Bus OD matrix reconstruction based on clustering Wi-Fi probe data

Abstract

The estimation of citywide passenger demand plays a vital role in system planning, operation, and management of the urban transit system. The Wi-Fi probe data, one of the emerging crowdsourcing data, is utilized to collect traces of smartphone users in this study. We establish a framework for OD matrix reconstruction, including extracting features for transit patronage and distinguishing them from non-transit users based on K-means clustering. Such a framework makes partial OD matrix more reliable. A probabilistic estimation method of bus OD matrix reconstruction is then proposed based on the partial OD matrix and the number of boarding and alighting passengers. A field study was carried out on bus line 5 in Suzhou, China. Compared to the measured ground truth, the difference in OD-level is 0.5–1.5 passengers per stop, showing that the proposed method for OD matrix reconstruction is reliable.