Land use and transportation planning are inter-dependent, as well as being important factors in forecasting urban development. In recent years, predicting traffic based on land use, along with several other variables, has become a worthwhile area of study. In this paper, it is proposed that Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) methods could be used to predict traffic. These methods used three key variables: land use, demographic and temporal data. The proposed methods were evaluated with other methods, using datasets collected from the City of Calgary, Canada. The proposed DNN-Regression focused on demographic and land use variables for traffic prediction. The study also predicted traffic temporally in the same geographical area by using DNN-RNN. The DNN-RNN used long short-term memory to predict traffic. Comparative experiments revealed that the proposed DNN-Regression and DNN-RNN models outperformed other methods.
%0 Journal Article
%1 abulazadlearning
%A Azad, Abul
%A Wang, Xin
%D 2020
%J Civil Engineering and Urban Planning: An International Journal (CiVEJ )
%K (DNN) (DNN-RNN) (DNN-Regression) Deep Network Neural Recurrent Regression prediction traffic
%P 3
%R 10.5121/civej.2020.7301
%T DEEP LEARNING NEURAL NETWORK APPROACHES TO LAND USE-DEMOGRAPHIC- TEMPORAL BASED TRAFFIC PREDICTION
%U https://www.airccse.com/civej/vol7.html
%V 7
%X Land use and transportation planning are inter-dependent, as well as being important factors in forecasting urban development. In recent years, predicting traffic based on land use, along with several other variables, has become a worthwhile area of study. In this paper, it is proposed that Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) methods could be used to predict traffic. These methods used three key variables: land use, demographic and temporal data. The proposed methods were evaluated with other methods, using datasets collected from the City of Calgary, Canada. The proposed DNN-Regression focused on demographic and land use variables for traffic prediction. The study also predicted traffic temporally in the same geographical area by using DNN-RNN. The DNN-RNN used long short-term memory to predict traffic. Comparative experiments revealed that the proposed DNN-Regression and DNN-RNN models outperformed other methods.
@article{abulazadlearning,
abstract = {Land use and transportation planning are inter-dependent, as well as being important factors in forecasting urban development. In recent years, predicting traffic based on land use, along with several other variables, has become a worthwhile area of study. In this paper, it is proposed that Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) methods could be used to predict traffic. These methods used three key variables: land use, demographic and temporal data. The proposed methods were evaluated with other methods, using datasets collected from the City of Calgary, Canada. The proposed DNN-Regression focused on demographic and land use variables for traffic prediction. The study also predicted traffic temporally in the same geographical area by using DNN-RNN. The DNN-RNN used long short-term memory to predict traffic. Comparative experiments revealed that the proposed DNN-Regression and DNN-RNN models outperformed other methods.},
added-at = {2020-10-06T09:15:12.000+0200},
author = {Azad, Abul and Wang, Xin},
biburl = {https://www.bibsonomy.org/bibtex/255784550e14eae43f293219a716d2d9d/civej},
doi = {10.5121/civej.2020.7301},
interhash = {f65ebaa231c8515cd4702e3e4c94301b},
intrahash = {55784550e14eae43f293219a716d2d9d},
journal = {Civil Engineering and Urban Planning: An International Journal (CiVEJ )},
keywords = {(DNN) (DNN-RNN) (DNN-Regression) Deep Network Neural Recurrent Regression prediction traffic},
month = {September},
pages = 3,
timestamp = {2021-02-11T12:34:06.000+0100},
title = {DEEP LEARNING NEURAL NETWORK APPROACHES TO LAND USE-DEMOGRAPHIC- TEMPORAL BASED TRAFFIC PREDICTION},
url = {https://www.airccse.com/civej/vol7.html},
volume = 7,
year = 2020
}