Hybrid modeling of lane changes near freeway diverges. (November 2022) - Zhou, Hao; Toth, Christopher; Guensler, Randall; Laval, Jorge
Abstract: This paper proposes a discrete/continuum hybrid framework for modeling the effects of lane-changing (LC) activity near freeway diverges in an effort to explain puzzling empirical observations of congestion waves not captured by current models for discretionary lane changes (DLCs). We show that this discrepancy is explained by the disruption of mandatory lane changes (MLCs), which come to a complete stop while waiting for a gap in the target lane. This disruption causes a backup upstream of the MLC and a void downstream responsible for reducing capacity. Our contribution is the formulation of two stochastic processes to capture these effects in the context of a hybrid framework combining the kinematic wave theory and moving bottlenecks treated as discrete particles. We find that the proposed method successfully replicates empirical observations, especially when it comes to the formation and propagation of stop-and-go waves. We also show that traditional continuum methods for treating lane changing are unable to capture observations. In all, our finding indicates that a key element to replicate traffic instabilities observed near freeway exits is the disruptive nature of both MLCs and DLCs. When this extreme disruption takes place, the system becomes chaotic and congestion spreads throughout the network quickly. The proposed hybrid approach requires only two additional parameters, and research is ongoing to determine if these parameters are transferable to other locations. Highlights: A discrete/continuum hybrid framework for modeling lane-changing (LC) near freeway diverges A stochastic process to capture waiting times and the congestion effects of MLCs. Continuum methods for treating lane changing are unable to capture observations. A key element to replicate traffic instabilities is the disruptive nature of both MLCs and DLCs.