Title : ( Improved Public Transport OD Matrix Estimation Using an Enhanced Trip Chain Model with Smart Card Data )
Authors: Hamidreza Koosha , Shariat Radfar , Ali Gholami , Atefeh Amindoust ,Access to full-text not allowed by authors
Abstract
High-quality public transportation necessitates innovative planning and operation strategies. Recent advancements in information technology provide advanced data collection methods. Smart card (SD) transactions offer a detailed and cost-effective approach to estimate public transport network flows. This study constructs zonal origin-destination (OD) matrices for Mashhad’s public transport (bus and subway) by inferring passenger destinations using an enhanced trip chain model based on smart card data. Around 27% of the cards have only one daily transaction and about 69% have two or three daily transactions. Analysis revealed that 52.4% of transactions correspond to trip origins, with the remaining being transfers. A 30-minute transfer window was considered. Uniquely, this study incorporates all year-long transactions, including unlinked trips, for enhanced accuracy. These first-ever OD matrices for Mashhad public transport can inform improved planning and operation strategies for transportation authorities.
Keywords
, Smart card data (SCD), Trip chain model (TCM), Origin-destination matrix, Traffic zones@article{paperid:1104896,
author = {Koosha, Hamidreza and َشریعت رادفر and علی غلامی and عاطفه امین دوست},
title = {Improved Public Transport OD Matrix Estimation Using an Enhanced Trip Chain Model with Smart Card Data},
journal = {International Journal of Intelligent Transportation Systems Research},
year = {2025},
month = {July},
issn = {1348-8503},
keywords = {Smart card data (SCD); Trip chain model (TCM); Origin-destination matrix; Traffic zones},
}
%0 Journal Article
%T Improved Public Transport OD Matrix Estimation Using an Enhanced Trip Chain Model with Smart Card Data
%A Koosha, Hamidreza
%A  َشریعت رادفر
%A  علی غلامی
%A  عاطفه امین دوست
%J International Journal of Intelligent Transportation Systems Research
%@ 1348-8503
%D 2025
            
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