  This paper proposes a self-calibrated transit service monitoring framework
that aims to obtain the performance of a transit system using automated
collected data. We first introduce an event-based transit simulation model,
which allows the detailed simulation of passenger travel behavior in a transit
system, including boarding, alighting, and transfer walking. To estimate
passenger path choices, we assume the path choices can be modeled using a
C-logit model, and propose a simulation-based optimization model to estimate
the path choice parameters based on automated fare collection and automated
vehicle location data. The path choices can be estimated on a daily basis,
which enables the simulation model to adapt to dynamic passenger behavior
changes, and output more accurate network performance indicators for regular
service monitoring such as train load, passenger travel time, and crowding at
platforms. The proposed system eliminates the need for conventional monitoring
equipment such as cameras at platforms and scaling/weighing systems on trains.
The Hong Kong Mass Transit Railway (MTR) system is used as the case study.
Results show that the model can well estimate the path choice behavior of
passengers in the system. The output passenger exit flows are closer to the
actual one compared to the two benchmark models (shortest path and uniform path
choice).
