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- W4387642787 abstract "A new macroscopic traffic flow model is proposed which incorporates traffic alignment behavior at transitions. In this model, velocity is a function of the distance headway and driver response time. It can be used to characterize the traffic flow for both uniform and non uniform headways. The well-known Zhang model characterizes this flow based on driver memory which can produce unrealistic results. The performance of the proposed Khan-Imran-Gulliver (KIG) and Zhang models is evaluated for an inactive bottleneck on a 2000 m circular road. The results obtained show that the traffic behavior with the KIG model is more realistic." @default.
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- W4387642787 date "2023-10-01" @default.
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- W4387642787 title "Macroscopic Traffic Characterization based on Driver Memory and Traffic Stimuli" @default.
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