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With the increasing adoption of Automatic Vehicle Location (AVL) andAutomatic Passenger Count (APC) technologies by transit agencies, a massiveamount of time-stamped and location-based passenger boarding and alightingcount data can be collected on a continuous basis. The availability of suchlarge-scale transit data offers new opportunities to produce estimates forOrigin-Destination (O-D) flows, helping inform transportation planning andtransit management. However, the state-of-the-art methodologies for AVL/APCdata analysis mostly tackle the O-D flow estimation problem within routes andbarely infer the transfer activities across the entire transit network. Thispaper proposes three optimization models to identify transfers and approximatenetwork-level O-D flows by minimizing the deviations between estimated andobserved proportions or counts of transferring passengers: A Quadratic IntegerProgram (QIP), a feasible rounding procedure for the Quadratic ConvexProgramming (QCP) relaxation of the QIP, and an Integer Program (IP). Theinputs of the models are readily available by applying the various route-levelflow estimation algorithms to the automatically collected AVL/APC data and theoutput of the models is a network O-D estimation at varying geographicalresolutions. The optimization models were evaluated on a case study for AnnArbor-Ypsilanti area in Michigan. The IP model outperforms the QCP approach interms of accuracy and remains tractable from an efficiency standpoint, contraryto the QIP. Its estimated O-D matrix achieves an R-Squared metric of 95.57% atthe Traffic Analysis Zone level and 92.39% at the stop level, compared to theground-truth estimates inferred from the state-of-practice trip-chainingmethods.

Optimization Models for Estimating Transit Network Origin-Destination Flows with AVL/APC Data