Paper Number

2052

Paper Type

CRP

Abstract

Open data sources have become invaluable resources for smart mobility systems offering real-time data on public transport and mobility patterns to help increase efficiency and user experience. However, the quality of open data remains a significant challenge. Inaccuracies, inconsistencies, data governance issues, interoperability challenges, usability problems, and formatting errors can compromise the effectiveness of smart mobility systems. This paper scrutinizes six fundamental open data quality parameters for smart mobility, in the first research phase. It then performs a comparative analysis of eight open data maturity models in the second phase, revealing gaps and assessing the coverage of identified parameters. Subsequently, in phase 3, these identified gaps are empirically examined through an analysis of 54 real-world datasets in five different countries to ascertain their tangible manifestations in practical settings. By addressing the critical issue of open data quality, this research contributes to the enhancement of data-driven decision-making in smart mobility systems.

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Jun 14th, 12:00 AM

ADDRESSING DATA QUALITY GAPS IN OPEN DATA MATURITY MODELS: A COMPARATIVE STUDY AND REAL-WORLD DATASET ANALYSIS

Open data sources have become invaluable resources for smart mobility systems offering real-time data on public transport and mobility patterns to help increase efficiency and user experience. However, the quality of open data remains a significant challenge. Inaccuracies, inconsistencies, data governance issues, interoperability challenges, usability problems, and formatting errors can compromise the effectiveness of smart mobility systems. This paper scrutinizes six fundamental open data quality parameters for smart mobility, in the first research phase. It then performs a comparative analysis of eight open data maturity models in the second phase, revealing gaps and assessing the coverage of identified parameters. Subsequently, in phase 3, these identified gaps are empirically examined through an analysis of 54 real-world datasets in five different countries to ascertain their tangible manifestations in practical settings. By addressing the critical issue of open data quality, this research contributes to the enhancement of data-driven decision-making in smart mobility systems.

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