Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison
Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison
复制标题
通过去噪方案和 ANN 感知数据支持交通流预测：比较
DOI：
10.1109/jsen.2020.3007809
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发表时间：
2020-12-01
影响因子：
4.3
通讯作者：
Zhao, Jiansen
中科院分区：
文献类型：
--
作者：
Chen, Xinqiang;Wu, Shubo;Zhao, Jiansen
短时交通流预测是智能交通系统（ITS）的重要组成部分，为交通规划、交通管理与控制、道路安全评估、能耗估算等提供支持。广泛部署的交通传感器为我们提供了大量连续的交通流数据，其中可能包含由于传感器故障而导致的异常样本。研究的主要目的是评估使用各种平滑模型清除交通流数据中的异常，并进一步处理，以预测短期交通流的演变与人工神经网络。对小波滤波器、移动平均模型和巴特沃思滤波器进行了仔细测试，以平滑收集的环路检测器数据。在此基础上，引入人工神经网络对不同时段的交通流进行预测，并采用常用的评价指标对预测结果进行定量分析。研究结果为短期交通流预测提供了高效、准确的去噪方法。
Short-term traffic flow prediction plays a key role of Intelligent Transportation System (ITS), which supports traffic planning, traffic management and control, roadway safety evaluation, energy consumption estimation, etc. The widely deployed traffic sensors provide us numerous and continuous traffic flow data, which may contain outlier samples due to expected sensor failures. The primary objective of the study was to evaluate the use of various smoothing models for cleaning anomaly in traffic flow data, which were further processed to predict short term traffic flow evolution with artificial neural network. The wavelet filter, moving average model, and Butterworth filter were carefully tested to smooth the collected loop detector data. Then, the artificial neural network was introduced to predict traffic flow at different time spans, which were quantitatively analyzed with commonly-used evaluation metrics. The findings of the study provide us efficient and accurate denoising approaches for short term traffic flow prediction.