Optimizing generation of anchor points for route choice modeling by community detection

Abstract

The anchor-based nested logit model is suitable to describe route choice behaviors since its abstract framework is consistent with style of travelers processing road network information. But in practice, the traditional definition of anchors is largely related to the properties of road network elements and too many anchors defined greatly increase computational burden. In this study, a data-driven anchor point generation method by community detection was proposed to address these issues. Travel communities are detected considering travel relationship topology by the massive individual trip data; and the frequently used bridges, expressways, and arterial roads in one community are identified as an anchor point which is in accordance with the mental representation of travelers’ route choice. The anchor points are employed to construct the nests in the route choice model to capture the choice correlation of travelers who pass through the same anchor points; and the small number of travel communities means the nests can be significantly reduced in number comparing with the traditional anchor-based models so that the computing burden is much less, while the ability to capture the correlation of routes is still remained. A case study is carried out for Guangzhou City, and the results suggest that the proposed anchor point based model obtains satisfying results in goodness-of-fit and forecasting; in addition, the computation time counts about only one-tenth that of the traditional anchor-based model, making it suitable for route choice analysis in the large road networks. Moreover, some practical implications are drawn for traffic management from the empirical study.