This study proposes a machine learning-based protocol for developing a TRANSYT-7F model for an urban arterial network. The developed artificial neural network (ANN) method models the queue lengths of TRANSYT-7F using saturation flow, start-up lost time, and platoon dispersion as inputs. The queue lengths of the selected approaches of the study network can be obtained using the ANN model without running the TRANSYT-7F model. The optimum values of the selected parameters (i.e., saturation flow, start-up lost time, and platoon dispersion) were obtained using the genetic algorithm, which ensures minimum difference between the measured queue length and the ANN output (i.e., queue length). Finally, the comparison of the measured queue length and the simulated queue length with the calibrated TRANSYT-7F model revealed that the mean absolute percentage error was less than 2.5% for all approaches of the study network.

这项研究提出了一种基于机器学习的协议，用于为城市动脉网络开发TRANSYT-7F模型。先进的人工神经网络（ANN）方法使用饱和流，启动损失时间和排扩散作为输入来模拟TRANSYT-7F的队列长度。可以使用ANN模型获得研究网络中所选方法的队列长度，而无需运行TRANSYT-7F模型。使用遗传算法获得所选参数的最佳值（即饱和流量，启动损失时间和排量），以确保所测队列长度与ANN输出（即队列长度）之间的最小差异。最后，