Behaviourally informed bicycle route assignment for activity-based and agent-based travel demand modelling

Cycling network evaluation and agent-based transport simulations commonly rely on shortest-path routing, implicitly assuming that cyclists minimise travel distance. However, empirical evidence shows that cycling route choice reflects trade-offs between safety, comfort, infrastructure quality, and topography. This study develops a behaviourally informed cycling routing framework that integrates public participation GIS (PPGIS) based route-choice modelling with agent-based simulation. Marginal utilities estimated using a Path Size Logit (PSL) model are transformed into link-level impedance factors and embedded within the agent-based transport simulation model MATSim, enabling cyclists’ behavioural preferences to directly influence network-wide route assignment while holding travel demand constant. The framework is evaluated against both shortest-path routing and observed cycling routes using the same origin-destination pairs. Results show that impedance-based routing more closely reproduces observed route characteristics, particularly in terms of exposure to low-stress links, speed environments, and cycling infrastructure use. At the network level, behaviourally informed routing increases low-stress exposure by 31.4% and reduces high-stress exposure by 41.5%, while the use of off-road and protected cycling facilities increases by 118.5%. Average exposure to higher-speed traffic environments decreases by 21.3%, accompanied by a modest 3.9% increase in trip length. Embedding behavioural impedance within the agent-based model also substantially alters the emergent exposure of cyclists to motorised traffic, reducing total network-wide exposure by up to 43.5% relative to shortest-path assignment and redistributing cycling flows away from high-speed arterial corridors toward lower-stress alternatives. These findings demonstrate that conventional shortest-path routing in agent-based models can systematically misrepresent cyclist exposure and infrastructure utilisation. The proposed framework provides a practical method for integrating behavioural evidence into agent-based cycling routing, enabling more realistic evaluation of cycling networks, safety outcomes, and infrastructure investments.