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Paper Information

Journal:   GEOGRAPHICAL DATA   Spring 2019 , Volume 28 , Number 109 #R00459; Page(s) 147 To 166.

Evaluating the effects of urban land use on traffic volume with the aim of organizing and redistributing them Case Study: Central texture of Kashan

Author(s):  Gholami Bimargh Yones*, HOSEINI SEYED AHMAD, SHATERIAN MOHSEN, AGHAMOHAMMADI AKRAM, Dehghan jazi Abolfazl
* Faculty of natural resources and earth sciences, Kashan University
Introduction Nowadays, rapid urbanization; mismatch between modern streets and the demands of population; population attracting land uses along streets; and vicinity of incompatible land uses have resulted in traffic congestion in cities. Traffic is one of the major problems in most large cities, and even medium and small cities. It is also one of the social problems of modern societies and cities. Although, extensive studies have been carried out on the network structure and land use separately, their interaction has been disregarded. Like other modern cities, the city of Kashan faces this problem. The central texture of Kashan attracts a large population throughout the day, and especially during rush hours. This is on the one hand, due to the presence of historical elements, such as Kashan historical bazaar, historical buildings and schools, and on the other hand, because of population attracting land uses like commercial, educational, and therapeutic land use. Therefore, it is necessary to consider this problem, and the spatial redistribution of population attracting land uses. Materials & Methods The present study applies a descriptive-analytic methodology. The necessary information was collected using library research, documentary method, and expert interview. Then, the data was entered in GIS software. GIS software and network analysis model were used for data analysis. Results & Discussion In this study, the role of educational and therapeutic land use in traffic congestion in central areas of Kashan was investigated. To carry out network analysis, the network map of Kashan streets and their operating speed were required. The street network was depicted in GIS software. Then, the maximum operating speed of the main streets of Kashan was determined based on the master plan. Table 1 presents operating speed in five main axes of Kashan based on the master plan. These include main and crowded streets of Kashan. The operating speed of other streets was collected through expert interviews. After designing the network and determining operating speed of streets, (educational and therapeutic) land uses with the most significant impact on the traffic congestion of Kashan were identified by interviewing ten experts, with the aim of determining service areas. For each sample land use, a test was performed to determine service areas in the network analysis phase. To conduct this test, the standard service radius of educational and sanitation land uses in Iran was used. In network analysis, the test was separately conducted for each primary school (minimum operating radius of 4 minutes/ maximum operating radius of 5 minutes), middle school (minimum access radius of 6 minutes/ maximum access radius of 7 minutes), high school (minimum access radius of 8 minutes/maximum access radius of 10 minutes), and therapeutic land uses (minimum access radius of 7 minutes/ maximum access radius of 8 minutes). Conclusion Based on the analysis of service provision range in Kashan downtown, we conclude that compared to other areas in the city, primary schools (in their minimum access radius) face 2. 25% increase in traffic congestion; middle schools in their minimum radius of access face 4. 67%, increase and in their maximum radius of access face 1. 83% increase; high schools in their minimum radius of access face 3. 25% increase, and in their maximum radius of access face 7. 95% increase, and therapeutic land use in their minimum radius face 7. 46% increase, and in their maximum radius face 6. 16 % increase in traffic congestion. However, primary schools in other areas of the city face 0. 24% higher traffic congestion in maximum access radius as compared to downtown. Thus, downtown attracts 13. 13% more unnecessary urban commutes and traffic in its minimum radius of access. This reaches 20. 68% in the maximum radius of access, which is due to a larger overlap between educational and therapeutic land use.
Keyword(s): Urban land use,urban traffic,redistribution of space,central part,network analysis
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