Connected Vehicle-Based Lane Selection Assistance Application

Connected vehicle (CV) technology has great potential to improve the performance of today's advanced driver assistance systems in terms of safety, energy efficiency, and driving comfort. The aim of this paper is to develop a specific CV application that assists with lane selection, i.e., finding the best travel lane in terms of travel time based on predicted lane-level traffic states. In this paper, a spatial-temporal model (ST-model) was developed, which utilizes spatial and temporal information of road cells to predict future traffic states. This information was used by the proposed lane selection assistance application to select an optimal lane sequence for the application-equipped vehicle. A comprehensive simulation-based evaluation was then conducted under various scenarios, e.g., with different traffic volumes, penetration rates of communication-capable vehicles, and information update cycles. The evaluation results reveal several interesting findings, including: 1) the proposed ST-model outperforms the basic estimation model in terms of traffic state prediction accuracy; 2) travel times of application-equipped vehicles can be reduced by up to 8% with the use of the proposed lane selection assistance application when compared with the baseline, under various traffic scenarios; 3) the application can be effective in the early deployment stage of CV technology, where the penetration rate of communication-capable vehicles is still low; and 4) the potential conflict risk of application-equipped vehicles is reduced, although the application is mainly designed for mobility benefits, due to the more strategic and informed lane changes suggested by the proposed application.