Freeway traffic speed prediction under the intelligent driving environment: a deep learning approach
The intelligent transportation system (ITS) has been proven capable of effectively
addressing traffic congestion issues. For vehicles to perform effectively and improve mobility …
addressing traffic congestion issues. For vehicles to perform effectively and improve mobility …
Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing
W Lu, Z Yi, W Liu, Y Gu, Y Rui… - IET Intelligent Transport …, 2020 - Wiley Online Library
Real‐time and accurate multi‐lane traffic condition forecasting is of great importance to the
connected and automated vehicle highway system. However, the majority of existing deep …
connected and automated vehicle highway system. However, the majority of existing deep …
Traffic flow prediction based on BRNN
H Bohan, B Yun - 2019 IEEE 9th International Conference on …, 2019 - ieeexplore.ieee.org
Accurate and real-time traffic flow prediction plays an important role in building intelligent
transportation systems and traffic control and induction. As traffic flow data is mostly time …
transportation systems and traffic control and induction. As traffic flow data is mostly time …
Congestion prediction with big data for real-time highway traffic
By collecting and analyzing a vast quantity and different categories of information, traffic flow
and road congestion can be predicted and avoided in intelligent transportation system …
and road congestion can be predicted and avoided in intelligent transportation system …
Identifying important variables for predicting travel time of freeway with non-recurrent congestion with neural networks
CS Li, MC Chen - Neural Computing and Applications, 2013 - Springer
The provision of long-distance travel time information has been a major factor facilitating the
intelligent transportation system to become more successful. Previous studies have pointed …
intelligent transportation system to become more successful. Previous studies have pointed …
Traffic speed prediction for urban transportation network: A path based deep learning approach
J Wang, R Chen, Z He - Transportation Research Part C: Emerging …, 2019 - Elsevier
Traffic prediction, as an important part of intelligent transportation systems, plays a critical
role in traffic state monitoring. While many studies accomplished traffic forecasting task with …
role in traffic state monitoring. While many studies accomplished traffic forecasting task with …
[HTML][HTML] Short-term passenger flow prediction of urban rail transit based on a combined deep learning model
Z Hou, Z Du, G Yang, Z Yang - Applied Sciences, 2022 - mdpi.com
It is difficult for a single model to simultaneously capture the nonlinear, correlation, and
periodicity of data series in the passenger flow prediction of urban rail transit (URT). To …
periodicity of data series in the passenger flow prediction of urban rail transit (URT). To …
Conjoining congestion speed-cycle patterns and deep learning neural network for short-term traffic speed forecasting
Forecasting accurate traffic conditions is essential to regional traffic management. Since
congestions are usually caused by regular activities, capturing speed-cycle patterns for …
congestions are usually caused by regular activities, capturing speed-cycle patterns for …
Coupled application of deep learning model and quantile regression for travel time and its interval estimation using data in different dimensions
The rapid development of sensing and computing methods and their application to
transportation engineering in recent years provide us data support to traffic flow prediction …
transportation engineering in recent years provide us data support to traffic flow prediction …
Improved deep hybrid networks for urban traffic flow prediction using trajectory data
The urban traffic flow prediction is a significant issue in the intelligent transportation system.
In consideration of nonlinear and spatial-temporal features of urban traffic data, we propose …
In consideration of nonlinear and spatial-temporal features of urban traffic data, we propose …