In many moving objects databases, future locations of the vehicles in arterial networks are predicted. While most of the studies apply the frequent behavior of historical trajectories or the vehicles’ recent kinematics as the basis of predictions, considering the dynamics of the intersections is mostly neglected. Signalized intersections make vehicles experience different delays which varies from zero to minutes based on the traffic state at intersections. In the absence of traffic signal information (red and green times of traffic signal phases, the length of the queues, approaching traffic volume, turning volumes to each intersection leg, etc.) the experienced delay in traffic signals is a random variable. In this paper, we model the probability distribution function (PDF) and cumulative distribution function (CDF) of the delay for any point on the arterial networks based on the spatiotemporal model of the queue at the intersection. The probability of the presence of a vehicle in a zone is determined based on the modeled probability function of delay. The comparison between the results of the proposed method and a well-known kinematic-based method indicates a significant improvement in the precisions of the predictions.