A deep convolutional neural network based approach for vehicle classification using large-scale GPS trajectory data

2020 ◽  
Vol 116 ◽  
pp. 102644 ◽  
Author(s):  
Sina Dabiri ◽  
Nikola Marković ◽  
Kevin Heaslip ◽  
Chandan K. Reddy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Deep Convolutional Neural Network ◽  
Vehicle Classification ◽  
Trajectory Data ◽  
Gps Trajectory Data ◽  
Gps Trajectory

scholarly journals Traffic congestion prediction based on GPS trajectory data

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771984744 ◽  
Author(s):  
Shuming Sun ◽  
Juan Chen ◽  
Jian Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Traffic Congestion ◽  
Learning Models ◽  
Trajectory Data ◽  
Congestion Prediction ◽  
Gps Trajectory Data ◽  
Conventional Machine ◽  
Gps Trajectory ◽  
Machine Learning Models

Since speed sensors are not as widely used as GPS devices, the traffic congestion level is predicted based on processed GPS trajectory data in this article. Hidden Markov model is used to match GPS trajectory data to road network and the average speed of road sections can be estimated by adjacent GPS trajectory data. Four deep learning models including convolutional neural network, recurrent neural network, long short-term memory, and gated recurrent unit and three conventional machine learning models including autoregressive integrated moving average model, support vector regression, and ridge regression are used to perform congestion level prediction. According to the experimental results, deep learning models obtain higher accuracy in traffic congestion prediction compared with conventional machine learning models.

Optimization of deep convolutional neural network for large scale image retrieval

2018 ◽  
Vol 303 ◽  
pp. 60-67 ◽  
Author(s):  
Cong Bai ◽  
Ling Huang ◽  
Xiang Pan ◽  
Jianwei Zheng ◽  
Shengyong Chen
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Deep Convolutional Neural Network ◽  
Large Scale Image Retrieval

scholarly journals Anomaly Detection of Electro-data Based on Deep Convolutional Neural Network

2018 ◽  
Vol 173 ◽  
pp. 03080
Author(s):  
Zhi Zhang ◽  
Liang Guo ◽  
Xianguang Dong ◽  
Yanjie Dai ◽  
Yan Du
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Recall Rate ◽  
Deep Convolutional Neural Network ◽  
Electrical Parameters ◽  
Sample Data ◽  
Novel Method ◽  
Time Information ◽  
Precision Rate

As diversity of electro-data anomaly, the methods based on artificial feature are becoming more difficult to detect anomalies among a great deal of electro-data. Hence, this paper proposes a novel method which is based on deep convolutional neural network (DCNN) to detect anomaly electro-data. This method models the sample data with time information and electrical parameters, and labels them as normal or abnormal automatically. Further, the paper improves the designing DCNN to extract precise features from large scale of electro-data to get high accuracy. The results of the case analysis show that our method can detect anomaly electro-data more exact and stable than the traditional methods. The abnormal precision rate and abnormal recall rate of our approach reach 92.7% and 91.3% respectively.

Sign in / Sign up

Export Citation Format

Share Document