Validating OpenStreetMap Data for Public Transport Stops Using Street-Level Imagery

Suggested by: Martin Loidl

Short description: Public transport (PT) operators and public authorities typically provide detailed location and timetable information on PT stops. However, data on the physical design, equipment, and immediate surroundings of these stops is often incomplete or entirely missing. This lack of information limits analyses on topics of high practical relevance, such as accessibility for users with disabilities, comfort and safety at stops, or environmental exposure (e.g., shade or sun).
OpenStreetMap (OSM) is widely recognized as a valuable source of transport-related geodata, offering a rich set of tags that describe physical features of PT stops. Yet, the completeness and accuracy of these attributes remain uncertain, particularly in rural areas.
This master thesis focuses on validating OSM data for public transport stops in rural contexts. It involves evaluating the accuracy and completeness of selected stop attributes (e.g., shelter, seating, signage, lighting) by systematically comparing OSM entries with street-level imagery from platforms such as Mapillary or Google Street View. The work includes developing a sampling strategy for rural stops, collecting and analyzing visual evidence, and documenting deviations between mapped information and observed reality. The goal is to assess OSM’s data quality for rural PT infrastructure and identify systematic patterns in missing or incorrect attributes.

The following research questions can guide the analysis:

• Which attributes of public transport stops are most frequently missing or incorrect in OSM?
• How reliable is street-level imagery for validating rural transport infrastructure?
• What sampling approach ensures representative coverage of rural stops?
• How can findings inform strategies for improving OSM data quality?

Related project: This thesis contributes to the SAFARI project, which focuses on identifying mobility barriers for vulnerable population groups. More information is available on the research group’s website.

Start/finish: anytime

Prerequisites/qualifications: Interest in mobility and transport planning, as well as in spatial data analysis. Skills in data management and geospatial analysis are required; scripting and coding skills are advantageous.