Two-lane traffic flow model based on regular hexagonal cells with realistic lane changing behavior

Abstract

In this paper, a two-lane cellular automata traffic flow model based on regular hexagonal cells is proposed. The realistic lane changing maneuvers with the lateral movement process, the lane changing duration, and the unsuccessful lane changing are considered by the new model. The vehicle trajectories from NGSIM dataset are used to extract the realistic lane changing maneuvers and calibrate the lane changing angle. The numerical results showed that the flow in the intermediate density range of the new model is lower than that of the traditional model. While the lane changing frequency in the same density range of the new model is higher than that of the traditional model. The lane changing duration will increase as the density grows. And the unsuccessful lane changing rate grows to large values in high density range. As to the heterogeneous traffic flow, the lane changing frequency is obviously greater than that in homogeneous condition. The trucks are more likely to make unsuccessful lane changing. And the lane changing duration of trucks is longer than that of cars in free flow region, but this is not the case in congested flow.