Driving behavior optimization can not only reduce energy consumption and the probability of traffic accidents but also improve the riding experience of passengers. Unfortunately, the low estimation accuracy resulting from the poor performance of prediction models greatly influences bus service performance. In this paper, a time cycle neural network, the long short-term memory (LSTM) network, is used to evaluate real-time bus riding comfort and provide driving suggestions. To ensure the prediction accuracy, a series of preprocessing procedures, such as data filtering, GPS data processing, parameter calculation and road segmentation, are performed. Three indicators, velocity, longitudinal acceleration, and yaw rate, are selected, while a critical zone-oriented training process is performed. Simulation results show that the proposed method has rapid convergence and acceptable prediction accuracy while providing driving suggestions is reasonable.

驾驶行为优化不仅可以降低能源消耗和交通事故发生的概率，还可以改善乘客的乘坐体验。不幸的是，由于预测模型性能不佳而导致的低估计精度极大地影响了公交服务性能。在本文中，时间周期神经网络，即长短期记忆（LSTM）网络，用于评估实时公交车乘坐舒适度并提供驾驶建议。为保证预测精度，进行了数据过滤、GPS数据处理、参数计算、道路分割等一系列预处理程序。选择三个指标，速度、纵向加速度和偏航率，同时执行面向关键区域的训练过程。