Traffic data is essential for intelligent traffic management and road maintenance. However, the enormous effort used for data collection and analysis, combined with conventional approaches for traffic monitoring, is inefficient due to its high energy consumption, high cost, and the nonlinear relationships among various factors. This article proposes a new approach to obtain traffic information by processing raw data on pavement vibration. A large amount of raw data was collected in real time by deploying a vibration‐based in‐field pavement monitoring system. The data was processed with an efficient algorithm to achieve the monitoring of the vehicle speed, axle spacing, driving direction, location of the vehicle, and traffic volume. The vehicle speed and axle spacing were back‐calculated from the collected data and verified with actual measurements. The verification indicated that a reasonable precision could be achieved using the developed methods. Vehicle types and vehicles with an abnormal weight were identified by a three‐layer artificial neural network and the k‐means++ cluster analysis, respectively, which may help law enforcement in determining on an overweight penalty. A cost and energy consumption estimation of an acceleration sensing node is discussed. An upgraded system with low cost, low energy consumption, and self‐powered monitoring is also discussed for enabling future distributed computing and wireless application. The upgraded system might enhance integrated pavement performance and traffic monitoring.

中文翻译：

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使用路面振动监测数据收集全面的交通信息

交通数据对于智能交通管理和道路维护至关重要。然而，由于其高能耗，高成本以及各种因素之间的非线性关系，用于数据收集和分析的巨大努力与常规的交通监控方法相结合，效率低下。本文提出了一种通过处理路面振动原始数据来获取交通信息的新方法。通过部署基于振动的现场路面监控系统，实时收集了大量原始数据。使用高效算法对数据进行处理，以实现对车速，轴距，行驶方向，车辆位置和交通量的监控。从收集到的数据中反算出车速和轴距，并通过实际测量进行了验证。验证表明，使用所开发的方法可以达到合理的精度。车辆类型和重量异常的车辆通过三层人工神经网络进行识别，并且k均值++聚类分析，分别可以帮助执法机构确定超重罚款。讨论了加速度感测节点的成本和能量消耗估计。还讨论了具有低成本，低能耗和自供电监控功能的升级系统，以支持将来的分布式计算和无线应用。升级后的系统可能会增强集成的路面性能和交通监控。