Traffic diversion is an effective measure to solve the incidental traffic congestion in urban expressway traffic system. By adopting the macroscopic traffic flow model METANET, this study analyses the state change of traffic flow on the road network and establishes the dynamic traffic diversion model, inducing the redistribution of traffic demand. Considering the changes in the amount of origin–destination (O–D) demand, diversion rate is introduced into the basic theory of dynamic O–D model, and then established a dynamic traffic flow model based on dynamic demand change. The genetic algorithm is used to solve the non-linearity problem of the objective function in the traffic diversion model. This study sets up five cases for numerical analyses, and gets the optimal diversion scheme.
Dynamic traffic diversion model based on dynamic traffic demand estimation and prediction
IET Intelligent Transport Systems ; 12 , 9 ; 1123-1130
2018-08-20
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
dynamic O-D model , METANET , dynamic traffic diversion model , numerical analysis , genetic algorithm , dynamic traffic demand estimation , dynamic traffic demand prediction , traffic demand redistribution , urban expressway traffic system , diversion rate , O-D demand , optimal diversion scheme , road network , macroscopic traffic flow model , dynamic demand change , nonlinearity problem , road traffic , dynamic traffic flow model , origin-destination demand , genetic algorithms , incidental traffic congestion , state change analysis
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