Increasing the modal share of bicycle traffic to reduce carbon emissions, reduce urban car traffic, and to improve the health of citizens, requires a shift away from car-centric city planning. For this, traffic planners often rely on simulation tools such as SUMO which allow them to study the effects of construction changes before implementing them. Similarly, studies of vulnerable road users, here cyclists, also use such models to assess the performance of communication-based road traffic safety systems. The cyclist model in SUMO, however, is very imprecise as SUMO cyclists behave either like slow cars or fast pedestrians, thus, casting doubt on simulation results for bicycle traffic. In this paper, we analyze acceleration, deceleration, velocity, and intersection left-turn behavior of cyclists in a large dataset of real world cycle tracks. We use the results to improve the existing cyclist model in SUMO and add three more detailed cyclist models and implement them in SUMO.

翻译：为减少碳排放、缓解城市汽车交通压力并提升市民健康水平，增加自行车交通模式占比需要转变以汽车为中心的城市规划理念。为此，交通规划者常依赖SUMO等仿真工具，以便在实施建设变更前研究其影响。同样地，针对弱势道路使用者（本文指骑行者）的研究也采用此类模型评估基于通信的道路交通安全系统性能。然而，SUMO中的自行车模型存在显著缺陷——其模拟的骑行行为要么类似低速汽车，要么类似快速行人，这导致自行车交通仿真结果的可信度受到质疑。本文基于大规模真实世界自行车道数据集，系统分析了骑行者的加速、减速、速度及交叉口左转行为特征。利用分析结果，我们改进了SUMO现有自行车模型，并新增三种精细化自行车行为模型，最终在SUMO平台中实现了这些模型的集成。