A Data-Driven Multicriteria Bicycle Routing Model for Large-Scale Applications with a Perturbed Extension

Abstract

Bicycle route models often face a trade-off between computational cost and the inclusion of the many variables affecting route choice. We propose a scalable multicriteria routing model designed for large-scale applications like traffic assignment and network design. By extending the weighted shortest path approach, the framework leverages trajectory data alongside optimization and machine learning to estimate edge weights. Improvements are observed across all metrics (up to 13 %) compared to the distance-based shortest path, with the neural network performing best among the learning models. These cost estimation models are network-agnostic; thus, they can adapt to altered edge attributes and are transferable to unseen regions as they rely on open data. Moreover, a perturbed variant is presented to enhance aggregate bicycle count accuracy. By avoiding costly choice-set construction, this framework provides a computationally efficient, data-driven solution for bicycle route generation.