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为快速高效地对经济圈交通网络进行评价分析,在确定经济圈交通网络评价指标后,采用新的评价方法——基于支持向量机的综合评价方法,通过计算机编程对样本数据学习,能较好地识别经济圈交通网络的评价等级。支持向量机对小样本、非线性的数据具有较好的学习识别能力,对具有相同数据特性的经济圈交通网络评价指标数据进行模式识别,可以准确地判断经济圈交通网络发展水平所属等级。最后对珠三角和长三角经济圈交通网络评价指标数据进行验证,并与基于BP神经网络的评价方法进行对比分析,结果表明该方法客观、准确,具有较高的评价效率。
In order to quickly and efficiently evaluate and analyze the transport network in the economic circle, a new evaluation method, a comprehensive evaluation method based on support vector machines, is adopted to determine the evaluation index of transport network in the economic circle. It is better to learn sample data through computer programming To identify the economic circle traffic network rating. SVM has good ability of learning and recognizing small sample and non-linear data, and pattern recognition of traffic network evaluation index data of economic circle with the same data characteristics, which can accurately determine the level of development of transport network in economic circle. Finally, the data of traffic network evaluation in Pearl River Delta and the Yangtze River Delta economic circle are verified and compared with the evaluation method based on BP neural network. The results show that this method is objective, accurate and has high evaluation efficiency.