  The lack of cycling infrastructure in urban environments hinders the adoption
of cycling as a viable mode for commuting, despite the evident benefits of
(e-)bikes as sustainable, efficient, and health-promoting transportation modes.
Bike network planning is a tedious process, relying on heuristic computational
methods that frequently overlook the broader implications of introducing new
cycling infrastructure, in particular the necessity to repurpose car lanes. In
this work, we call for optimizing the trade-off between bike and car networks,
effectively pushing for Pareto optimality. This shift in perspective gives rise
to a novel linear programming formulation towards optimal bike network
allocation. Our experiments, conducted using both real-world and synthetic
data, testify the effectiveness and superiority of this optimization approach
compared to heuristic methods. In particular, the framework provides
stakeholders with a range of lane reallocation scenarios, illustrating
potential bike network enhancements and their implications for car
infrastructure. Crucially, our approach is adaptable to various bikeability and
car accessibility evaluation criteria, making our tool a highly flexible and
scalable resource for urban planning. This paper presents an advanced
decision-support framework that can significantly aid urban planners in making
informed decisions on cycling infrastructure development.
