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Accordingly, there is a need to develop an appropriate headway model especially under mixed traffic situation while taking decisions on transportation infrastructure development [3].
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Therefore, negative exponential distribution is commonly used for headway modelling.
This paper proposed theoretical headway models for a suburban arterial in Korea.
Similarly, Pearson 5 and Pearson 6 distributions reveal their appropriateness in time headway modelling under heavy flow of traffic [8].
In the present study, K-S test is chosen to measure goodness-of-fit of the selected headway models to the observed headways.
Thus, the present study considers four distributions namely, log-logistic, lognormal, Pearson 5 and Pearson 6 for headway modelling at moderate and heavy flow of traffic.
However, studies that have been devoted to headway modelling at high flow, in which all vehicles are in the car-following state, are still insignificant [4].
Pearson 3 distribution, which is basically a very general case of negative exponential distribution and normal distribution as well, could be used for headway modelling at moderate flow levels [32, 33]; this is perhaps the most general function that traffic engineers use under such traffic.
In a Portuguese study a new critical-headway model to describe the gap-acceptance process at microscopic level for roundabouts was proposed [53].
Developing an appropriate headway distribution model is an important step in traffic modelling and simulation.
We proposed a train headway adjustment model based on linear quadratic optimal control.
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