目前我国大中城市交通信息采集和传输的技术瓶颈已经基本解决，但是实时交通路况信息难以进一步与底层路网空间信息匹配和融合，造成大量路况信息难以得到有效应用，直接影响车载导航系统、公众出行信息平台、物流运输系统等位置服务（LBS）与智能交通系统（ITS）应用的服务水平。本文针对多以自然语言表达的实时路况信息与路网空间信息匹配融合这一技术难题，分析了实时路况信息的多源异构线性参照方法（LRM）表达形式，将中文自然语言理解技术融入信息融合过程，利用改进最大匹配算法实现了自然语言表达实时路况信息的自动化、智能化处理，并通过原型系统实现和实例应用验证了技术方案的有效性。

In recent years, technical bottleneck of traffic information collecting and transferring have basically been broken through, but large amounts of real-time traffic information cannot be well matched and fused with road network spatial database, which results in low efficiency in concerned applications, such as vehicle navigation system, public travel information service platform, logistics system and other ones in location based service（LBS）and intelligent transportation Systems（ITS). This paper aims to integrate real-time urban traffic information represented in Chinese natural-language with road networks spatial database, analysze linear reference methods embedded in multi-source and heterogeneous traffic information, as well as present an improved maximum matching algorithm to understand traffic information in Chinese natural-language and match it with spatial database automatically. Finally, a prototype system is developed to validate the approaches.