LSTM network: a deep learning approach for short‐term traffic forecast
Short‐term traffic forecast is one of the essential issues in intelligent transportation system.
Accurate forecast result enables commuters make appropriate travel modes, travel routes …
Accurate forecast result enables commuters make appropriate travel modes, travel routes …
Short-term traffic flow forecasting method with MB-LSTM hybrid network
Q Zhaowei, L Haitao, L Zhihui… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Deep learning has achieved good performance in short-term traffic forecasting recently.
However, the stochasticity and distribution imbalance are main characteristics to traffic flow …
However, the stochasticity and distribution imbalance are main characteristics to traffic flow …
ST‐LSTM: A Deep Learning Approach Combined Spatio‐Temporal Features for Short‐Term Forecast in Rail Transit
Q Tang, M Yang, Y Yang - Journal of Advanced Transportation, 2019 - Wiley Online Library
The short‐term forecast of rail transit is one of the most essential issues in urban intelligent
transportation system (ITS). Accurate forecast result can provide support for the forewarning …
transportation system (ITS). Accurate forecast result can provide support for the forewarning …
Daily traffic flow forecasting through a contextual convolutional recurrent neural network modeling inter-and intra-day traffic patterns
Traffic flow forecasting is an important problem for the successful deployment of intelligent
transportation systems, which has been studied for more than two decades. In recent years …
transportation systems, which has been studied for more than two decades. In recent years …
A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data
T Bogaerts, AD Masegosa, JS Angarita-Zapata… - … Research Part C …, 2020 - Elsevier
Traffic forecasting is an important research area in Intelligent Transportation Systems that is
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …
Long-term traffic prediction based on lstm encoder-decoder architecture
Z Wang, X Su, Z Ding - IEEE Transactions on Intelligent …, 2020 - ieeexplore.ieee.org
Accurate traffic flow prediction is becoming increasingly important for transportation
planning, control, management, and information services of successful. Numerous existing …
planning, control, management, and information services of successful. Numerous existing …
Forecasting transportation network speed using deep capsule networks with nested LSTM models
Accurate and reliable traffic forecasting for complicated transportation networks is of vital
importance to modern transportation management. The complicated spatial dependencies …
importance to modern transportation management. The complicated spatial dependencies …
Traffic flow forecast through time series analysis based on deep learning
J Zheng, M Huang - IEEE Access, 2020 - ieeexplore.ieee.org
Traffic congestion is a thorny issue to many large and medium-sized cities, posing a serious
threat to sustainable urban development. Recently, intelligent traffic system (ITS) has …
threat to sustainable urban development. Recently, intelligent traffic system (ITS) has …
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 …
Deep temporal convolutional networks for short-term traffic flow forecasting
To reduce the increasingly congestion in cities, it is essential for intelligent transportation
system (ITS) to accurately forecast the short-term traffic flow to identify the potential …
system (ITS) to accurately forecast the short-term traffic flow to identify the potential …