Estimation of heterogeneous vehicle carbon dioxide emission trajectories using integrated vehicle, road, travel, and environmental data

Abstract

Individual vehicle travel carbon dioxide (CO₂) emission (CE) trajectories were crucial for targeting high-emitters for precise control and guiding low-carbon travel. Variations in CE arise from vehicle performance, traffic conditions, and trip purposes. Using real Automatic Vehicle Identification (AVI) data and integrating multi-source vehicle, road, trip, and environmental data, this study proposed an "Identification-Calculation-Evaluation" framework to quantify and analyze city-scale full-individual vehicle CE trajectories. A case in Xuancheng, China, was conducted and revealed spatiotemporal CE heterogeneity. The results showed that approximately 50 % of CE was contributed by the top 5 % of high-emission vehicles, exhibiting a significant “Pareto Principle”. Among the top 5 % of high-emission vehicles, LPC-gasoline (57 % of vehicles, 40 % of CE), HDT-diesel (32 %, 42 %), and Taxi-gasoline (5 %, 12 %) were the main contributors. Their daily CE trajectory ranges were [0, 6] kg, [0, 15] kg, and [0, 8] kg, respectively. Taxi-gasoline and HDT-diesel exhibit more individual variation. Peak-time CE trajectories on these Top 5 % vehicles were 2–6 times higher than off-peak. For LPC-gasoline and Taxi-gasoline, over 60 % of CE occurred during congestion links. Peak times of CE trajectories occurred around 7:00 and 17:00 on a day, with spatial hotspots predominantly concentrated in urban core areas. Notably, Taxi-gasoline vehicles exhibited more clustered hotspots. HDT-diesel CE trajectories peaked earlier (6:00–7:00), with hotspots distributed along major urban corridors, and CE was 1–3 times higher than in ordinary areas. This study provided precise support for low-carbon traffic governance, and the framework could be extended to other cities to inform carbon reduction strategies.