Understanding User Applications and Indicators for Smart Talking Bicycle Data
A literature review for the application of RingRing and Tracefy data
V.I. Nijholt (TU Delft - Civil Engineering & Geosciences)
Y. Yuan – Mentor (Transport and Planning)
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Abstract
This study explores data-driven bicycle policy development using “Talking Bikes” data from RingRing and Tracefy. These datasets contain high-resolution GPS-based trajectory data of cyclists, including position, speed, direction, and trip information. The goal of the study is to improve data quality and to explore potential policy applications of large-scale bicycle mobility data.
Data quality improvements include map-matching, interpolation and extrapolation of trajectories, and outlier detection. In addition, multiple use cases are developed to demonstrate how processed cycling data can support urban mobility policy. These include real-time traffic monitoring, area-based network utilisation (GGB+), transport demand estimation, network design, and policy evaluation.
From these applications, key performance indicators (KPIs) are derived across categories such as accessibility, safety, reliability, health, environment, and equity. Examples include flow, speed, travel time, route choice, stops, incident risk, and exposure. These indicators enable detailed analysis of cycling behaviour in different temporal and spatial contexts, such as peak versus off-peak hours and weekday versus weekend patterns.
The study demonstrates that high-resolution bicycle data can support a wide range of policy applications, from real-time traffic management to long-term infrastructure planning. By improving data quality and systematically structuring mobility indicators, Talking Bikes data can provide valuable insights for more effective and evidence-based bicycle policy development.