Electrical engineering

Noise reduction

Computer network

Outlier

Data mining

Filter (signal processing)

Engineering

Artificial intelligence

Traffic generation model

Traffic flow (computer networking)

Energy consumption

Computer science

Real-time computing

Computer vision

Smoothing

Wavelet

Artificial neural network

Anomaly detection

IEEE Sensors Journal

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.

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