Crowdsourced Highway Network Data in China: A Multi‐Dimensional Quality Assessment and Analysis of Influencing Factors in OpenStreetMap

ABSTRACT With the rapid development of digital technologies and participatory mapping platforms, crowdsourced geographic data has emerged as a valuable and widely adopted resource for transportation research and urban planning. Broadly, crowdsourced geographic data refers to geospatial datasets collected, edited, and shared by the general public via a crowdsourced contribution model, which is a core form of Volunteered Geographic Information (VGI). Crowdsourced highway network data, as a key application subset of crowdsourced geographic data, is the core research object of this study. OpenStreetMap (OSM), as one of the most well‐established and widely used crowdsourced geographic data platforms globally, serves as the primary data source for this work. However, the open, voluntary user‐generated nature of OSM brings inherent challenges to data quality, with commonly noted issues including missing features, geometric deviations, and inaccurate attribute information. To date, most prior studies on OSM road network data quality have predominantly focused on urban areas, where higher contributor density generally supports more stable data quality. In comparison, the quality of highway and intercity road networks has received relatively limited research attention, despite the more prominent quality‐related issues observed in these datasets. Meanwhile, existing related studies in the Chinese context are mostly limited to local or regional spatial scales with short observation periods, and relatively few have established a systematic evaluation framework aligned with international geospatial data quality standards or conducted quantitative analysis of the factors driving spatial and temporal variations in OSM highway data quality. To address these noted gaps in existing research, this study evaluates the quality of OSM highway network data across China, with a spatial scope covering 366 prefecture‐level cities nationwide and a continuous temporal window spanning 2015–2024. By constructing matched crowdsourced datasets and authoritative reference datasets, we develop a comprehensive evaluation framework aligned with international geospatial data quality standards, which covers four key dimensions of data quality: completeness, positional accuracy, logical consistency, and temporal validity. This framework complements existing fragmented, single‐dimensional evaluation approaches by enabling a systematic multi‐faceted assessment of highway network data quality. Based on this framework, we analyze the temporal evolution, spatial clustering patterns, and statistical distribution characteristics of the four quality indicators over the study period. To further explore the drivers of spatial and temporal variations in data quality, we compile a multi‐dimensional set of potential influencing factors, including economic, demographic, cultural, and transportation‐related variables, and conduct corresponding quantitative analysis. This work complements conventional quality assessment by exploring the underlying mechanisms of quality variations, forming a coherent integrated analytical workflow of multi‐dimensional quality evaluation and driving factor attribution. The findings of this study offer empirical insights into the strengths and limitations of crowdsourced highway network data, provide a preliminary empirical reference for its standardized application in transportation research and planning, and may offer supporting references for the development of targeted data quality improvement strategies in open crowdsourced mapping platforms.