The impact of driving homogeneity due to automation and cooperation of vehicles on uphill freeway sections

Abstract

Background: This work presents a microsimulation study on the topic on an uphill network, regarding the potential impact of AVs and Cooperative-AVs (Coop-AVs or CAVs), vehicles able to cooperate with the infrastructure. The novelty of the proposed approach is that the simulation of all vehicles is performed with a common hybrid car-following model that takes explicitly into account the variability in the vehicle dynamics and the driving behaviors. Methods: Simulation of longitudinal movement of the individual vehicles is performed with a common hybrid carfollowing model that takes explicitly into account the variability in the vehicle dynamics and the driving behaviors. Different homogeneity levels in the vehicles and drivers are tested, while the cooperation is explicitly assessed by proposing a realistic Vehicle to Infrastructure (V2I) logic. Possible reduction in the response times of the vehicles is also studied. Results: Results with more homogenous vehicle movements have more consistent performance in terms of traffic flow, that is independent of the order that the vehicles enter the network. Finally, the cooperation with the infrastructure can limit high variations in the vehicles' accelerations and thus potential traffic jams. Conclusions: Homogenized flows can mitigate or even solve traffic-related problems related to the variability in driving behaviors, such as bottlenecks and stop-and-go waves.

Rule-Based On-Off Traffic Control Strategy for CAVs on Motorway Networks: Assessing Cooperation Level and Driving Homogeneity

“…During implementation, care was taken to avoid disturbing influences on the speed traveled, for example, due to tight curve radii. The traffic demand (split 80% cars and 20% trucks) was set realistically (comparable to real-world case studies [39] with congested freeway segments), around 2000 [veh/h/lane] under the theoretical capacity for autonomous vehicles but large enough to create congestion.…”

Benchmarking the driver acceleration impact on vehicle energy consumption and CO2 emissions

“…The use of theoretical microsimulation models is an appealing solution to reproduce different driving styles based on the elaboration of new trajectories ( Laval et al, 2014 ) ( Xu et al, 2015 ) ( Javanmardi et al, 2017 ). Several works in the past have considered the consequences of the acceleration diversity in both energy and traffic-related studies ( Makridis et al, 2020a ) ( He et al, 2020a ) ( Makridis et al, 2020b ). In particular, Berry ( Berry, 2010 ) modelled the impact of the velocity on the fuel consumption, but the speed profiles used in the simulations were retrieved from a generic database and were not characteristic of any specific driver.…”

Comparing the Observable Response Times of ACC and CACC Systems

“…Similarly, Makridis et al [8] highlight that the anticipated increase in the capacity for connected and automated vehicle can also lead to benefits with regard to energy consumption. Vehicle cooperation can also lead to faster response on perturbations and lower driver heterogeneity as mentioned in the literature [9], [10]. Li et al [11] mention that for long platoons, oscillation amplitude tends to exacerbate very quickly, which can cause significant overshooting and safety hazards.…”