A systematic review of big data-based urban sustainability research: State-of-the-science and future directions
The future of humanity depends increasingly on the performance of cities. Big data provide
new and powerful ways of studying and improving coupled urban environmental, social, and …
new and powerful ways of studying and improving coupled urban environmental, social, and …
Sustainable mobility: A review of possible actions and policies
M Gallo, M Marinelli - Sustainability, 2020 - mdpi.com
In this paper, a review of the main actions and policies that can be implemented to promote
sustainable mobility is proposed. The work aims to provide a broad, albeit necessarily not …
sustainable mobility is proposed. The work aims to provide a broad, albeit necessarily not …
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Spatiotemporal forecasting has various applications in neuroscience, climate and
transportation domain. Traffic forecasting is one canonical example of such learning task …
transportation domain. Traffic forecasting is one canonical example of such learning task …
A hybrid deep learning based traffic flow prediction method and its understanding
Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic
flow with big data. While existing DNN models can provide better performance than shallow …
flow with big data. While existing DNN models can provide better performance than shallow …
Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies
Multistep traffic forecasting on road networks is a crucial task in successful intelligent
transportation system applications. To capture the complex non-stationary temporal …
transportation system applications. To capture the complex non-stationary temporal …
Spatiotemporal data mining: a survey on challenges and open problems
Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay
between space and time. Several available surveys capture STDM advances and report a …
between space and time. Several available surveys capture STDM advances and report a …
Unidirectional and bidirectional LSTM models for short‐term traffic prediction
This paper presents the development and evaluation of short‐term traffic prediction models
using unidirectional and bidirectional deep learning long short‐term memory (LSTM) neural …
using unidirectional and bidirectional deep learning long short‐term memory (LSTM) neural …
Deep sequence learning with auxiliary information for traffic prediction
Predicting traffic conditions from online route queries is a challenging task as there are many
complicated interactions over the roads and crowds involved. In this paper, we intend to …
complicated interactions over the roads and crowds involved. In this paper, we intend to …
[HTML][HTML] Traffic congestion patterns in the urban road network:(Dammam metropolitan area)
KM Almatar - Ain Shams engineering journal, 2023 - Elsevier
Traffic congestion is a significant problem affecting the sustainable development of urban
traffic. It is important to analyze the congestion and forecast future traffic models to prevent …
traffic. It is important to analyze the congestion and forecast future traffic models to prevent …
Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data
Long short-term memory (LSTM) models provide high predictive performance through their
ability to recognize longer sequences of time series data. More recently, bidirectional deep …
ability to recognize longer sequences of time series data. More recently, bidirectional deep …