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
2018-11-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
dynamic traffic demand prediction , genetic algorithms , incidental traffic congestion , state change analysis , urban expressway traffic system , dynamic O‐D model , macroscopic traffic flow model , optimal diversion scheme , numerical analysis , nonlinearity problem , O‐D demand , road traffic , dynamic traffic flow model , traffic demand redistribution , origin‐destination demand , diversion rate , dynamic traffic diversion model , road network , METANET , dynamic demand change , dynamic traffic demand estimation , genetic algorithm
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