City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN

Abstract

Traffic congestion is a significant problem faced by large and growing cities that hurt the economy, commuters, and the environment. Forecasting the congestion level of a road network timely can prevent its formation and increase the efficiency and capacity of the road network. However, despite its importance, traffic congestion prediction is not a hot topic among the researcher and traffic engineers. It is due to the lack of high-quality city-wide traffic data and computationally efficient algorithms for traffic prediction. In this paper, we propose (i) an efficient and inexpensive city-wide data acquisition scheme by taking a snapshot of traffic congestion map from an open-source online web service; Seoul Transportation Operation and Information Service (TOPIS), and (ii) a hybrid neural network architecture formed by combing Convolutional Neural Network, Long Short-Term Memory, and Transpose Convolutional Neural Network to extract the spatial and temporal information from the input image to predict the networkwide congestion level. Our experiment shows that the proposed model can efficiently and effectively learn both spatial and temporal relationships for traffic congestion prediction. Our model outperforms two other deep neural networks (Auto-encoder and ConvLSTM) in terms of computational efficiency and prediction performance. INDEX TERMS Convolutional neural network, long short-term memory, partially convolutional neural network, spatiotemporal feature, traffic congestion forecasting, transport network.

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

“…Ranjan et al [41] redict the congestion level of a transportation network by integrating the CNN, transpose CNN, and LSTM. The convolutional encoder as a spatial feature extraction network encodes the input image into a lowresolution latent state.…”

“…The various techniques for predicting the traffic collisions in machine learning are sampling, regressions, correlations [35], clustering algorithms [36,37], k-nearest neighbor (kNN) algorithm [38], and artificial neural network (ANN) [39] are clobbered by the deep learning (DL) models in terms of accuracy in predicting the collision. CNN [40], transpose CNN [41], and long short-term Memory (LSTM) [42] are some of the deep learning techniques [43][44][45][46][47][48] used for predicting the collision [41]. The systematic random sampling ameliorates in getting the automobilist samples, samples of the commuter, and samples of arid for reducing the hazards of bias.…”

“…A conventional recurrent neural network called LSTM contains the cell state, the memory part, and three gates to predict the collision based on the time series sequence of images in traffic. The transpose convolutional neural network produces the predicted images of collision [41].…”

Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)

“…Convolutional neural networks are a special type of deep neural network (DNN) inspired by Hubel and Wiesel's work in neuroscience [58]. CNNs process the spatial correlations between neighboring pixels and represent better input images than other deep learning architectures such as autoencoders and multilayer perceptron [59]. CNNs are composed of two layers: the convolutional layer and the pooling layer.…”

“…It is based on locally connected neurons, i.e., each neuron in the output layer receives input only from a small local group of neurons in the previous layer. It consists of several convolutional kernels that convolute with the image or the previous layer to learn different feature representations [59]. Next is the pooling layer, which acts as a down-sampling operator, combining semantically similar features into one, reducing the network's dimensionality and parameters.…”