Control and Management Engineering "Antonio Ruberti", Department of Computer, University of Rome "La Sapienza", Via Ariosto 25, 00185 Rome, Italy.
Sensors (Basel). 2020 Dec 17;20(24):7228. doi: 10.3390/s20247228.
In the last few years, with the exponential diffusion of smartphones, services for turn-by-turn navigation have seen a surge in popularity. Current solutions available in the market allow the user to select via an interface the desired transportation mode, for which an optimal route is then computed. Automatically recognizing the transportation system that the user is travelling by allows to dynamically control, and consequently update, the route proposed to the user. Such a dynamic approach is an enabling technology for multi-modal transportation planners, in which the optimal path and its associated transportation solutions are updated in real-time based on data coming from (i) distributed sensors (e.g., smart traffic lights, road congestion sensors, etc.); (ii) service providers (e.g., car-sharing availability, bus waiting time, etc.); and (iii) the user's own device, in compliance with the development of smart cities envisaged by the 5G architecture. In this paper, we present a series of Machine Learning approaches for real-time Transportation Mode Recognition and we report their performance difference in our field tests. Several Machine Learning-based classifiers, including Deep Neural Networks, built on both statistical feature extraction and raw data analysis are presented and compared in this paper; the result analysis also highlights which features are proven to be the most informative ones for the classification.
在过去的几年中，随着智能手机的指数级扩散，逐向导航服务越来越受欢迎。目前市场上提供的解决方案允许用户通过界面选择所需的交通方式，然后为其计算最佳路线。自动识别用户所使用的交通系统，可以动态控制并相应地更新向用户建议的路线。这种动态方法是多式联运规划者的一项使能技术，其中最优路径及其相关的交通解决方案可以根据来自以下方面的数据实时更新：（i）分布式传感器（例如，智能交通信号灯、道路拥堵传感器等）；（ii）服务提供商（例如，汽车共享可用性、公共汽车等待时间等）；以及（iii）用户自己的设备，以符合 5G 架构所设想的智慧城市的发展。在本文中，我们提出了一系列用于实时交通模式识别的机器学习方法，并报告了它们在我们的现场测试中的性能差异。本文介绍并比较了基于统计特征提取和原始数据分析的几种基于机器学习的分类器，包括深度神经网络；分析结果还突出显示了哪些特征对分类最有信息。
