Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder
The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.
Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder
The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.
Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder
Navin Ranjan (Autor:in) / Sovit Bhandari (Autor:in) / Pervez Khan (Autor:in) / Youn-Sik Hong (Autor:in) / Hoon Kim (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Multi-scale convolutional autoencoder network-based implicit information mining of cultural relics
| British Library Conference Proceedings | 2023
Segmentation of backscattered electron images of geopolymers using convolutional autoencoder network
| BASE | 2022
Congestion-related information and road network performance
| Online Contents | 1996
Prediction of Congestion Due Road Works on Freeways
| British Library Conference Proceedings | 2001