Copenhagen Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark.
Department of Economics, University of Copenhagen, Copenhagen, Denmark.
PLoS One. 2020 Jul 2;15(7):e0234003. doi: 10.1371/journal.pone.0234003. eCollection 2020.
Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications.
了解人们使用的交通方式对于智慧城市和规划者来说至关重要，这样他们才能更好地为市民服务。我们表明，利用无处不在的 Wi-Fi 接入点和蓝牙设备的信息可以增强 GPS 和地理信息，从而提高智能手机上的交通检测能力。Wi-Fi 信息还可以改善交通方式的识别，并有助于节省电池，因为它已经被大多数手机收集。我们的方法使用机器学习方法从预处理数据中确定模式。这种方法的总体准确率为 89%，对于推断自供电、基于汽车和公共交通三种分组模式的平均 F1 得分为 83%。当按个别模式细分时，Wi-Fi 功能可提高与 GPS 功能相比，对公共汽车旅行、火车旅行和驾驶的检测准确性，并且可以替代 GIS 功能而不会降低性能。我们的研究结果表明，Wi-Fi 和蓝牙在城市交通研究中可能很有用，例如通过改进移动出行调查和城市感应应用。
