Bi-level clustering of vehicle trajectories for path choice set and its nested structure identification

• Data-driven process of path choice set identification. • DBSCAN clustering of Bluetooth trajectories for path choice set identification. • Automatised process of identifying DBSCAN hyper-parameters. • A hard clustering approach for nested structure identification, and a soft clustering approach for cross-nested structure identification. • Demonstration of the proposed framework on real OD pair. Path choice set identification is essential for route choice modelling and travel behaviour studies. Recent advancements in data collection techniques have gained attention towards a data-driven choice set identification process. However, empirical vehicle trajectory datasets result in several path observations compared to the traditional algorithms, complicating the route choice modelling process. This study proposes a bi-level vehicle trajectory clustering framework where the output of the upper-level clustering provides a representative path choice set for simple/mixed logit modelling (MNL), whereas the lower-level clustering provides a nested or cross-nested representation of the paths based on hard and soft clustering, respectively. As proof of concept, the proposed methodology is applied on real Bluetooth-based trajectories from Brisbane, where 62 unique paths were observed from a one-year trajectory data for an origin–destination pair. The results of the MNL model for the representative paths provide desirable magnitude with negative coefficients for the distance and travel time path attributes. Further, the results of the (cross) nested modelling appropriately identified the (cross) nested structure for the path choice set.