Dynamics of 2 types of complex-valued neural network is numerically analyzed. In [Kuroe, Y, et al., 2003], some mathematical properties of a mutually connected Hopfield-type network of nonrotating complex-valued neurons were shown, and the sufficient conditions of the existence of an energy function are derived for 2 types (types A and B) of the activation function of the neuron. In this paper, we consider the Hopfield network of rotating complex-valued neurons. The network dynamics is decomposed into the dynamics of the amplitude and phase of each neuron. For type B network, the dynamics of the phases is shown to be the dynamics of a coupled system of rotating phase oscillators with a pair-wise sinusoidal phase-difference interaction [Nishikawa, I and Kuroe, Y, 2004]. Therefore a phase synchronization, which is well known in a phase oscillator system [Kuramoto, Y, 1984], is expected also in type B network. At the same time in this type B network, the network dynamics of homogeneously rotating neurons can be transformed into the network dynamics of non-rotating neurons, for which the existence condition of an energy function is derived explicitly. On the other hand for type A network, there is no such correspondence to a phase oscillator system, nor equivalence to the network whose convergence is assured by the existence of an energy function. One recent result on a phase oscillator system is the effectiveness for an area-wide signal control of an urban traffic network. Therefore in this paper, the dynamics of type A and B complex-valued rotating neural networks is numerically investigated, especially from the point of view of the effective control of the signal offset. The similarity and the difference between the 2 types of dynamics are shown through computer simulations using a microscopic traffic simulator on several traffic flow patterns and conditions.