… to extend deep learning theory into large-scale transportation network analysis. A deep … is
utilized to model and predict traffic congestion evolution based on Global Positioning System (…

… specific to congestion prediction. In this survey, we present the current state of deep
learning applications in the tasks related to detection, prediction, and alleviation of congestion. …

… We define a neighborhood-wide congestion score for congestion management and four …
We define a deep learning model based on LSTM architecture for predicting the congestion …

… deep learning models in predicting the traffic congestion on roads. To overcome the congestion
… behavior of the vehicle and to predict the congestion in traffic effectively on roads. The …

… In this section we present the proposed deep learning methodology for predicting the traffic
congestion along motorways. Fig. 1 presents the proposed methodological framework, which …

… 2) Hyperparameters: We use the open-source deep learning library, deeplearning4j,1 to
build our models. The first (L −1) convolutions use 64 filters of size 2 × 2, and the last one uses …

… We propose to use a deep learning model titled Deep Congestion Prediction Network (DCPN)
inspired by DAEs for transportation network congestion prediction. It is designed to learn …

… This section dedicated to the traffic congestion prediction architecture. Using CNN as
well as BLSTME models, the hybrid project predicts the traffic moments. To enable …

… planning mobile application that can predict traffic conditions, allowing road … deep learning
model for congestion prediction and supplements a Linear Regression (LR) model to predict …

… , and explain the architecture of our hybrid deep learning neural network which learns both
spatial and temporal features for traffic congestion prediction. Section IV presents the data …