Estimating Carbon Dioxide Emissions of Freeway Traffic: A Spatiotemporal Cell-Based Model

To accurately estimate freeway traffic carbon dioxide (CO
₂
) emissions, this paper proposes a spatiotemporal cell-based model by taking traffic dynamics into account. High-fidelity vehicle trajectory data is used to construct a spatiotemporal traffic (ST) diagram and to calculate the exact CO
₂
emissions of the traffic in the ST diagram. The factors impacting the CO
₂
emissions in the ST diagram are selected and taken as model inputs. Firstand second-order regression models are employed to fit the exact CO
₂
emissions. It is found that the relationship between complicated traffic dynamics and CO
₂
emissions can be simply described by using a linear or nearly linear function; i.e., for larger cells (such as 90·150 sec·m) that are used to construct an ST diagram, a first-order regression model is able to well reflect the relationship, while for small cells (such as 30·50 sec·m) a second-order model is more accurate. To validate the proposed model, another trajectory dataset that was collected in a different freeway segment is introduced, and the transferability and predictability of the model are demonstrated. The proposed spatiotemporal cell-based model allows us to accurately estimate CO
₂
emissions by inputting the prevailing ST diagram. It opens a gate for estimating CO
₂
emissions from widely available low-fidelity traffic data, since the ST diagram can be constructed by using various traffic flow data, such as loop detector data and floating car data.