CycleRAP Risk Assessment for Urban Cycling in Loja, Ecuador (2025)

Research Hypothesis:
The study hypothesized that urban cyclists in Loja, Ecuador, misalign their subjective risk perception with objectively assessed cycling infrastructure risk. The dataset was generated to quantify objective risk levels along the city’s cycling network and to support analyses of risk perception accuracy.
Data Collection and Structure:
The dataset contains georeferenced risk assessment data for 1,075 cycling network segments in Loja, evaluated using the CycleRAP methodology in 2025. Assessments were conducted at 10-meter intervals and include the following key variables:
• Segment ID – Unique identifier for each evaluated segment.
• Coordinates (Latitude/Longitude) – Location of segment midpoint.
• CycleRAP Risk Level – Categorical risk score (Low, Medium, High, Extreme).
• Traffic Volume and Speed Estimate – Derived from Google Traffic color codes and validated equations for flow-speed inference.
• Pedestrian Density Estimate – Based on land-use type (central/commercial, residential, industrial).
• Infrastructure Characteristics – Lane width, surface quality, slope, intersection presence, traffic separation, and lighting conditions.
How the Data Was Gathered:
• Traffic conditions were monitored using Google Traffic data and local validation counts.
• Pedestrian density was inferred from land-use patterns and in-field observations.
• Infrastructure features (width, slope, intersections) were measured in-field by trained civil engineering students using GPS-enabled mobile devices and documented according to the CycleRAP coding manual.
• All data were compiled and processed in ArcGIS to create a geospatial dataset.
Notable Findings:
Preliminary analysis revealed that:
• 50% of segments were classified as Medium risk, 45% as High risk, 3% as Extreme, and 2% as Low.
• High-risk zones were concentrated along mixed-traffic corridors, while extreme-risk sites were associated with high-speed vehicle interactions and poor cycling separation.
Data Interpretation and Use:
The dataset represents objective cycling infrastructure risk and can be used to:
• Map spatial risk distributions for urban planning.
• Evaluate the alignment between cyclist perception and infrastructure risk.
• Support interventions for cycling safety improvement in Latin American cities with emerging bike networks.
Researchers and policymakers should note that CycleRAP outputs are indicative, as risk scores depend on the accuracy of input data for traffic and pedestrian estimates.