  Urban rail services are the principal means of public transportation in many
cities. To understand the crowding patterns and develop efficient operation
strategies in the system, obtaining path choices is important. This paper
proposed an assignment-based path choice estimation framework using automated
fare collection (AFC) data. The framework captures the inherent correlation of
crowding among stations, as well as the interaction between path choice and
left behind. The path choice estimation is formulated as an optimization
problem. The original problem is intractable because of a non-analytical
constraint and a non-linear equation constraint. A solution procedure is
proposed to decompose the original problem into three tractable sub-problems,
which can be solved efficiently. The model is validated using both synthetic
data and real-world AFC data in Hong Kong Mass Transit Railway (MTR) system.
The synthetic data test validates the model's effectiveness in estimating path
choice parameters, which can outperform the purely simulation-based
optimization methods in both accuracy and efficiency. The test results using
actual data show that the estimated path shares are more reasonable than
survey-derived path shares and uniform path shares. Model robustness in terms
of different initial values and different case study dates are also verified.
