Analyzing OpenStreetMap as data source for travel demand models A case study in Karlsruhe

Briem, Lars; Heilig, Michael; Klinkhardt, Christian; Vortisch, Peter

doi:10.1016/j.trpro.2019.09.021

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…Depending on the region of interest, preprocessed data are updated once a week (3). For example, Briem et al (4) found that locations open to the public are kept up-to-date. Changes are made on a daily basis unlike the annual statistics of public authorities where changes only appear in the next edition.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In contrast, locations that are not open to the public are maintained less frequently. Any assessment of workplaces is, therefore, distorted because many small offices, which are not open to the public, are missing from OSM (4). Having the data maintained by enthusiasts is an advantage in many regions, but in others it is less so, especially if there are only a few active volunteers.…”

Section: Literature Reviewmentioning

confidence: 99%

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Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

Klinkhardt

Woerle

Briem

et al. 2021

Transportation Research Record: Journal of the Transportation R

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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 plausibility checks should be done to ensure a certain quality. Further, we present a methodology for calculating attractiveness measures for typical activities from single POIs and national trip generation guidelines. We show that the quality of these calculated measures is good enough for them to be used in travel demand models. Using our approach, therefore, allows the quick, automated, and flexible generation of attractiveness measures for travel demand models.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

Klinkhardt

Woerle

Briem

et al. 2021

Transportation Research Record: Journal of the Transportation R

Self Cite

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“…e road network data of streets, county roads, provincial roads, national roads, highspeed road networks, and railways in the core urban area of Zhengzhou City was also obtained from the Baidu network map [47]. Based on the principle of topology analysis, the redundant roads and small roads in the road network data are first eliminated.…”

Section: E Road Network Datamentioning

confidence: 99%

“…en, the road network data are topologically edited. Finally, the road network map of Zhengzhou core urban area is obtained [47,48]. According to the national four-level highway width standards and the road width data of Zhengzhou City, the highways, provincial highways, national highways, railways, county highways, and streets in the road network were widened by 55 m, 45 m, 45 m, 40 m, 35 m, and 25 m, respectively.…”

Section: E Road Network Datamentioning

confidence: 99%

Identification of Urban Functional Area by Using Multisource Geographic Data: A Case Study of Zhengzhou, China

Xie

Zhao

et al. 2021

Complexity

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The rational allocation of functional areas is the foundation for addressing the sustainable development of cities. Efficient and accurate identification methods of urban functional areas are of great significance to the adjustment and testing of urban planning and industrial layout optimization. Firstly, by employing multisource geographic data, an identification method of urban functional areas was developed. A quantitative measurement approach of the urban functional area was then established considering the comprehensive effects of human-land, space-time, and thematic information to present the covering area of ground objects, public awareness, and empirical research. Finally, the Zhengzhou city, which locates in Henan province of central China, was used to test the method. The results show that the developed method is efficient, accurate, and universal and can identify urban functional areas quickly and accurately. We found that the overall distribution of Zhengzhou’s functional areas presents a spatial pattern of single and multimixed coordinated development. The city’s commercial functional areas and commercial-based mixed functional areas are located in the city’s central area. The green square’s function area occupies relatively low and is mainly distributed in the city’s fringe.

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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%

Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

Yeow

Low

Tan

et al. 2021

IJGI

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Point-of-interest (POI) data from map sources are increasingly used in a wide range of applications, including real estate, land use, and transport planning. However, uncertainties in data quality arise from the fact that some of this data are crowdsourced and proprietary validation workflows lack transparency. Comparing data quality between POI sources without standardized validation metrics is a challenge. This study reviews and implements the available POI validation methods, working towards identifying a set of metrics that is applicable across datasets. Twenty-three validation methods were found and categorized. Most methods evaluated positional accuracy, while logical consistency and usability were the least represented. A subset of nine methods was implemented to assess four real-world POI datasets extracted for a highly urbanized neighborhood in Singapore. The datasets were found to have poor completeness with errors of commission and omission, although spatial errors were reasonably low (<60 m). Thematic accuracy in names and place types varied. The move towards standardized validation metrics depends on factors such as data availability for intrinsic or extrinsic methods, varying levels of detail across POI datasets, the influence of matching procedures, and the intended application of POI data.

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Analyzing OpenStreetMap as data source for travel demand models A case study in Karlsruhe

Cited by 8 publications

References 6 publications

Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

Identification of Urban Functional Area by Using Multisource Geographic Data: A Case Study of Zhengzhou, China

Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

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