained that most traffic jams are caused by “low-volume vehicles,” ie. single occupant cars. These pilots are human; some drive faster, others slower; some often change lanes, others stop abruptly when a traffic light flashes yellow before red. Because humans behave so differently, there is a level of unpredictability in the circulation system. Much of his work is aimed at making public transit more attractive to commuters.

“You reduce the rate of accidents that could happen when you reduce the number of vehicles that are there,” Ricks added.

With that in mind, Menard started looking at the Internet of Things for its cloud platform, mining data from smartphones, automotive sensors, transit logs, and delivery vehicles to understand patterns. traffic at different times of the day as well as during a special period. outside of events, such as a sports match at a local stadium. He said the first hurdle was operating from a place of known information rather than guesswork; In the past, he explained, it took a human staring at a video screen for hours and hours to even begin to estimate next steps.

It launched in San Jose, California, where over the past three years it has worked with the city to optimize bus routes by 20%, reducing fuel consumption by 14% and emissions at intersections by 12 %. Using a predictive estimate of arrival time at each traffic light, its platform reduced travel time between bus stops by optimizing bus lanes and traffic lights to ensure buses can move as efficiently as possible without disrupting other traffic. He now works in other Northern California cities, including other Bay Area cities and Sacramento, as well as in the Pacific Northwest: Seattle and Portland, Ore.

Menard is also looking at bicycle and pedestrian traffic, which he says are of interest and priority for many transport authorities. It has worked to make cycling safer by creating dedicated, demarcated cycle lanes with their own traffic lights synchronized with those of vehicular traffic to help avoid car-bike collisions. For pedestrians, Ricks explained that foot traffic uses sensors and adaptive controls to adjust parameters in real time as needed — a time where the AI ​​algorithm and real-time data intersect.

Another benefit of AI technology for traffic patterns relates to first responders. Menard used machine learning to analyze data from emergency vehicles like ambulances and fire trucks to improve speed. He noted that in many urban environments, congestion and traffic patterns make it difficult for first responders to quickly arrive on scene or at a hospital in a life-and-death situation. In Sacramento, California, he tackled this problem.

“It was literally better day or night in less than 15 minutes,” he said, reviewing data collected from all relevant stakeholders in the city. There, he improved the slowest 10% of emergency vehicles by more than 16 km/h, allowing them to arrive 70% faster in the event of an intervention. Even the top 10% of vehicles saw a 10 km/h improvement.

For every single passenger car that switches to public transit, there is one less vehicle on the road causing congestion. Ménard regularly reminds people that when they’re sitting in their car stuck in traffic, they’re surrounded by lots of other people doing the exact same thing. If they opt for a shared vehicle – a high occupancy mode of transport – they can speed up very quickly.

But getting commuters to change their habits is always difficult. The new option must therefore be convincing enough to motivate them to adjust their mode of operation. “What you want in a transit system is to show up now [and] there is a bus ready to take you in due time,” Ricks said. “We have to manage traffic so that public transit is that attractive alternative. There is still a lot of work to do. »

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