ABSTRACT
Measuring traffic congestion potential in real-time has been one of the challenges of traffic engineers in managing the problem of traffic congestion. Considering that the basis of the urban planning system is based on capacity measurement and traffic as a sub-branch of this system is not an independent phenomenon. However, it is the result of various demographic, physical, traffic, economic, cultural, and social effects. Based on this, the current research was conducted to analyze the traffic congestion potential in new and old neighborhoods of Urmia, emphasizing physical, traffic, and socio-economic criteria. The type of research based on the goal is applied, and the method of doing the work is descriptive-analytical. The collection of information is done by combining library and field studies. To calculate the coefficient of importance of indicators, the FUCOM method was used, and its results were obtained in the form of coding in Excel and Lingo software. The obtained results indicate that the highest coefficient of importance extracted is related to distance from sports land uses with 0.071, and the lowest is related to the indicator of distance from urban cores with 0.005. To visualize the traffic congestion potential in 16 neighborhoods of the new structure and 13 neighborhoods of the old structure of Urmia city, the CoCoSo model has been used. The obtained results indicate that the traffic potential is higher in the new and old neighborhoods, with a very small difference.
Extended Abstract
Introduction
Transportation and traffic systems as part of urban activities express the dynamism and life of an urban complex. Undoubtedly, a city cannot be imagined alive and dynamic without movement. Measuring traffic congestion potential in real-time has been a great challenge for traffic engineers to manage the problem of traffic congestion, especially during peak periods. The residential and non-residential land use patterns and the spatial framework resulting from the behavioral mechanism between them form the basis of urban travel. A distinguishing feature of land use is its ability or potential to "generate" traffic. Therefore, it is quite natural to relate the land use potential of a piece of land with specific activities to generate a certain amount of traffic flow per day. Because the basis of the urban planning system is based on capacity measurement and traffic as a sub-branch of this system is not an independent phenomenon but is the result of various demographic, physical, traffic, economic, cultural, and social effects, this research uses various physical indicators. The non-physical effects on urban traffic are aimed at solving the gap in Urmia city's traffic planning system and comparing the areas with traffic congestion potential in the new (Region 1) and old (Region 4) textures of Urmia city. The innovation of the current research can be seen in the application of a variety of 25 indicators in the form of 3 physical, traffic, and socio-economic variables, the implementation of the new FUCOM and CoCoSo methods, the use of the Google Maps application to obtain the average volume of traffic, and also the comparative comparison of traffic congestion potential on the scale of old-style neighborhoods. The new one mentioned that the output from it can be used as a way to prioritize the implementation of thematic and local plans to solve the traffic problems of the neighborhoods, compare the traffic efficiency of the types of textures, remove the passing traffic that disturbs peace from the residential areas, rearrange the uses based on the travel rate, etc.
Methodology
According to its purpose, this research is of applied research type, and according to the work method, it has a descriptive-analytical nature. Information was collected through library studies, field studies (including referring to offices to obtain data for comprehensive and detailed plans and comprehensive studies of transport and traffic in Urmia city), and census data from the Iran Statistics Center in 2016. We used the full compatibility method (FUCOM) to weight the indicators. First, we ranked the indicators using a questionnaire and then compiled pairwise comparisons based on the obtained rank. In the next step, the questionnaires with a sample number of 50 were randomly distributed among the elites, and the data were entered into Excel and Lingo software and were calculated and analyzed. After analyzing the questionnaires and calculating the weight of the indicators based on the FUCOM method based on the acceptable error level (DFC) to analyze the traffic congestion potential in the new and old neighborhoods of Urmia city based on 25 indicators, the information layers of the indicators were prepared in the GIS software. Then, the operation was converted into Raster format, and standardization was done based on the purpose of the research. In the next step, using the Zonal tool, traffic potential values have been extracted by separating 29 localities. In the next step, the CoCoSo multi-criteria decision-making method has been used to analyze and evaluate the traffic congestion potential in the new and old neighborhoods textures.
Results and discussion
The results obtained based on the FUCOM multi-criteria decision-making method indicated that the highest coefficient of importance extracted was related to distance from sports uses with 0.071, and the lowest was related to the index of distance from urban cores with 0.005. Also, the analysis of the indicators shows that in the neighborhoods of the new structure (Region 1), out of 16 neighborhoods, 3 neighborhoods are in the area of very low traffic potential, 3 neighborhoods are in the area of low traffic potential, 4 neighborhoods are in the area of medium traffic potential, and 4 neighborhoods are in the area of potential. There is much traffic and 2 neighborhoods are located in the area with a lot of traffic potential. In the neighborhoods of old texture (Region 4), out of 13 neighborhoods, 2 neighborhoods are in the zone of very low traffic potential, 3 neighborhoods are in the zone of low traffic potential, 2 neighborhoods are in the zone of medium traffic potential, 2 neighborhoods are in the zone of high traffic potential, and 4 neighborhoods are in the zone of high traffic potential.
Conclusion
The analysis of the traffic congestion potential in the new neighborhoods (Region 1) and the old neighborhood (Region 4) of Urmia city shows that the traffic potential is higher in the new neighborhoods than in the old neighborhoods, with a very small difference. Also, based on the analysis of 25 indicators, the highest traffic potential among the neighborhoods based on the indicators of distance from green, sports, administrative, commercial, and educational uses, road width, building density, average land price, student and working population, the area covered by public transportation, The number of cars, the service level of roads and the number of households are related to the new texture (Region 1) and are based on the indicators of distance from cultural, recreational, medical and religious uses, distance from urban cores, traffic volume, travel rate of uses, access to multi-story parking, density population and residential and per capita car ownership has the highest traffic potential belonging to old texture neighborhoods (Region 4).
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper. |
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