Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison

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 traff...

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Bibliographic Details
Published in:IEEE sensors journal 2020-12, Vol.20 (23), p.14317-14328
Main Authors: Chen, Xinqiang, Wu, Shubo, Shi, Chaojian, Huang, Yanguo, Yang, Yongsheng, Ke, Ruimin, Zhao, Jiansen
Format: Article
Language:English
Subjects:
artificial neural network
Artificial neural networks
butterworth filter
Butterworth filters
data denoising
Energy consumption
Intelligent transportation systems
Neural networks
Noise reduction
Outliers (statistics)
Predictive models
Roads
Smoothing methods
Traffic control
Traffic flow
Traffic flow prediction
Traffic management
Traffic planning
Traffic safety
Transportation networks
Transportation planning
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Summary: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.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3007809