智能交通系统中路况信息的编码

Research on Coding of Road Information in ITS

根据提出的一种交通量生成模型所产生的仿真数据 ,对城市交通中路况信息数据流的信源编码进行了研究 .压缩数据的方法是对初始信源等效变换 ,使变换后等效信源的概率分布比原信源更加有序 ,熵值更小 ,从而压缩率更高 .传输时还要对等效信源使用霍夫曼 (Huffman)算法编出即时可译码 ,使该变长码的平均码长接近熵值 .给出的编码方案可用于智能交通系统 (ITS)的车辆导航中对路况信息的传输 .

The code of road information is analyzed, based on the data provided by a model of the traffic volume. The method to compress data is equivalent conversion of the initial source, so the probability distribution of the equivalent conversed source becomes more sequential with less entropy value and greater compression ratio than those of initial one. When being transmitted, the instantaneously decodable code could be coded via huffman algorithm in order that the average length of variable length prefix code (VLPC) is closed to the entropy value of the source. The result shows that the correlation of traffic volume at a cross is more than that in a street as well as in whole a city. Its achievements can be applied in ITS to communicate the road information.