摘要
为比较江苏南部和浙江北部两个典型发达地区的城乡公交线网结构,选择了海宁市、海盐县、张家港市和吴江区4 个县级行政区作为样本,借助Transcad 4.5 软件计算线网密度、非直线系
数、重复系数、连通度、站点覆盖率、平均站距等6 个城乡公交线网结构指标属性值并进行标准化,采用熵值加权逼近理想排序技术作为评价方法,通过利用评价指标固有信息来判别指标权重,计算城乡公交线网结构评价值与正、负理想解的距离后得到评价值与理想解的贴近度。对4
个样本的贴近度进行排序,发现张家港市城乡公交线网结构最佳,其后依次为吴江区、海宁市、海盐县,总的来说苏南样本优于浙北样本,两地城乡公交线网结构仍有改善空间。
Abstract
To compare the structure of urban and rural bus networks of southern Jiangsu Province with
that of northern Zhejiang Province, which are two famous developed areas in China, four county-level
districts named Haining, Haiyan, Zhangjiagang and Wujiang were selected as samples. Six indexes of urban and rural bus network including network density, nonlinear coefficient, overlapping degree, connectivity, stop coverage ratio, average stop distance were calculated and standardized with software Transcad 4.5. A model named TOPSIS (Technique for Order Performance by Similarity to an Ideal Solution)
weighted with entropy was presented. Index weight was determined by using inherent information of evaluation index. After calculating the distance between urban and rural bus network structure evaluation
value and the positive and negative ideal solutions, the closeness between the evaluation value and the
ideal solution was obtained. Ranking the closeness of 4 samples, it was found that urban and rural bus
network structure of Zhangjiagang City is the best, followed by Wujiang district, Haining City, Haiyan
County. Finally, it is found that the structure of urban and rural bus network of southern Jiangsu is better
than that of northern Zhejiang. Besides, the urban and rural bus network structure of both two areas can be improved to a large extent.
关键词
公路运输 /
线网结构 /
逼近理想排序技术 /
熵 /
城乡公交
Key words
highway transportation /
network structure /
TOPSIS(Technique for Order Performance by Similarity to an Ideal Solution) /
entropy /
urban and rural bus
姚志刚,翟垒,刘松.
基于熵值加权逼近理想排序技术的城乡公交线网结构比较研究[J]. 交通运输研究. 2017, 3(4): 30-34
YAO Zhi-gang, ZHAI Lei and LIU Song.
Comparative Evaluation of Urban and Rural Bus Network
Structure Based on TOPSIS Weighted with Entropy[J]. Transport Research. 2017, 3(4): 30-34
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