Spatiotemporal differences in traffic efficiency and CO₂ emissions across day types: A city-level data-driven analysis of cooperative vehicle-infrastructure systems.

Authors

Bin Sun, Haoyu Xu, Zhaolang Wu, Qijun Zhang, Lin Wu, Yan Liu, Yanan Mi, Hongjun Mao

Published in

Journal of environmental management. Volume 414. Pages 130478. Jul 12, 2026. Epub Jul 12, 2026.

Abstract

Speed-guided Cooperative Vehicle-Infrastructure System (CVIS) can alleviate congestion and reduce emissions, but the performance differences between weekdays and weekends are unclear. This study analyzes traffic optimization and CO₂ emissions on weekdays and weekends using big data from CVIS in Zibo, a representative, medium-sized, prefecture-level city in China at an early stage of low-penetration citywide CVIS adoption. We developed a trajectory-based peak travel period detection method, a Traffic Resistance Index (TRI), and a CO₂ emission quantification model. Analysis of 47 million trajectories revealed that CVIS increases vehicle speed but intensifies its volatility, with the effect more pronounced on weekends. CVIS significantly reduces traffic resistance and improves operational efficiency. Both the relative improvement rate and the absolute optimization effect of traffic resistance are more pronounced on weekends than on weekdays. In terms of time periods, CVIS amplifies the weekend advantage of TRI under both off-peak and peak conditions, strengthens the spatial aggregation of CO₂ emissions during peak periods and weakens it during off-peak periods, while it weakens the spatial aggregation of TRI during weekday peak periods and uniformly across all weekend periods. However, CVIS increases CO₂ emission factors, with 11.0% on weekends and 5.6% on weekdays, due to its prioritization of speed improvements and low vehicle penetration rates. Accordingly, this paper proposes three optimization strategies for CVIS to improve its performance across different day types and road classes. These findings provide decision support for CVIS applications and promote the development of green, intelligent transportation.

PMID:
42437543
Bibliographic data and abstract were imported from PubMed on 13 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.