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The result is that the spacing will again increase. When the driver first reaches the opposite threshold, he accelerates and again tries to achieve the desired spacing (for further details, see Chapter 2) If one assumes that the relationship of the perceptual thresholds for spacing is the same for both 26 J. Barceló positive and negative changes in relative speed, then the resulting spacing behavior resembles a symmetrical pendulum about its equilibrium point. It can be shown (Leutzbach, 1988) that the described behavior corresponds to a particular case of the general car-following model: x¨ n+1 (t + T) = c m x˙ n+1 (t) xn (t) − xn+1 (t) l x˙ n (t) − x˙ n+1 (t) with m=0 and l=2.
9) is u=λ 1 1 − k kjam where kjam is the jam density and, taking into account the fundamental relationship q=ku, the resulting steady-state equation is 20 J. Barceló q = ku = λ 1 − k kjam This is inconsistent with the observed traffic stream data (Gerlough and Huber, 1975; Rothery, 1992). To overcome this inconsistency, Gazis et al. 10) By integrating eq. 10) now, calculating the integration constraint in a similar way, and replacing the values for known steady flow conditions, the speed–density relationship is λ= u = c1 ln kjam k Defining qm as the maximum flow and km as the density at maximum flow, it can be shown that c1 =um , the speed at maximum flow.
In this case there is neither routing in the model nor DTA. Vehicles travel stochastically in the network, leaving the network occasionally, according to the turning and exit proportions. This is the usual mode in most practical applications of microscopic simulation to small networks. 2. Vehicles travel across the network from origins to 1 Models, Traffic Models, Simulation, and Traffic Simulation 33 destinations along the available paths that join them. Route choice models and therefore DTA and/or DUE became an essential part of the model and the simulation study.