Mandatory lane-changing strategy for connected automated trucks in off-ramp area based on evolutionary game theory

In highway off-ramp area, vehicles often engage in mandatory lane-changing behavior. This behavior can cause speed fluctuations in the mainline traffic flow, leading to traffic congestions and more emissions. The impact of mandatory lane-changing is particularly pronounced for trucks, due to the unique characteristics of their vehicle types. To mitigate speed fluctuations and traffic emissions caused by trucks’ mandatory lane-changing behavior, this paper proposes a connected automated trucks (CATs) off-ramp lane-changing strategy based on evolutionary game theory. First, we presented urgency utility, synergy utility, and deceleration utility of CATs during mandatory lane-changing process in off-ramp area, in order to construct a utility matrix. Based on this, we derived replicator dynamic equation. Building on this, an off-ramp lane-changing strategy for CATs was developed. Finally, the effectiveness of the proposed strategy in mitigating speed fluctuations and reducing traffic emissions was validated using the SUMO simulation platform. The results show that the proposed strategy can smooth speed fluctuations and reduce traffic emissions. Compared with traditional truck off-ramp lane-changing strategy, the overall traffic emissions of CO, HC, and NOx under the proposed strategy are reduced by 13.3 %, 8.4 %, and 10.7 %, respectively. Notably, while optimizing their own lane-changing decisions, CATs also improve operational conditions of surrounding vehicles. Compared with the traditional strategy, the proposed strategy leads to a reduction in CO, HC, and NOx emissions of the surrounding vehicles by 7.8 %, 7.6 %, and 16.7 %, respectively.