2021
DOI: 10.1177/0361198121997415
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Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

Abstract: We present a methodology to extract points of interest (POIs) data from OpenStreetMap (OSM) for application in travel demand models. We use custom taglists to identify and assign POI elements to typical activities used in travel demand models. We then compare the extracted OSM data with official sources and point out that the OSM data quality depends on the type of POI and that it generally matches the quality of official sources. It can therefore be used in travel demand models. However, we recommend that pla… Show more

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Cited by 29 publications
(14 citation statements)
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“…In recent years, despite the lower number of articles, the field developed in several new directions, such as in more sophisticated statistical machine learning methods for quality assessment [24,25] and POI matching [26,27]. Recent studies also explore novel data sources beyond OSM, such as LBSNs and review websites [27,28], and broader applications of POI data [10,29,30]. The relevant approaches applicable to POI data sources were extracted from the surveyed literature and categorized into the six elements of data quality defined by ISO 19157:2013 [31], which are completeness, logical consistency, positional accuracy, temporal quality, thematic accuracy, and usability.…”
Section: Review Of Approaches For Validating Poi Data Qualitymentioning
confidence: 99%
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“…In recent years, despite the lower number of articles, the field developed in several new directions, such as in more sophisticated statistical machine learning methods for quality assessment [24,25] and POI matching [26,27]. Recent studies also explore novel data sources beyond OSM, such as LBSNs and review websites [27,28], and broader applications of POI data [10,29,30]. The relevant approaches applicable to POI data sources were extracted from the surveyed literature and categorized into the six elements of data quality defined by ISO 19157:2013 [31], which are completeness, logical consistency, positional accuracy, temporal quality, thematic accuracy, and usability.…”
Section: Review Of Approaches For Validating Poi Data Qualitymentioning
confidence: 99%
“…Errors can arise from commission, where excess data exists in a dataset, or omission, where data are missing from a dataset. The simplest approach for measuring completeness is to compare the number of points in D eval and in D ref [10,[33][34][35][36]. If a correspondence between points in D eval and in D ref was established, the proportion of points in D eval that are found in D ref , and vice versa, serves as a measure of completeness.…”
Section: Completenessmentioning
confidence: 99%
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