scholarly journals Traffic Flow Prediction Model Based on Drivers’ Cognition of Road Network

2020 ◽  
Vol 24 (7) ◽  
pp. 900-907
Author(s):  
Songjiang Li ◽  
◽  
Wen An ◽  
Peng Wang
Keyword(s):  
Prediction Model ◽  
Traffic Flow ◽  
Road Network ◽  
Probability Model ◽  
Prediction Method ◽  
Path Selection ◽  
Peak Period ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

The traditional traffic flow prediction method is based on data modeling, when emergencies occur, it is impossible to accurately analyze the changes in traffic characteristics. This paper proposes a traffic flow prediction model (BAT-GCN) which is based on drivers’ cognition of the road network. Firstly, drivers can judge the capacity of different paths by analyzing the travel time in the road network, which bases on the drivers’ cognition of road network space. Secondly, under the condition that the known road information is obtained, people through game decision-making for different road sections to establish the probability model of path selection; Finally, drivers obtain the probability distribution of different paths in the regional road network and build the prediction model by combining the spatiotemporal directed graph convolution neural network. The experimental results show that the BAT-GCN model reduces the prediction error compared with other baseline models in the peak period.

scholarly journals The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fan Hou ◽  
Yue Zhang ◽  
Xinli Fu ◽  
Lele Jiao ◽  
Wen Zheng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Road Network ◽  
Short Term Memory ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Point Of Interest ◽  
Flow Prediction ◽  
Long Short Term Memory ◽  
On The Road

Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). The model can capture the complex spatial dependence of road nodes on the road network and use LSGC (local spectrogram convolution) to capture spatial correlation features from the K-order local neighbors of the road segment nodes in the road network. It is more accurate to extract the information of neighbor nodes by replacing the single-hop neighborhood matrix with K-order local neighborhoods to expand the receptive field of graph convolution. The high-order neighborhood of road nodes is also fully considered instead of only extracting features from first-order neighbor nodes. In addition, an external attribute enhancement unit is designed to extract external factors (weather, point of interest, time, etc.) that affect traffic flow in order to improve the accuracy of the model’s traffic flow prediction. The experimental results show that when considering the static, dynamic, and static and dynamic combination, the model has excellent performance: RMSE (4.0406, 4.0362, 4.0234), MAE (2.7184, 2.7044, 2.7030), accuracy (0.7132, 0.7190, 0.7223).

scholarly journals Tensor Decomposition for Spatial—Temporal Traffic Flow Prediction with Sparse Data

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6046
Author(s):  
Funing Yang ◽  
Guoliang Liu ◽  
Liping Huang ◽  
Cheng Siong Chin
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Sparse Data ◽  
Time Of Day ◽  
Traffic Prediction ◽  
Traffic Surveillance ◽  
Data Sparsity ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Sparsity Problem

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

scholarly journals Traffic Flow Prediction with Relevance Vector Machine

2021 ◽  
Author(s):  
W.-Z. Xiong ◽  
X.-M. Shen ◽  
H.-J. Li ◽  
Z. Shen
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Traffic Flow ◽  
Sequence Data ◽  
Machine Method ◽  
Traffic Flow Prediction ◽  
Correlation Vector ◽  
Model Based ◽  
Flow Prediction ◽  
Short Period

Abstract Real-time prediction of traffic flow values in a short period of time is an importantelement in building a traffic management system. The uncertainty, complexity andnonlinearity of traffic flow data make it difficult to predict traffic flow in real time,and the accurate traffic flow prediction has been an urgent problem in the industry.Based on the research of scholars, a traffic flow prediction model based on thecorrelation vector machine method is constructed. The prediction accuracy of thecorrelation vector machine is better than that of the logistic regression and supportvector machine methods, and the correlation vector machine method has the functionof generating prediction error range for the actual traffic sequence data. Theprediction results are very satisfactory, and the prediction speed is significantlyfaster than the other two models, which meets the requirement of real-time trafficflow prediction and is suitable for real-time online prediction, and the predictionaccuracy of the used method is relatively high. The three-way comparison analysisshows that the traffic flow prediction by the correlation vector machine methodcan describe the nonlinear characteristics of traffic flow change more accurately,and the model performance and real-time performance are better. The case studyshows that the traffic flow prediction model based on the correlation vector machinecan improve the speed and accuracy of prediction, which is very suitablefor traffic flow prediction estimation with real-time requirements, and provides ascientific method for real-time traffic flow measurement.

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