An Aggregation Approach to Short-Term Traffic Flow Prediction

By Manchun Tan, S. Wong, Jian-min Xu, Zhan-Rong Guan, Peng Zhang. IEEE transactions on intelligent transportation systems (Print), 2009

333 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Short-term traffic flow prediction using seasonal ARIMA model with limited input data (2015, methodology): “[11] used a time series of traffic flow”
Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm (2012, background): “…fuzzy filter performs the clustering operation on traffic flow data and provides a rough prediction, which is the input of the feedforward NN. Accurate short-term traffic flow forecasting is produced by the feedforward NN, which utilizes each cluster as input for modeling the input–output relation.”
Data-Driven Intelligent Transportation Systems: A Survey (2011, methodology): “[76] fitted three models using three traffic flow sets and utilized neural network as a data aggregation model to combine the prediction results of the three models.”
Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning (2014, background): “As we know, a transportation system is a highly correlated network [29].”
Traffic Flow Prediction With Big Data: A Deep Learning Approach (2015, methodology): “The MA, ES, and ARIMA models were used to obtain three relevant time series that were the basis of the NN in the aggregation stage [35].”