Validating Rail Transit Assignment Models with Cluster Analysis and Automatic Fare Collection Data

Abstract

Passenger flow data are necessary for making and coordinating operational plans for urban rail transit (URT) systems; the availability and the service state of those systems directly influence the activity of a city and its people. Although many transit assignment models have been developed, the results of passenger flows estimated by these models as well as assumptions made in the estimation process, especially for large-scale, complex, and dynamically changing URT networks, had not been validated. This paper proposes a methodology that can validate existing URT assignment models by using automatic fare collection data and a cluster analysis technique. Initial applications to the URT system of Shanghai, China, which is one of the largest in the world, show that the proposed approach works well and can efficiently find the origin–destination pairs in which passengers' route choices are misestimated by those assignment models. The analysis suggests that several factors result in errors (for the URT assignment model used in Shanghai). These factors include the threshold for the difference in travel costs, a misrepresentation of the transferring cost, and inadequate values for the standard deviation. This information is useful for detecting errors in existing URT assignment models, leading to improvements.

Cluster Analysis Based Arc Detection in Pantograph-Catenary System

“…The clustering approach adopted here is based on the fact that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from the points with higher densities [17,18]. For each data point , we compute two quantities: local density and the distance from the points with higher density.…”

Optimizing Passenger Flow Control and Bus‐Bridging Service for Commuting Metro Lines

“…To validate existing metro flow assignment models, Zhu et al. () further developed a methodology based on automatic fare collection data and a cluster analysis technique. It is found that the accuracy of flow assignment depends on whether travel cost and transferring cost can be determined accurately.…”

“…Path candidates for each origin-destination pair are first generated, and the proposed calibration method subsequently assigns the detailed passenger flow over the metro network. To validate existing metro flow assignment models, Zhu et al (2015) further developed a methodology based on automatic fare collection data and a cluster analysis technique. It is found that the accuracy of flow assignment depends on whether travel cost and transferring cost can be determined accurately.…”

A Bayesian model to improve transit assignment using smart card data

“…In recent years, many transit assignment models have been proposed, and comprehensive reviews have been presented in the literature (Oliker and Bekhor, 2018;Yao et al, 2017;Patriksson, 2015;Saw et al, 2014;Imran and Matthews, 2015). Over time, researchers and experts have acknowledged that these models must be validated, especially for those largescale and multimodal transit networks (Zhu et al, 2015).…”

“…Agard et al, 2013;Asakura et al, 2012;Kusakabe and Asakura, 2014;Zhao et al, 2017a), journey time reliability(Uniman et al, 2010;Rahbar et al, 2017;Bagherian et al, 2016;Ma et al, 2017b), trip purposes(Lee and Hickman, 2014b;Trépanier et al, 2007;Long and Thill, 2015;Lee and Hickman, 2014a), passenger flow on different routesRahbar et al, 2017;Zhu et al, 2014;Zhu et al, 2015;Zhu et al, 2017), etc Pelletier et al (2011b). present a comprehensive review of uses of smart card…”