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.

翻译：尽管（电动）自行车作为可持续、高效且有益健康的交通方式具有明显优势，但城市环境中自行车基础设施的缺乏阻碍了其作为通勤可行模式的推广。自行车网络规划是一个繁琐的过程，依赖于启发式计算方法，这些方法经常忽视引入新自行车基础设施的更广泛影响，特别是重新规划机动车道的必要性。在本研究中，我们主张优化自行车网络与机动车网络之间的权衡，有效推动帕累托最优。这种视角的转变催生了一种面向最优自行车网络分配的新型线性规划模型。我们使用真实数据和合成数据进行的实验验证了该优化方法相较于启发式方法的有效性和优越性。特别地，该框架为利益相关者提供了一系列车道重新分配方案，阐明了自行车网络的潜在改进及其对机动车基础设施的影响。至关重要的是，我们的方法能适应不同的自行车友好度和机动车可达性评估标准，使得我们的工具成为城市规划中高度灵活且可扩展的资源。本文提出了一种先进的决策支持框架，可显著帮助城市规划者在自行车基础设施发展方面做出明智决策。