The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users’ mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution.

流动性数据的海量和不断增加的可用性使研究和预测人类流动行为和活动在各个层面成为可能。在本文中，我们解决了长期预测汽车驾驶员碰撞风险的问题。这是一项非常具有挑战性的任务，需要对驾驶员及其周围环境有深入的了解，但它对公共安全（例如通过指导高风险驾驶员）和保险市场（例如通过调整定价以适应风险）有几个有用的应用。我们使用基于用户移动性的网络表示的数据驱动方法对每个用户进行建模。此外，我们通过定义一组广泛的城市指标来代表用户移动的区域，这些指标涵盖了城市的不同方面。这些指标基于人类的流动性，并根据一组不同的数据源自动计算，包括移动轨迹和道路网络。通过这些城市指标，我们为碰撞风险任务开发了一种地理迁移学习方法，这样我们就可以为另一个没有标记数据的区域建立有效的预测模型。真实数据集的实证结果显示了我们解决方案的优越性。