There is consensus among researchers that the transfer of operating speed models is limited in geographical scope. This paper advances the argument that, even within a single region, the transferability of operating speed models can be problematic when applied to dissimilar road segments of a large road network. To address this issue, a novel methodology is introduced aimed at enhancing the transferability. This paper involves identifying similar road segments using landmark-based spectral clustering, based on attributes that prominently factor into speed models. Models are developed using data from select segments within a cluster, and their transferability to other segments is subsequently assessed on segments situated at a distance but belonging to the same cluster. To gauge this transferability, a multifaceted approach is employed, including conventional cross-validation methods, a transferability index, and covariance analysis. The results indicate an enhancement in the transferability of cluster-calibrated models. In addition, the nontransferability of the models from one cluster to an entirely different cluster within the same network is discussed.
Shilpa et al. (Sun,) studied this question.