scholarly journals A Graph Convolutional Method for Traffic Flow Prediction in Highway Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tianpu Zhang ◽  
Weilong Ding ◽  
Tao Chen ◽  
Zhe Wang ◽  
Jun Chen
Keyword(s):  
Traffic Flow ◽  
Weather Conditions ◽  
Learning Models ◽  
Convolutional Network ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Time Space ◽  
Highway Network ◽  
Highway Management ◽  
Machine Learning Models

As a transportation way in people’s daily life, highway has become indispensable and extremely important. Traffic flow prediction is one of the important issues for highway management. Affected by many factors, including temporal, spatial, and other external ones, traffic flow is difficult to accurately predict. In this paper, we propose a graph convolutional method. And the name of our model proposed is the hybrid graph convolutional network (HGCN), which comprehensively considers time, space, weather conditions and date type to achieve better predicted results of traffic flow at highway stations. Compared with baselines implemented by various machine learning models, all metrics of our model are reduced dramatically.

scholarly journals Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

2022 ◽  
Vol 71 (2) ◽  
pp. 3953-3968
Author(s):  
Mesfer Al Duhayyim ◽  
Amani Abdulrahman Albraikan ◽  
Fahd N. Al-Wesabi ◽  
Hiba M. Burbur ◽  
Mohammad Alamgeer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Traffic Flow ◽  
Weather Conditions ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction

2022 ◽  
Vol 13 (2) ◽  
pp. 1-21
Author(s):  
He Li ◽  
Xuejiao Li ◽  
Liangcai Su ◽  
Duo Jin ◽  
Jianbin Huang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Historical Data ◽  
Temporal Correlation ◽  
Weather Conditions ◽  
External Factors ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Spatio Temporal ◽  
The Impact

Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

scholarly journals Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

2014 ◽  
Vol 26 (5) ◽  
pp. 393-403 ◽  
Author(s):  
Seyed Hadi Hosseini ◽  
Behzad Moshiri ◽  
Ashkan Rahimi-Kian ◽  
Babak Nadjar Araabi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Weather Conditions ◽  
Traffic Data ◽  
Data Loss ◽  
Traffic Lights ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Flow Prediction ◽  
Forecasting Method

Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.

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