Year: 2025
Type: article
Abstract: With the increasing digitalization and connectivity of transportation systems, there are many opportunities for data-based approaches in transportation planning and management. In this context, data quality management has a special role to play, including the systematic quality assessment of data assets. Data quality is particularly crucial for emerging data that has not yet been widely researched from a quality perspective. Emerging data is often found in Smart Parking Systems (SPSs). Currently, it remains unclear how SPS-generated data can be exploited by potential data consumers, such as municipal parking managers. One reason is the lack of knowledge about the quality of available data sources and the data provided. This paper presents an approach to assessing and defining data quality in the field of on-street parking. It examines relevant quality issues in this field and consolidates the findings into relevant quality indicators. The methodology includes a cross-check analysis of data sources and an inductive taxonomy development. The cross-check analysis provided empirical findings through qualitative analyses of available parking data in Hamburg, Germany, considering various conventional and SPS-based data sources. Based on this, a set of relevant quality criteria and quality metrics was developed.
Source: Future Transportation
Author: Peter Lubrich
Institution: Federal Highway and Transport Research Institute
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,207
Funder: European Commission
Award: G2135434561
OpenAlex ID: W4413874938