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 cellbased 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 center dot 150 sec center dot 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 center dot 50 sec center dot 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.