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.研究假设： 本研究假设厄瓜多尔洛哈市的城市骑行者，其主观风险感知与客观评估的骑行基础设施风险存在偏差。本数据集旨在量化该市骑行网络沿线的客观风险等级，并为风险感知准确性的相关分析提供支撑。 数据收集与结构： 本数据集包含洛哈市1075条骑行网络路段的地理参考风险评估数据，于2025年采用CycleRAP方法完成评估。评估以10米为间隔开展，涵盖以下核心变量： • 路段ID：每条待评估路段的唯一标识符。 • 坐标（纬度/经度）：路段中点的地理位置。 • CycleRAP风险等级：分类式风险评分（低、中、高、极高）。 • 交通流量与速度估算：基于谷歌交通（Google Traffic）颜色编码及流量-速度推演验证公式得出。 • 行人密度估算：依据土地利用类型（中央商业区、住宅区、工业区）推导而来。 • 基础设施特征：车道宽度、路面质量、坡度、交叉口存在情况、交通隔离状况及照明条件。 数据采集方式： • 交通状况通过谷歌交通（Google Traffic）数据与本地验证计数进行监测。 • 行人密度通过土地利用模式与实地观测进行推断。 • 基础设施特征（宽度、坡度、交叉口）由经过培训的土木工程专业学生借助搭载GPS的移动设备在实地测量，并按照CycleRAP编码手册进行记录。 • 所有数据均在ArcGIS中进行整合与处理，最终生成地理空间数据集。 主要发现： 初步分析结果显示： • 50%的路段被归类为中等风险，45%为高风险，3%为极高风险，2%为低风险。 • 高风险区域集中在混合交通走廊，而极高风险路段则与高速车辆交互及骑行隔离设施不完善相关。 数据解读与用途： 本数据集代表客观的骑行基础设施风险，可用于： • 绘制城市骑行空间风险分布图，辅助城市规划。 • 评估骑行者感知与基础设施风险的匹配程度。 • 为拉丁美洲新兴骑行网络城市的骑行安全改善干预措施提供支撑。 研究人员与政策制定者需注意：CycleRAP输出结果仅为参考性指标，因为风险评分取决于交通与行人估算输入数据的准确性。