路面平整度是路面性能的主要技术指标之一，其准确、快速检测对于路面养护、维修和管理具有重要意义。文中利用自主开发的智能手机App采集行车状态等相关数据，开展了实际道路的行车试验，采集了振动加速度、车速等行车数据，研究了利用行车数据检测路面平整度IRI的可行性。本文中提出了以合成振动加速度作为行车振动加速度指标的方法，建立了归一化的kNN特征向量空间。研究结果表明：文中提出的方法技术简便、易于应用，提升了利用智能手机检测路面平整度IRI的检测精度，IRI检测绝对评价准确率达到78%以上，而考虑相邻评价后的相对准确率达到96%以上，可以满足道路路网中路面平整度IRI的实时检测和监测要求，在提高路面平整度IRI检测的针对性、减少路面性能总体检测量方面具有应用前景，可为路网路面的养护决策和管理提供宏观指导。

Roughness is one of the main technical indexes of pavement performance. Accurate and rapid IRI detection has great significance for pavement maintenance and management. In this paper, the self-developed smart phone App was used to collect driving status and other relevant datas. Driving datas such as vibration acceleration and speed were collected through driving experiments in real road, and the feasibility of detecting road roughness IRI by using these driving datas was studied. It proposed a method to take the composite vibration acceleration as the index of driving vibration acceleration and established the normalized kNN eigenvector space . The results show that the proposed method is simple and easy to apply and it improves the detection accuracy of pavement roughness IRI by using smart phones. The absolute evaluation accuracy of IRI detection reaches more than 78%, and the re-lative accuracy after considering adjacent evaluations reaches more than 96%, which meets the real-time detection and monitoring of pavement roughness IRI in the road network. It has a promising application prospects in improving the pertinence of IRI detection of pavement roughness and reducing the overall detection amount of pavement performance, thus can provide macroscopic guidance for the maintenance decision and management of road network pavement.