“I haven’t met anyone that really loves traffic,” says Karina Ricks of the Federal Transit Administration.
Except, possibly, professionals like her who are tasked with reducing it.
Ricks has made her career out of caring about traffic patterns. Before her current role as the associate administrator for research, innovation, and demonstration at the FTA, she was the director of mobility and infrastructure for the City of Pittsburgh in Pennsylvania. She has spent countless hours thinking about cars, public transit, roads, and pedestrians—and how to make it all flow more smoothly.
“When you’re in the peak times for travel, when the system is so full, it only takes a small disruption to cause really big problems,” Ricks says. “The work is to quickly flag those disruptions and rapidly retool the system to operate 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 problem when it comes to traffic, and a common occurrence in metropolitan areas. Add to that the number of variables at any given time, including human operators of vehicles and geography, and it results in a mind-boggling puzzle to even attempt to solve.
If there were an easy way to reduce traffic, it would have been actioned in the past 50 years, she said. Instead, she, government organizations, and startups in the space, such as Lyt, are all looking at an immense amount of traffic data available—from traffic sensors to ride share data and even bike and scooter data from smartphones—and using it to inform decisions on how to get people to work, home, and the grocery store safely and quickly.
That solution involves artificial intelligence and machine learning.
“There are tasks that humans just aren’t good at that machinery is, and that’s recognizing patterns,” explains Tim Menard, founder and chief executive officer of Lyt, a software technology platform providing mobility solutions for cities. “A.I. 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 that into a system that can make operational changes.”
Menard started Lyt after studying intelligent transportation systems for more than 13 years. His company uses vehicle data to solve traffic problems, especially when it comes to the efficiency of public transit options. For Menard, the end goal is to “make more cities equitable by making public transit reliable, predictable, and faster.”
Both Ricks and Menard believe that the way to reduce traffic is to get more people onto public transportation, such as buses, subways, and light rail systems. Public transportation is the safest surface transportation mode, with fewer injuries and fatalities. It’s also a speedier way to move a larger number of people.
Ricks explained that most of congestion is caused by “low-volume vehicles,” ie. single-occupant cars. Those drivers are human; some drive faster, some slower; some change lanes often, others stop abruptly when a traffic light flashes yellow before red. Because humans behave so differently, there is a level of unpredictability in the traffic system. Much of her work aims to make mass transit more enticing for commuters.
“You’re reducing the rate of crashes that might occur when you’re reducing the number of vehicles that are there,” Ricks added.
With that in mind, Menard started looking at the Internet of Things for his cloud platform, pulling data from smartphones, automotive sensors, public transportation logs, and delivery vehicles to understand traffic patterns at various times of the day as well as during special one-off events, such as a sports game at a local stadium. He said that the first hurdle was to operate from a place of known information rather than guessing; in the past, he explained, it took a human looking at a video screen for hours and hours to even begin to make an estimate on next steps.
He launched in San Jose, Calif., where for the past three years, he has collaborated with the city to optimize bus routes by 20%, thereby reducing fuel consumption by 14% and emissions at intersections by 12%. Using a predictive estimated time of arrival at each traffic light, his platform reduced the travel time between bus stops by optimizing bus lanes and traffic lights to ensure buses could move as effectively as possible without disrupting other traffic. He now works in other northern California cities, including additional Bay Area towns and Sacramento, as well as in the Pacific Northwest: Seattle and Portland, Ore.
Menard is also looking at bicycle and pedestrian traffic, something he says is of interest and priority to many transit authorities. He has worked to make bicycling safer by creating dedicated, curbed bike lanes with their own traffic signals synced with those of vehicle traffic to help avoid car-bicycle collisions. For pedestrians, Ricks explained that foot traffic uses sensors and adaptive controls to adjust settings in real time based on needs—a moment when the A.I. algorithm and real time data intersect.
Another benefit of A.I. technology for traffic patterns surrounds first responders. Menard employed 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 prohibit first responders from promptly arriving on scene or to a hospital with a life-or-death situation. In Sacramento, Calif., he tackled this problem.
“It was literally night and day better in under 15 minutes,” he said of taking a look at amassed data from all the relevant stakeholders in the city. There, he improved the slowest 10% of the emergency vehicles by more than 10 miles per hour, allowing them to arrive 70% faster on any response. Even the performing top 10% of vehicles saw an improvement of 6 miles per hour.
For every single-occupant car that swaps to public transit, there is one less vehicle on the road causing congestion. Menard regularly reminds people that when they are sitting in their car, stuck in traffic, they are surrounded by many other people doing the exact same thing. If they traded to a shared vehicle—a high-occupancy mode of transit—they may speed along very quickly.
But it’s always challenging to inspire commuters to change habits, so the new option needs to be compelling enough to motivate them to adjust the way they operate. “What you want in a transit system is to show up now [and] there’s a bus ready to get you in a timely fashion,” Ricks said. “We need to address traffic in order for transit to be that attractive alternative. There’s quite a bit of work to still do.”