To analyze the impact of different proportions of connected automated vehicles (CAVs) on fuel consumption and traffic emissions, this paper studies fuel consumption and traffic emissions of mixed traffic flow with CAVs at different traffic scenarios. Firstly, the car-following modes and proportional relationship of vehicles in the mixed traffic flow are analyzed. On this basis, different car-following models are applied to capture the corresponding car-following modes. Then, Virginia Tech microscopic (VT-micro) model is adopted to calculate the instantaneous fuel consumption and traffic emissions. Finally, based on three typical traffic scenarios, a basic segment with bottleneck zone, ramp of the freeway, and signalized intersection, a simulation platform is built based on Python and SUMO to obtain vehicle trajectory data, and the fuel consumption and traffic emissions in different scenarios are obtained. The results show that (1) In different traffic scenarios, the application of CAVs can reduce fuel consumption and traffic emissions. The higher the penetration rate, the more significant the reduction in fuel consumption and traffic emissions. (2) In the three typical traffic scenarios, the advantages of CAVs are more evident in the signalized intersection. When the penetration rate of CAVs is 100%, the fuel consumption and traffic emissions reduction ratio is as high as 32%. It is noteworthy that the application of CAVs in urban transportation will significantly reduce fuel consumption and traffic emissions.
Connected automated vehicles (CAVs) can significantly shorten the headway of car following, thereby effectively improving the traffic capacity and injecting new power to alleviate traffic congestion. To investigate the congestion characteristics of mixed traffic flow with CAVs and human-driven vehicles (HDVs), this paper proposes a cell transmission model to capture and simulate traffic congestion for mixed traffic flow. Firstly, the Newell, adaptive cruise control (ACC), and cooperative adaptive cruise control (CACC) models are adopted to capture the car-following behavior of different vehicles. Secondly, the fundamental diagram under different penetration rates of CAVs is derived based on car-following models. Then, the cell transmission model (CTM) of mixed traffic flow is developed based on the classical CTM and fundamental diagram of mixed traffic flow. Finally, two simulation methods, mixed traffic flow CTM and micro-simulation, are designed to verify the effectiveness of the proposed model. Moreover, taking the moving bottleneck on the expressway as an example, the congestion characteristics of mixed traffic flow are analyzed using multiple indexes, such as average travel speed, congestion delay, and congestion scale. The results show the following: (i) CAVs can significantly alleviate traffic congestion, (ii) the duration of the bottleneck is positively correlated with the degree of traffic congestion, and (iii) The traffic congestion assessment results under different model parameters slightly differ, but the impact is negligible.
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