Urban congestion is a pressing challenge, driving up emissions and compromising transport efficiency. Advances in big-data collection and processing now enable adaptive traffic signals, offering a promising strategy for congestion mitigation. In our study of China’s 100 most congested cities, big-data empowered adaptive traffic signals reduced peak-hour trip times by 11% and off-peak by 8%, yielding an estimated annual CO₂ reduction of 31.73 million tonnes. Despite an annual implementation cost of US$1.48 billion, societal benefits—including CO₂ reduction, time savings, and fuel efficiency—amount to US$31.82 billion. Widespread adoption will require enhanced data collection and processing systems, underscoring the need for policy and technological development. Our findings highlight the transformative potential of big-data-driven adaptive systems to alleviate congestion and promote urban sustainability. Big-data empowered traffic signal control in China can reduce vehicle trip times, creating potential reduction of 31.73 million tonnes (Mt) of CO2 emissions annually and US$31.8 billion benefits per year.
Sensors are cheap and have been around for a long time, but I’m going to guess the number one reason is the small part. Fewer cars = less traffic.
I’ve actually watched a city I visit regularly grow over about 20 years and it went from them having zero traffic to Los Angeles style traffic jams. This is despite their best efforts like making extra wide roads, using roundabouts, etc.
Sensors are cheap and have been around for a long time, but I’m going to guess the number one reason is the small part. Fewer cars = less traffic.
I’ve actually watched a city I visit regularly grow over about 20 years and it went from them having zero traffic to Los Angeles style traffic jams. This is despite their best efforts like making extra wide roads, using roundabouts, etc.