Measuring positive public transit accessibility using big transit data

Most of the current existing accessibility measures quantify the potential of reaching desirable opportunities across space and time. Nevertheless, these potential measurements only illustrate the maximum possible accessibility a person can have, which may not accurately measure real-world transit a...

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Autores principales:	Tong Zhang, Wenyuan Zhang, Zhenxuan He
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	public transit positive accessibility smart card data spatio-temporal Mathematical geography. Cartography GA1-1776 Geodesy QB275-343
Acceso en línea:	https://doaj.org/article/6062ccb9cf1a4608840416676452f044
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spelling	oai:doaj.org-article:6062ccb9cf1a4608840416676452f0442021-11-04T15:00:41ZMeasuring positive public transit accessibility using big transit data1009-50201993-515310.1080/10095020.2021.1993754https://doaj.org/article/6062ccb9cf1a4608840416676452f0442021-10-01T00:00:00Zhttp://dx.doi.org/10.1080/10095020.2021.1993754https://doaj.org/toc/1009-5020https://doaj.org/toc/1993-5153Most of the current existing accessibility measures quantify the potential of reaching desirable opportunities across space and time. Nevertheless, these potential measurements only illustrate the maximum possible accessibility a person can have, which may not accurately measure real-world transit accessibility in urban areas. This paper introduces a novel methodology to measure positive public transit accessibility based on multi-source big public transit data such as Smart Card Data (SCD) and Global Navigation Satellite System trajectory data, which embed rich travel information and real-world spatio-temporal constraints. First, we use multi-source transit data to reconstruct trip chains, which are used to extract popular destinations. A novel transit accessibility measure is defined to account for latent trip information such as mode/route preference, opportunity attraction, and travel impedance that are difficult to capture explicitly via traditional normative measures. Finally, we produce accessibility maps to visualize time-varying and heterogeneous accessibility patterns distributed over the study region. We performed an empirical evaluation on real-world transit data collected in Shenzhen City, China, demonstrating the applicability and effectiveness of the proposed method in mapping positive transit accessibility over large metropolitan areas. The results and findings of the empirical study demonstrate that the proposed positive accessibility measure can better capture travel behavior characteristics and constraints than traditional normative measures. The measurement method can be used as a practical high-resolution mapping tool for transit decision makers in evaluating public transit systems, supporting strategic transit planning, and improving daily transit management.Tong ZhangWenyuan ZhangZhenxuan HeTaylor & Francis Grouparticlepublic transitpositive accessibilitysmart card dataspatio-temporalMathematical geography. CartographyGA1-1776GeodesyQB275-343ENGeo-spatial Information Science, Vol 0, Iss 0, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	public transit positive accessibility smart card data spatio-temporal Mathematical geography. Cartography GA1-1776 Geodesy QB275-343
spellingShingle	public transit positive accessibility smart card data spatio-temporal Mathematical geography. Cartography GA1-1776 Geodesy QB275-343 Tong Zhang Wenyuan Zhang Zhenxuan He Measuring positive public transit accessibility using big transit data
description	Most of the current existing accessibility measures quantify the potential of reaching desirable opportunities across space and time. Nevertheless, these potential measurements only illustrate the maximum possible accessibility a person can have, which may not accurately measure real-world transit accessibility in urban areas. This paper introduces a novel methodology to measure positive public transit accessibility based on multi-source big public transit data such as Smart Card Data (SCD) and Global Navigation Satellite System trajectory data, which embed rich travel information and real-world spatio-temporal constraints. First, we use multi-source transit data to reconstruct trip chains, which are used to extract popular destinations. A novel transit accessibility measure is defined to account for latent trip information such as mode/route preference, opportunity attraction, and travel impedance that are difficult to capture explicitly via traditional normative measures. Finally, we produce accessibility maps to visualize time-varying and heterogeneous accessibility patterns distributed over the study region. We performed an empirical evaluation on real-world transit data collected in Shenzhen City, China, demonstrating the applicability and effectiveness of the proposed method in mapping positive transit accessibility over large metropolitan areas. The results and findings of the empirical study demonstrate that the proposed positive accessibility measure can better capture travel behavior characteristics and constraints than traditional normative measures. The measurement method can be used as a practical high-resolution mapping tool for transit decision makers in evaluating public transit systems, supporting strategic transit planning, and improving daily transit management.
format	article
author	Tong Zhang Wenyuan Zhang Zhenxuan He
author_facet	Tong Zhang Wenyuan Zhang Zhenxuan He
author_sort	Tong Zhang
title	Measuring positive public transit accessibility using big transit data
title_short	Measuring positive public transit accessibility using big transit data
title_full	Measuring positive public transit accessibility using big transit data
title_fullStr	Measuring positive public transit accessibility using big transit data
title_full_unstemmed	Measuring positive public transit accessibility using big transit data
title_sort	measuring positive public transit accessibility using big transit data
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/6062ccb9cf1a4608840416676452f044
work_keys_str_mv	AT tongzhang measuringpositivepublictransitaccessibilityusingbigtransitdata AT wenyuanzhang measuringpositivepublictransitaccessibilityusingbigtransitdata AT zhenxuanhe measuringpositivepublictransitaccessibilityusingbigtransitdata
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Measuring positive public transit accessibility using big transit data

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