Queuing model-based optimal traffic flow in a grid network

In order to deal with the impact on traffic flow of the rule, we compare the influence factors of traffic flow (passing, etc.) into viscous resistance of fluid mechanics, and establish a traffic model based on fluid mechanics. First, in heavy and light traffic, we respectively use this model to simulate the actual segment of the road and find that when the traffic is heavy, the rule hinder the further increase in traffic. For this reason, we make further improvements to the model to obtain a fluid traffic model based on no passing and find that the improved model makes traffic flow increase significantly. Then, the improved model is applied to the light traffic, we find there are no significant changes in traffic flow .In this regard we propose a new rule: when the traffic is light, passing is allowed, but when the traffic is heavy, passing is not allowed.

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Research of the Temporary Parking Acceleration-Spacing Model Based on Swarm Theory

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.361-363.2113 ◽

2013 ◽

Vol 361-363 ◽

pp. 2113-2116

Author(s):

Jin Xin Cao ◽

Lei Wang ◽

Wei Li Zhang ◽

Jun Wu

Keyword(s):

Traffic Flow ◽

Potential Field ◽

Traffic Congestion ◽

Field Method ◽

Lagrange Method ◽

Traffic Jams ◽

Model Based ◽

Potential Field Method ◽

Swarm Theory

The disturbance factors in the traffic flow may lead to traffic congestion. The agglomeration characteristics shown in traffic jams are similar to the biological swarm characteristics. In this paper, acceleration-spacing model is established based on the potential field method and the Lagrange method. The vehicle in front is viewed as the main force source. Then the data of the traffic congestion caused by the temporary parking in front of the school are used to calibrate the parameters of the model. It can be verified that the model is effective.

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Traffic Flow Prediction with Relevance Vector Machine

10.21203/rs.3.rs-547121/v1 ◽

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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