Bike usage in Smart Cities is paramount for sustainable urban development: cycling promotes healthier lifestyles, lowers energy consumption, lowers carbon emissions, and reduces urban traffic. However, the expansion and increased use of bike infrastructure has been accompanied by a glut of bike accidents, a trend jeopardizing the urban bike movement. This paper leverages data from a diverse spectrum of sources to characterise geolocated bike accident severity and, ultimately, study cycling risk and discomfort. Kernel density estimation generates a continuous, empirical, spatial risk estimate which is mapped in a case study of Zürich city. The roles of weather, time, accident type, and severity are illustrated. A predominance of self-caused accidents motivates an open-source software artifact for personalized route recommendations. This software is used to collect open baseline route data that are compared with alternative routes minimizing risk and discomfort. These contributions have the potential to provide invaluable infrastructure improvement insights to urban planners, and may also improve the awareness of risk in the urban environment among experienced and novice cyclists alike.

中文翻译：

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关于骑自行车的风险和不适：城市安全地图和自行车路线建议

智能城市中的自行车使用对于可持续城市发展至关重要：骑自行车促进更健康的生活方式，降低能源消耗，减少碳排放，并减少城市交通。然而，自行车基础设施的扩张和使用的增加伴随着自行车事故的泛滥，这种趋势危及城市自行车运动。本文利用来自不同来源的数据来描述地理定位自行车事故的严重程度，并最终研究骑行风险和不适。核密度估计生成连续的、经验性的空间风险估计，该估计在苏黎世市的案例研究中得到了映射。说明了天气、时间、事故类型和严重性的作用。主要是自发事故促使开源软件工件用于个性化路线推荐。该软件用于收集开放基线路线数据，这些数据与替代路线进行比较，最大限度地减少风险和不适。这些贡献有可能为城市规划者提供宝贵的基础设施改善见解，也可能提高经验丰富和新手骑自行车者对城市环境风险的认识。