scholarly journals Spatiotemporal Features—Extracted Travel Time Prediction Leveraging Deep-Learning-Enabled Graph Convolutional Neural Network Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1253
Author(s):  
Xiantong Li ◽  
Hua Wang ◽  
Pengcheng Sun ◽  
Hongquan Zu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Travel Time ◽  
Coarse Grained ◽  
Travel Time Prediction ◽  
Spatiotemporal Features ◽  
Traffic Conditions ◽  
Time Prediction ◽  
Proposed Model

Travel time prediction is one of the most important parameters to forecast network-wide traffic conditions. Travelers can access traffic roadway networks and arrive in their destinations at the lowest costs guided by accurate travel time estimation on alternative routes. In this study, we propose a long short-term memory (LSTM)-based deep learning model, deep learning on spatiotemporal features with Convolution Neural Network (DLSF-CNN), to extract the spatial–temporal correlation of travel time on different routes to accurately predict route travel time. Specifically, this model utilizes network-wide travel time, considering its topological structure as inputs, and combines convolutional neural network and LSTM techniques to accurately predict travel time. In addition to their spatial dependence, both coarse-grained and fine-grained temporal dependences are fully considered among the road segments along a route as well. The shift problem is formulated in the coarse-grained granularity to predict the route travel time in the next time interval. The experimental tests were conducted using real route travel time obtained by taxi trajectories in Harbin. The test results show that the travel time prediction accuracy of DLSF-CNN is above 90%. Meanwhile, the proposed model outperformed the other machine learning models based on multiple evaluation criteria. The RMSE (Root Mean Squard Error) and R2 (R Squared) increased by 18.6% and 22.46%, respectively. The results indicate the proposed model performs reasonably well under prevailing traffic conditions.

scholarly journals Travel Time Prediction by Providing Constraints on a Convolutional Neural Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 59336-59349 ◽  
Author(s):  
Xiangdong Ran ◽  
Zhiguang Shan ◽  
Yufei Fang ◽  
Chuang Lin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Travel Time ◽  
Travel Time Prediction ◽  
Time Prediction

scholarly journals Multi-output bus travel time prediction with convolutional LSTM neural network

2019 ◽  
Vol 120 ◽  
pp. 426-435 ◽  
Author(s):  
Niklas Christoffer Petersen ◽  
Filipe Rodrigues ◽  
Francisco Camara Pereira
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Bus Travel Time ◽  
Convolutional Lstm

scholarly journals Travel time prediction of urban road based on BP neural network

2020 ◽  
Vol 1651 ◽  
pp. 012190
Author(s):  
Fangyi Deng ◽  
Pei Su ◽  
Bingxue Luo ◽  
Peng Wu ◽  
Yan Guo
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Bp Neural Network ◽  
Travel Time Prediction ◽  
Urban Road ◽  
Time Prediction

scholarly journals An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Early Stage ◽  
Binary Classification ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

A Multiple Regression Convolutional Neural Network for Estimating Multi-parameters Based on Overall Data in the Inverse Heat Transfer Problem

2021 ◽  
pp. 1-31
Author(s):  
Feiding Zhu ◽  
Jincheng Chen ◽  
Yuge Han
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Problem ◽  
Sparse Data ◽  
Heat Transfer Problem ◽  
Inverse Heat Transfer ◽  
Inverse Heat Transfer Problem ◽  
Proposed Model

Abstract The inverse heat transfer problem (IHTP) is a central task for estimating parameters in heat transfer. It is ill-posedness that is characterised by instability and non-uniqueness of the solution. Recently, novel algorithms using deep learning and neural networks for application of various sparse data in the inverse heat transfer problem. In order to overcome the optimization problem of input nodes under sparse data, we try to use the overall data of the target as the basis for inversion. In this work, we used an improved convolutional neural network (CNN) to estimate multi-parameters in the inverse heat transfer problem. Computational fluid dynamics (CFD) and deep learning are fused to provide datasets for training of the proposed model. The proposed model was verified by experiments with a cubic cavity. Additionally, the improved CNN model was used to predict the different parameters of the more complex armored vehicle model. The results showed that the model has good prediction accuracy for estimating multi-parameters on different datasets. These attempts of introducing convolutional neural network to the IHTP in the present study were successful and it was significant for the study of the inverse heat transfer problem of estimating multi-parameters.

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