Decisions made by the drivers on the road have a significant impact on the traffic, as well as on their vehicular emissions. Simple decisions such as accelerating at a yellow traffic signal can alter the dynamics of traffic flow and engine operating points. Especially in cities, these decisions, cumulatively, can impact large complex systems like transportation network. For example, these can lead to unstable conditions such as traffic congestion or high concentration of emissions. In order to reduce these impacts, it is necessary to understand the drivers’ decisions, their influence on other vehicles, and the resulting impacts on emissions. This need drives the main objective of this paper, which is, to understand the relationships between the drivers decisions and vehicular CO2 emissions. In this paper, we model the drivers’ decisions by introducing a utility function for each driver in a micro-transportation model. Our hypothesis is that drivers have their own safe distance not only over front headway but also on rear headway. This usually happens when the driver chooses to change from acceleration to deceleration phases. We also propose a non-linear driver model to quantify the risk attitude of a driver based on factors such as age, experience, time of journey and the number of occupants. The Full Velocity Difference Method (FVDM) represents the traffic characteristics including speed, position, and vehicle acceleration at a traffic signal better than other car-following models. Hence, FVDM is used in our simulations. Numerical simulations show that a risk prone person results in a greater fuel consumption compared to the risk averse person. Simulation results also show that decisions made by a lead vehicle can be influenced by the follower vehicles properties such as speed and position and vice versa. The results show how driver decisions can alter the vehicular CO2 emissions. The proposed model is a step towards developing a decision support tool, which can help drivers in reducing CO2 emissions.

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