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

TL;DR

This paper introduces a novel dynamic transit flow estimation model that leverages stop count data and a schedule-based equilibrium framework, validated through synthetic and real-world bus data with high accuracy.

Contribution

It develops a new estimation method incorporating schedule-based hyperpath flows and provides an efficient solution algorithm with polynomial complexity.

Findings

Model converges within 10 iterations with 0.005 tolerance.

Estimated flows are within 2.5% of observed flows on average.

Relative errors among segments are 42.5%.

Abstract

A dynamic transit 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 (MPEC) with polynomial computational complexity. The equilibrium constraints corresponding to the schedule-based hyperpath flow are modified from the literature to fit into an estimation problem. Computational experiments are conducted first to verify the methodology with two synthetic data sets (one of which is Sioux Falls), followed by a validation of the method using bus data from Qingpu District in Shanghai, China, with 4 bus lines, 120 segments, 55 bus stops, and 120 one-minute intervals. The estimation model converged to 0.005 tolerance of relative change in 10 iterations. The estimated average of segment flows are only 2.5% off from the average of the observed segment flows; relative errors among segments are 42.5%.