Developing a graphical algorithm to find the shortest path in transportation networks
Today, routing in urban transportation networks is considered essential with the increase in the volume of cars and traffic restrictions in metropolitan areas. Urban development, the use of personal cars has affected the external development of metropolises and has caused the pattern of suburban settlement, which new cities are concrete examples of this problem within the boundaries of metropolises.
This research is applied in terms of purpose and descriptive-analytical in terms of cognitive methodology. Considering the nature of the data and the impossibility of controlling the behavior of the effective variables in the problem, it is also non-empirical. In order to review the sources of the research, a systematic review of related sources has been used using the documentary method. In this research, in order to analyze the data, the state of dispersion of access indicators was analyzed using the GWR method, crowding optimization and fuzzy mean clustering.
Multivariate regression analysis indicates that there is a linear and direct and high correlation between the independent index and the dependent index (final access) and the identified independent indicators are able to explain 95% of the changes in access and the remaining small variances by Unknown factors are explained and predicted. By looking at the beta β values, it can be seen that the network position index has access changes compared to other indicators, and according to the access indicators, network characteristics are ranked second to fourth.
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