Assessing spatial and temporal transferability of cycleway impact models for bike-share usage in London.

Assessing spatial and temporal transferability in models of cycleway impacts can inform evidence-based transport policies and wider implementation of cycling infrastructure. Yet, limited research on model transferability constrains their practical application in infrastructure planning. This study examines spatial and temporal transferability of models estimating the effects of cycleway implementation on bike-share usage. Using 13 years of fortnightly data from London's bike-share scheme across Cycleways 1, 3, and 6 (over 14,000 time-station observations), we combine three modelling frameworks (Autoregressive Integrated Moving Average with Exogenous variables [ARIMAX], Generalised Additive Models [GAM], and Generalised Additive Mixed Models with ARMA errors [GAMM+ARMA]) with three transferability strategies (direct transferability, contextual calibration, and local re-estimation). Results show that contextual calibration generally outperforms the other two strategies, reducing errors by 20-80% compared with direct transferability and by 5-45% compared with local re-estimation. Cycleway interventions are positively associated with bike-share usage, whilst demographic covariates exhibit spatial heterogeneity. These findings highlight the value of partial model adaptation for balancing transferability with local relevance and suggest contextual calibration as a practical strategy for transferable cycleway intervention modelling. This study provides insights for transport authorities to prioritise investment, scale cycling infrastructure efficiently, and adapt successful interventions to diverse urban contexts.