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

To accurately estimate freeway traffic carbon dioxide (CO₂) 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 CO₂ emissions of the traffic in the ST diagram. The factors impacting the CO₂ emissions in the ST diagram are selected and taken as model inputs. First- and second-order regression models are employed to fit the exact CO₂ emissions. It is found that the relationship between complicated traffic dynamics and CO₂ emissions can be simply described by using a linear or nearly linear function; i.e., for larger cells (such as $90\cdot 150$ sec $\cdot \text{m}$ ) 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 $30\cdot 50$ sec $\cdot \text{m}$ ) 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 CO₂ emissions by inputting the prevailing ST diagram. It opens a gate for estimating CO₂ 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.