An innovative method is presented in this paper to derive the transportation mode shares on
urban freeways using mobile-phone trajectory information. It consists of two major parts:
offline learning and online inference. The offline learning first extracts the temporal feature
from the mobile-phone trajectories. By comparing to the existed link volumes, the inference
parameters are calibrated through the offline learning process. The online inference determines
the transportation modes for each individual mobile phone users in a real-time manner. The
methodology was tested via a case study designed for both the offline learning and online
inference parts. The results show the great potential of using mobile-phone trajectory
information as a means to estimating the transportation mode shares.
University of Wisconsin-Madison
Southeast University
South Dakota State University
Presented at the 18th World Congress on ITS, October 2011, Orlando, Florida