摘要:定位技术的普遍应用，使得随时随地获取个人位置成为可能，进一步推动了基于位置的服务等新型应用的发展，产生了海量轨迹数据.精确的路网匹配对提高这些新型应用的服务质量具有重要的研究意义，然而受众多因素的影响，大部分轨迹的采样率较低，比如由签到类应用或低功耗设备生成的低采样轨迹，给路网匹配带来了巨大的挑战.研究基于偏好的个性化路网匹配（driving preference based personalized map-matching，简称DPMM），提出了在动态道路交通网络中的用户驾驶偏好模型.基于该模型，提出了两阶段路网匹配算法：局部匹配搜索用户最可能采用的几条局部Skyline路径；设计了全局匹配的动态规划算法，该算法返回在用户驾驶偏好下最可能的多条全局路径作为最终匹配结果.实验结果充分表明，该方法是有效的和高效的，具有一定的使用价值.



Abstract:With the increasing proliferation of position technologies, there comes huge volumes of trajectory data, which are used in many modern applications such as path planning and location based services. Accurate road network matching can improve the service quality of these new applications. However, the low sampling trajectories bring a major challenge for map-matching. This paper studies the problem of matching individual law-sampling trajectory to a dynamic multi-criteria road network based on user's driving preferences. First, a driving preference model in the dynamic road traffic network is proposed. Based on this model, a two-stage map-matching algorithm is developed. While local matching searches multiple local likely Skyline paths, a global matching dynamic programming algorithm is designed and the most probable k global paths are selected as the matching result. Experiments show that the proposed method is effective and efficient.



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