Big-data empowered traffic signal control could reduce urban carbon emission

Kan Wu, Jianrong Ding, Jingli Lin, Guanjie Zheng, Yi Sun, Jie Fang, Tu Xu, Yongdong Zhu and Baojing Gu ()
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Kan Wu: Hangzhou City University
Jianrong Ding: Shanghai Jiao Tong University
Jingli Lin: Shanghai Jiao Tong University
Guanjie Zheng: Shanghai Jiao Tong University
Yi Sun: Hangzhou City University
Jie Fang: Hangzhou City University
Tu Xu: Zhejiang Police college
Yongdong Zhu: Zhejiang Lab
Baojing Gu: Zhejiang University

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-56701-4

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