数据驱动的城市自行车安全评估方法
A Data-Driven Approach for Assessing Biking Safety in Cities
摘要：
随着世界各地的城市在过去几年中对可持续交通的关注，骑自行车已成为世界各地地方政府的焦点之一。世界各地的城市都在投资自行车基础设施，包括自行车道、自行车停车架、共享（无桩）自行车系统等。然而，将城市居民转变为（常规）自行车用户/通勤者的关键因素之一是安全。在这项工作中，我们利用来自不同城市的自行车事故数据来模拟基于街道级（地理和基础设施）功能的自行车安全。我们的评估表明，我们的模型提供了良好的校准概率，准确地捕捉骑自行车事故的风险。我们进一步进行跨城市的比较，以探讨是否有普遍的功能，涉及到骑自行车的安全。最后，我们讨论并展示了如何利用我们的模型来探索“假设”的情况下，促进政策决策。
英文摘要：
With the focus that cities around the world have put on sustainable transportation during the past few years, biking has become one of the foci for local governments around the world. Cities all over the world invest in bike infrastructure, including bike lanes, bike parking racks, shared (dockless) bike systems etc. However, one of the critical factors in converting city-dwellers to (regular) bike users/commuters is safety. In this work, we utilize bike accident data from different cities to model the biking safety based on street-level (geographical and infrastructural) features. Our evaluations indicate that our model provides well-calibrated probabilities that accurately capture the risk of a biking accident. We further perform cross-city comparisons in order to explore whether there are universal features that relate to cycling safety. Finally, we discuss and showcase how our model can be utilized to explore "what-if" scenarios and facilitate policy decision making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
