为解决浮动车数据在城市范围内存在的“信号漂移”和“信号盲区”问题，提高地图道路 适配准确率和路况信息准确率，研究应用WSN无线定位技术以及地图匹配技术，设计数据融合 框架，提出基于WSN和GPS的融合数据路况优化算法及计算流程，监测车辆在路网上的行驶状 况和各路段的拥堵程度。通过选取广州市800 个车载传感器节点、20 个路侧传感器节点以及3 个路段进行现场数据验证，结果显示在加入WSN定位数据并采用上述算法处理后，地图道路适配准确率提高约4%，路况信息准确率提高约5%，同时路况信息的处理速度也得以提高。可见，基于该优化算法，WSN和GPS融合定位数据能有效提高浮动车路况的准确性和有效性。

In order to solve the problem of "signal drift" and "signal blind zone" of FCD （floating car data） in the city, improve the road adaptation accuracy of map and the traffic information accuracy, the wireless location technology of WSN （wireless sensor networks） and map-matching technology were studied and applied, a data fusion framework was designed, and a traffic optimization algorithm and calculation process based on the integration of WSN and GPS (global positioning system) data were proposed, which could be used to detect the vehicle travelling condition and the degree of congestion. Taking an example of some field data of 800 vehicle sensor nodes, 20 roadside sensor nodes and 3 sections in Guangzhou, the effect of this optimization algorithm was tested. The results showed that the road adaptation accuracy of map was improved by about 4%, the traffic information accuracy was improved by about 5%, and the processing speed of traffic information was improved after adding WSN positioning da⁃ ta and using the algorithm. It proves that the accuracy and effectiveness of FCD can be improved by using the integration positioning data of WSN and GPS with the optimization algorithm.