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Fuzzy Logic-based Incident Detection System using Loop Detectors Data

机译：基于环路检测器数据的基于模糊逻辑的事件检测系统

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Vehicle loop detectors or other equipment installed on cross-sections are commonly used for monitoring traffic flow conditions on road network. For operational analysis it is crucial to distinguish between low level of service related to oversaturated conditions and generated by extraordinary events as incidents. In case of incident it is fundamental to have a prompt response in order to activate any requested countermeasure, such as rescue activation and traffic detour. This paper introduces a control system which recognizes incidents from vehicle loop detectors data (system control), and identifies the optimal position of loop detectors (system design).The system was developed using fuzzy logic concepts and calibrated using data from micro simulation experiments. Micro simulation approach is justified from the impossibility to get the requested data from on-field observations. The analysis has been focused on a two-way four-lane freeway basic segment; traffic flow variables (Density, Space Mean Speed and Flow Rate) were estimated with reference to the set of consecutive time intervals (one-minute long) belonging to the whole observation time period (3hours). Simulated data were obtained running the model several times (10 runs) for each traffic volume class adopted in the analysis (1,000, 2,000, 3,000, 3,500 vehicles/hour), with different random number seeds. Calibration dataset was used to determine the knowledge base of each FIS using the open-source software FisPro, and the remaining data (validation dataset) to evaluate the performance of the system. The main finding of the study is that the detection system, despite its simplicity, shows excellent False Alarm Rate and satisfactory Mean Time To Detection.
机译：安装在横截面上的车辆环路检测器或其他设备通常用于监视道路网络上的交通状况。对于运营分析，至关重要的是区分与过饱和条件相关的服务水平低下以及由异常事件作为事件所产生的低水平服务。万一发生事故，必须迅速做出响应，以激活任何请求的对策，例如救援激活和交通traffic回。本文介绍了一种控制系统，该系统可以识别车辆环路探测器数据中的事件（系统控制），并确定环路探测器的最佳位置（系统设计）。该系统是使用模糊逻辑概念开发的，并使用来自微仿真实验的数据进行了校准。微观模拟方法的合理性在于无法从现场观察中获得所需的数据。分析重点放在两向四车道高速公路基本路段;参考属于整个观察时间段（3小时）的一组连续时间间隔（一分钟长）估算交通流量变量（密度，空间平均速度和流速）。对于分析中采用的每种交通量类别（1,000、2,000、3,000、3,500辆/小时），使用模型运行几次（10次）可获得模拟数据，并使用不同的随机数种子。校准数据集用于使用开源软件FisPro确定每个FIS的知识库，其余数据（验证数据集）用于评估系统的性能。该研究的主要发现是，尽管检测系统简单，但仍显示出极佳的误报率和令人满意的平均检测时间。

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