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Clusters of Driving Behavior From Observational Smartphone Data

机译:观察智能手机数据的驾驶行为集群

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摘要

Understanding driving behaviors is essential for improving safety andmobility of our transportation systems. Data is usually collected viasimulator-based studies or naturalistic driving studies. Those techniques allowfor understanding relations between demographics, road conditions and safety.On the other hand, they are very costly and time consuming. Thanks to theubiquity of smartphones, we have an opportunity to substantially complementmore traditional data collection techniques with data extracted from phonesensors, such as GPS, accelerometer gyroscope and camera. We developedstatistical models that provided insight into driver behavior in the SanFrancisco metro area based on tens of thousands of driver logs. We used noveldata sources to support our work. We used cell phone sensor data drawn fromfive hundred drivers in San Francisco to understand the speed of traffic acrossthe city as well as the maneuvers of drivers in different areas. Specifically,we clustered drivers based on their driving behavior. We looked at driver normsby street and flagged driving behaviors that deviated from the norm.
机译:了解驾驶行为对于提高运输系统的安全性至关重要。数据通常是由Ssimulator的研究或自然主义驾驶研究收集的。这些技术允许了解人口统计,道路条件和安全之间的关系。另一方面,它们是非常昂贵和耗时的。由于智能手机的恒星,我们有机会基本上与传统的数据收集技术相得益彰,通过从充电电道传感器提取的数据,例如GPS,加速度计陀螺仪和相机。我们开发了统计模型,在Sanfrancisco地铁地区的驾驶员行为中提供了基于成千上万的驱动器日志。我们使用Noveldata来源来支持我们的工作。我们使用旧金山的手机传感器数据从旧金山绘制了百名司机,了解交通Actosshe城市的速度以及不同领域的司机的演习。具体而言,我们基于其驾驶行为进行聚集的驱动程序。我们看着司机Normsby Street和标记偏离常态的行为行为。

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