  With the increasing adoption of Automatic Vehicle Location (AVL) and
Automatic Passenger Count (APC) technologies by transit agencies, a massive
amount of time-stamped and location-based passenger boarding and alighting
count data can be collected on a continuous basis. The availability of such
large-scale transit data offers new opportunities to produce estimates for
Origin-Destination (O-D) flows, helping inform transportation planning and
transit management. However, the state-of-the-art methodologies for AVL/APC
data analysis mostly tackle the O-D flow estimation problem within routes and
barely infer the transfer activities across the entire transit network. This
paper proposes three optimization models to identify transfers and approximate
network-level O-D flows by minimizing the deviations between estimated and
observed proportions or counts of transferring passengers: A Quadratic Integer
Program (QIP), a feasible rounding procedure for the Quadratic Convex
Programming (QCP) relaxation of the QIP, and an Integer Program (IP). The
inputs of the models are readily available by applying the various route-level
flow estimation algorithms to the automatically collected AVL/APC data and the
output of the models is a network O-D estimation at varying geographical
resolutions. The optimization models were evaluated on a case study for Ann
Arbor-Ypsilanti area in Michigan. The IP model outperforms the QCP approach in
terms of accuracy and remains tractable from an efficiency standpoint, contrary
to the QIP. Its estimated O-D matrix achieves an R-Squared metric of 95.57% at
the Traffic Analysis Zone level and 92.39% at the stop level, compared to the
ground-truth estimates inferred from the state-of-practice trip-chaining
methods.
