Choice-based macroscopic lane-change prediction model for weaving areas

This paper introduces two macroscopic lane change models, separately for small vehicles (passenger cars and vans) as well as heavy vehicles. The model is structured using a Nested Logit discrete choice framework. The model was estimated and validated using unique trajectory data collected from a busy weaving section in Antwerp, Belgium. The models exhibit an average and maximum rate of inaccurate prediction results, amounting to 11% and 16%, respectively. Additionally, the model underestimates the total number of lane change maneuvers, with a margin of no more than 4.1-7.7%. This study aims to fill the gap in macroscopic lane change models, which mainly depend on theoretical underpinnings, by including empirical data from the analysis of more than 31,000 observed maneuvers. Furthermore, it differentiates among three maneuver intents, a unique alternative-specific discrete choice framework. This differentiation allows for the consideration of drivers' systematic taste variations. In contrast to other models that utilize empirical datasets and are heavily dependent on expensive video-based data, including the entire traffic flow, this study introduces a novel approach for estimating the position and frequency of maneuvers by using just a small fraction (1-2%) of vehicle trajectories, supplemented by loop detector data, therefore eliminating the need for information from surrounding vehicles. Additionally, the work's uniqueness is its validation methods extending beyond just numerical evaluations. The practical usability and usefulness of the model are shown in real-world traffic circumstances. Finally, the model is integrated in the context of a macroscopic simulation that represents a highly consistent lane balance based on the Scalable Quality Value statistics.