Open AccessDynamic traffic diversion model based on dynamic traffic demand estimation and prediction
Author(s) -
Pengpeng Jiao,
Yigang Li,
Dongyue Li
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5309
Subject(s) - traffic congestion reconstruction with kerner's three phase theory , traffic flow (computer networking) , traffic generation model , computer science , microscopic traffic flow model , traffic congestion , transport engineering , traffic optimization , simulation , floating car data , engineering , real time computing , computer network
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.