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A procedure for public transit OD matrix generation using smart card transaction data

A procedure for public transit OD matrix generation using smart card transaction data Most fare collection systems are initially installed as single-purpose devices which are only used for collecting fare; however, many transit planners consider them as a rich source of data required for studying the passengers' trip trends. Although, usually, there is no transaction made at the destination stop, making some assumptions can help us infer the destination. In this study, we present an integrated procedure that can generate origin–destination matrices and passenger load profiles as essential tools for public transport planning processes. Moreover, this procedure can be used to detect and analyze trips that include transfers. In an attempt to employ the proposed algorithm in the Tehran bus rapid transit network, 52% of the transactions could be used to trace the trips in an origin–destination format. The trips that include transfers are recognized and analyzed further. Our detailed results of the method application indicate that the proposed algorithm is a productive and economical public transport planning method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Public Transport Springer Journals

A procedure for public transit OD matrix generation using smart card transaction data

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Publisher
Springer Journals
Copyright
Copyright © Springer-Verlag GmbH Germany, part of Springer Nature 2020
ISSN
1866-749X
eISSN
1613-7159
DOI
10.1007/s12469-020-00257-7
Publisher site
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Abstract

Most fare collection systems are initially installed as single-purpose devices which are only used for collecting fare; however, many transit planners consider them as a rich source of data required for studying the passengers' trip trends. Although, usually, there is no transaction made at the destination stop, making some assumptions can help us infer the destination. In this study, we present an integrated procedure that can generate origin–destination matrices and passenger load profiles as essential tools for public transport planning processes. Moreover, this procedure can be used to detect and analyze trips that include transfers. In an attempt to employ the proposed algorithm in the Tehran bus rapid transit network, 52% of the transactions could be used to trace the trips in an origin–destination format. The trips that include transfers are recognized and analyzed further. Our detailed results of the method application indicate that the proposed algorithm is a productive and economical public transport planning method.

Journal

Public Transport – Springer Journals

Published: Oct 21, 2020

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