Abstract
Aiming at the problem of increasing road traffic vulnerability in urban agglomerations year by year, this paper proposes a practice-oriented model and method. Firstly, the index system of urban agglomeration road traffic is established on the basis of analyzing and sorting out relevant planning and design indexes at home and abroad. Secondly, based on principle, an evaluation model of urban agglomeration road traffic vulnerability based on entropy weight- model was constructed. Finally, an empirical study was conducted to calculate the indexes with software, and the corresponding indexes were put into the model for calculation. By comparing the calculated results with the grade classification standard, the vulnerability of the urban agglomeration and the corresponding urban road traffic was judged and the corresponding improvement Suggestions were put forward.
Highlights
As for the factors affecting the vulnerability of road traffic system in urban agglomerations, this study explored the following indexes, as shown in table 1
An evaluation model of road traffic vulnerability of urban agglomeration based on entropy weight-TOPSISmodel was established
Pajek software was used to study the urban agglomeration of Hohhot, Baotou, Ordos and Yulin
Summary
For positive indexes,the calculation formula is shown in (1) : indexes C1,C2 ,...,Cn 。 aij represents the number of the city i ’s index j . Step three: Considering the weight of road traffic dij amax − aij amax − amin. (3)The information entropy of index j in the evaluation index system road traffic vulnerability in urban agglomeration is calculated, the calculation formula of index entropy is shown in (3): ej =. Step five: Calculate the distance between the most vulnerable and the least vulnerable cities in each urban agglomeration, the calculation formula is shown in (5):. Step six: Calculate closeness degree: Represents the relative closeness between the index factor and the ideal solution under the criterion j of the city i , the formula for calculating j i is shown in (6):.
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