Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada
Land use and transportation planning have a significant impact on the performance of cities’ traffic conditions and the quality of people’s lives. The changing characteristics of land use will affect and challenge how a city is able to manage, organize, and plan for new developments and transportati...
Main Authors: | Abul Azad, Xin Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-05-01
|
Series: | ISPRS International Journal of Geo-Information |
Subjects: | |
Online Access: | https://www.mdpi.com/2220-9964/10/6/358 |
Similar Items
-
Short term prediction of wireless traffic based on tensor decomposition and recurrent neural network
by: Tao Deng, et al.
Published: (2021-08-01) -
Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
by: Xue Zhang, et al.
Published: (2021-03-01) -
Hybrid LSTM Neural Network for Short-Term Traffic Flow Prediction
by: Yuelei Xiao, et al.
Published: (2019-03-01) -
Internet Traffic Prediction Using Recurrent Neural Networks
by: Mircea Eugen Dodan, et al.
Published: (2022-09-01) -
A deep relearning method based on the recurrent neural network for land cover classification
by: Yunwei Tang, et al.
Published: (2022-12-01)