Fast and accurate short-term traffic volume prediction is the core content of ITS. However, the traditional prediction methods for a certain road section cannot provide useful information for urban traffic management. This study proposes a method to predict the traffic flow distribution of the road network for a period of time in the future. K-means clustering with dynamic time warping algorithm (DTW) is used to identify the characteristics of time series of traffic flow. Artificial neutral network (ANN) is trained to predict traffic flow of the whole network. The short-term traffic flow prediction model of urban road network based on DTW and ANN is established and compared with the ARIMA, the ANN model, and the emerging LSTM. DTW-ANN model has better prediction effect in data sets of various intervals and can effectively predict the overall traffic distribution of urban road network.
Short-Time Traffic Forecasting of Urban Road Network: An ANN Model Based on DTW Clustering
19th COTA International Conference of Transportation Professionals ; 2019 ; Nanjing, China
CICTP 2019 ; 6070-6082
2019-07-02
Conference paper
Electronic Resource
English
Clustering Based RBF Neural Network Model for Short-Term Freeway Traffic Volume Forecasting
British Library Conference Proceedings | 1998
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