A congested schedule-based dynamic transit passenger flow estimator using stop count data

A network-level dynamic transit passenger flow estimation model based on congested schedule-based transit equilibrium assignment is proposed using observations from stop count data. A solution algorithm is proposed for the mathematical program with schedule-based transit equilibrium constraints with proven quadratic space- and time-complexities. The error bound is proven to be linearly proportional to the number of measurements. Computational experiments are conducted first to verify the methodology with two synthetic data sets (one of which is Sioux Falls compared to a benchmark model), followed by a validation of the method using bus data from Qingpu District in Shanghai, China from 1 July 2016, with 4 bus lines, 120 segments, and 55 bus stops. The estimated average of segment flows is only 2.5% off from the average of the observed segment flows; relative errors among segments are 42.5%, which fares well compared with even less complex OD estimation methods in the literature.

Automated summary

A network-level transit passenger flow estimation model based on congested schedule-based transit equilibrium assignment is proposed, using stop count data. The methodology is verified using synthetic data sets and bus data from Shanghai, showing good accuracy compared to other estimation methods.