Network-scale carbon mitigation potential of truck platooning via large-scale trajectory data

Truck platooning is a promising strategy to reduce carbon emissions in freight transport, but its real-world mitigation potential across large-scale traffic networks has not been fully quantified. This study develops a data-driven framework to empirically evaluate the carbon reduction effects of truck platooning using eight months of high-resolution GPS trajectories. Advanced map-matching, an enhanced Longest Common Subsequence trajectory similarity method, and graph-based clustering are applied to identify spontaneous platooning events and estimate associated emission reductions. The framework also enables analysis of the spatiotemporal distribution of platooning opportunities across a regional road network. Results indicate that fully exploiting observed spontaneous platooning could cut total fuel consumption by 11.8% compared with a no-platooning baseline. Notably, only 35.8% of platoons occur within a single enterprise, highlighting the importance of cross-company coordination. These findings provide empirical evidence and methodological support for policymakers and industry stakeholders to promote sustainable freight transport through effective platooning strategies.