The Dynamic Adjusting Model of Traffic Queuing Time—A Monte Carlo Simulation Study

The traffic queueing problem usually can be solved by two concepts: static and dynamic mode. Usually, the static mode may not reflect the real-world situation. This encourages us to propose the dynamic adjusting model to solve the traffic queuing problem with a time-varying feature. Our model develops two modes and includes eight formulae to calculate the analysis results. In our first calculating mode, the three formulae intend to simulate the maximum traffic flow in the real-world situation. The second mode is to calculate the results based on the simulation data generated by Monte Carlo simulation. To see our model capability, we validate our model by the simulation data and verify the results by the Monte Carlo simulation method. By our proposed performance measurement, the resulting outcomes show that our model outperforms the previous studies. Additionally, our solution procedure is simple and assumes the model following pseudo-randomly, which coincides with the current status. Furthermore, because of the Internet of Things (IoT) trend, one can expect that the data can be automatically collected. This leads to our proposed model can be implemented once the data are obtained.