This thesis investigates whether traffic patterns captured by a cyclist's camera coincide with moments of rider-reported workload increases, and whether a simple, scalable pipeline using a single forward-facing camera can extract useful signals for workload modelling. We developed an end-to-end system that detects and tracks nearby road users in cyclist point-of-view video, computing traffic features from 984 temporal windows across three urban rides in the city of Delft.

Using a compact set of eight traffic features and logistic regression modelling, we evaluated performance through three complementary approaches. Individual feature analysis revealed consistent directional differences between low and high workload episodes, with six of eight features maintaining the same directional relationships in multivariate analysis. Ranking performance exceeded baseline expectations, achieving within-route ROC-AUC of 0.570 (0.070 above chance) and PR-AUC of 0.450 (0.080 above baseline), meeting established criteria for small effect sizes. Cross-route generalization proved more challenging, with performance dropping to ROC-AUC of 0.517 and PR-AUC of 0.401, though consistently exceeding route-specific baselines across all test routes.

Binary classification demonstrated above-baseline performance, with within-route F1 scores of 0.478 representing a 0.108 improvement over our baseline expectations. Threshold optimization increased F1 to 0.517 by improving recall from 55.0\% to 78.3\%, though cross-route binary performance remained more limited. The traffic patterns associated with high workload episodes point toward defensive cycling scenarios characterized by increased object density, complex motion patterns, and reduced cyclist velocity in dense urban environments.

These findings establish that camera-derived traffic metrics contain detectable workload-related information while highlighting significant constraints for practical deployment. The modest magnitude of detected relationships and limited cross-route transferability indicate that while the approach demonstrates feasibility, substantial development is needed before robust real-world application. This work provides quantitative evidence for traffic-workload relationships in cycling and establishes methodological foundations for future research in automated cycling safety monitoring.