在路面平整度预测达到规范化、标准化的同时，面向智慧城市、智能交通设施建设，路面平整度IRI（国际平整度指数）的快速、高频次、低成本的智能预测也得到了广泛研究，但基于不同智能设备的平整度预测精度、预测效果等尚未得到深入的研究。该研究首先利用笔者课题组开发的两种智能预测设备，即路面行车数据采集智能手机App软件和路面行车数据采集智能终端，开展了路面行车实验，采集了行车振动、速度、位置等数据；然后引入随机森林模型，确定了最能够反映IRI影响的4个振动加速度特征指标；接着利用循环神经网络（RNN）、门控循环单元（GRU）和长短期记忆网络（LSTM）3种神经网络，建立了路面平整度IRI的预测模型，并对不同模型、不同设备的IRI预测精度进行了对比。结果表明：LSTM在3种神经网络模型中实现了最好的鲁棒性和最高的预测精度，两个智能设备预测IRI的决定系数分别为0.864和0.789，智能终端的预测精度高于智能手机。研究成果对于提升我国道路路面平整度预测和监测的信息化水平、提高道路养护决策科学化水平等，具有重要的理论意义和应用价值。

While the prediction of surface smoothness has achieved a certain level of standardization and normalization, the rapid, high-frequency, and low-cost intelligent prediction of surface smoothness-specifically the International Roughness Index (IRI)-has gained widespread attention in the context of smart city and intelligent transportation infrastructure development. However, the prediction accuracy and performance of IRI based on different intelligent devices have yet to be thoroughly investigated. In this study, road driving experiments were first conducted using two types of intelligent prediction devices developed by the authors’research team: a smartphone App for road driving data collection and an intelligent terminal device for the same purpose. During these experiments, data such as driving vibration, speed, and location were collected. Next, a random forest model was employed to identify four vibration acceleration features that most significantly influence the prediction result of IRI. Finally, three neural networks, namely Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short Term Memory Network (LSTM), were used to establish the prediction model for road roughness IRI. The prediction accuracy of different models and devices was then compared and analyzed. The results show that, LSTM model achieved the best robustness and highest prediction accuracy among three neural network models. The coefficients of determination of IRI prediction model for two devices were 0.864 and 0.789, respectively, with the intelligent terminal outperforming the smartphone in prediction accuracy. These research findings hold significant theoretical and practical value for enhancing the informatization level of surface smoothness prediction and monitoring in China, as well as for improving the scientific basis of road maintenance decision-making.