Road traffic forecasting: Recent advances and new challenges
Due to its paramount relevance in transport planning and logistics, road traffic forecasting
has been a subject of active research within the engineering community for more than 40 …
has been a subject of active research within the engineering community for more than 40 …
[HTML][HTML] Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights
Accurate traffic state (ie, flow, speed, density, etc.) on an urban road network is important
information for urban traffic control and management strategies. However, due to the …
information for urban traffic control and management strategies. However, due to the …
Spatial-temporal transformer networks for traffic flow forecasting
Traffic forecasting has emerged as a core component of intelligent transportation systems.
However, timely accurate traffic forecasting, especially long-term forecasting, still remains an …
However, timely accurate traffic forecasting, especially long-term forecasting, still remains an …
Online incremental machine learning platform for big data-driven smart traffic management
The technological landscape of intelligent transport systems (ITS) has been radically
transformed by the emergence of the big data streams generated by the Internet of Things …
transformed by the emergence of the big data streams generated by the Internet of Things …
A modeling framework for the dynamic management of free-floating bike-sharing systems
Given the growing importance of bike-sharing systems nowadays, in this paper we suggest
an alternative approach to mitigate the most crucial problem related to them: the imbalance …
an alternative approach to mitigate the most crucial problem related to them: the imbalance …
Combining weather condition data to predict traffic flow: a GRU‐based deep learning approach
D Zhang, MR Kabuka - IET Intelligent Transport Systems, 2018 - Wiley Online Library
Traffic flow prediction is an essential component of the intelligent transportation
management system. This study applies gated recurrent neural network to predict urban …
management system. This study applies gated recurrent neural network to predict urban …
Attention meets long short-term memory: A deep learning network for traffic flow forecasting
W Fang, W Zhuo, J Yan, Y Song, D Jiang… - Physica A: Statistical …, 2022 - Elsevier
Accurate forecasting of future traffic flow has a wide range of applications, which is a
fundamental component of intelligent transportation systems. However, timely and accurate …
fundamental component of intelligent transportation systems. However, timely and accurate …
Δfree-LSTM: An error distribution free deep learning for short-term traffic flow forecasting
Timely and accurate traffic flow forecasting is open challenging. Canonical long short-term
memory (LSTM) network is considered qualified to capture the long-term temporal …
memory (LSTM) network is considered qualified to capture the long-term temporal …
Intelligent transportation and control systems using data mining and machine learning techniques: A comprehensive study
Traffic congestion is becoming the issues of the entire globe. This study aims to explore and
review the data mining and machine learning technologies adopted in research and industry …
review the data mining and machine learning technologies adopted in research and industry …
Deep autoencoder neural networks for short-term traffic congestion prediction of transportation networks
Traffic congestion prediction is critical for implementing intelligent transportation systems for
improving the efficiency and capacity of transportation networks. However, despite its …
improving the efficiency and capacity of transportation networks. However, despite its …