NDLI: Dynamic traffic diversion model based on dynamic traffic demand estimation and prediction

Content Provider	IET Digital Library
Author	peng, Jiao Peng gang, Li Yi yue, Li Dong
Abstract	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.
Starting Page	1123
Ending Page	1130
Page Count	8
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 9, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5309
Journal	IET Intelligent Transport Systems
Publisher Date	2018-07-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Diversion Rate Dynamic Demand Change Dynamic O-D Model Dynamic Traffic Demand Estimation Dynamic Traffic Demand Prediction Dynamic Traffic Diversion Model Dynamic Traffic Flow Model Genetic Algorithm Incidental Traffic Congestion Macroscopic Traffic Flow Model METANET Nonlinearity Problem Numerical Analysis Numerical Method O-D Demand Optimal Diversion Scheme Optimisation Technique Origin-destination Demand Road Network Road Traffic State Change Analysis System Theory Application in Transportation Traffic Demand Redistribution Urban Expressway Traffic System
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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