Analytical Measurement of Traffic Congestion Potentials in Urban Regions of Iran the case study of Urmia city

ABSTRACT
The present study aims to combine physical, socioeconomic, and traffic criteria to evaluate and analyze the traffic congestion potential of Urmia city. This study is applied and descriptive-analytical, where the required data were collected through library and field studies. To achieve the research goal, 25 indices classified under three physical, socioeconomic, and traffic criteria were selected, and their importance coefficients were calculated using the BWM approach. The BWM questionnaires were distributed among 50 elites in two steps as select the best and worst indices and complete the paired comparison questionnaire to determine the priority of the best index over other indices and the priority of other indices over the worst index). The outputs of the questionnaires were entered into the GAMS software to calculate the indices’ importance coefficients. The “distance from urban cores” and “average land price” indices obtained the highest and lowest weights, respectively. To show the traffic congestion potential of the five districts of Urmia city, the SECA model was implemented in Lingo software with different values of β. The findings divide Urmia city into 5 zones in terms of traffic congestion as very low traffic congestion (13%), low traffic congestion (32%), moderate traffic congestion (21%), high traffic congestion (21%), and very high traffic congestion (15%). The results indicate that District 4 has the highest traffic congestion potential, followed by Districts 5, 1, 3, and 2, respectively
Extended Abstract
Introduction
The urban planning system is based on a capacity assessment or potential evaluation, so traffic, as a sub-system of this system, is not an independent phenomenon and is the consequence of various demographic, physical, traffic, economic, cultural, and social factors. Thus, the present study aims to evaluate the traffic congestion potential of urban areas from a multi-dimensional perspective. Domestic experiences have shown that most urban traffic and transportation plans have been partially developed and implemented, disregarding environmental, social, economic, and cultural conditions. This is also true for Urmia city, and it faces traffic problems. According to its residents and city officials, traffic is one of the major problems of this city due to the following reasons as the centralization of a large part of commercial, administrative, educational, and medical uses in the central context, lack of contemporization of this context considering residents’ present needs, high population density in informal settlements, unregulated building density in the city, especially in newer context, neglect of urban road hierarchy in the subdivision, neglect of the trip generation rate of land uses in urban development plans, lack and mislocation of multi-story car parks, inattention to different transport modes, changing the function of local roads from local traffic to through traffic, etc. Therefore, the present research aims to apply various physical and non-physical indices effective in urban traffic to evaluate the districts in Urmia city in traffic congestion potential.
 
Methodology
This study is applied and descriptive-analytical, where the required data were collected through library study (including the review of the detailed master, transport, and traffic plans of Urmia city and the statistical yearbook of Iran (2016) and field studies. Since the GIS indices data were available for Urmia city, 25 indices were selected and classified under 3 socioeconomic, physical, and traffic criteria out of various indices influencing traffic congestion potential. After collecting the information on the required indices, the information layers were prepared in the GIS software. Next, to determine the importance of each index using the BWM approach, the BWM questionnaires were distributed among 50 elites in 2 steps, and the obtained data were analyzed through programming in the GAMS software to extract the weights of the indices. After calculating the importance coefficient of the indices, they were normalized in the GIS software according to the research goal using Fuzzy large and small functions. After analyzing traffic indices, their importance coefficients were combined to assess the traffic congestion potential of Urmia city. In the last step, to depict the results obtained by the five Urmia city districts, the SECA method was used with different values of β.
 
Results and discussion
The “distance from urban cores” and “average land price” indices obtained the highest and lowest weights, respectively. Moreover, the results indicate that the area of each district of Urmia City can be divided into 5 zones as follows: District 1 (very low traffic congestion (13%), low traffic congestion (30%), moderate traffic congestion (20%), high traffic congestion (17%), and very high traffic congestion (20%)), District 2 (very low traffic congestion (19%), low traffic congestion (43%), moderate traffic congestion (23%), high traffic congestion (12%), and very high traffic congestion (3%), District 3 (very low traffic congestion (16%), low traffic congestion (38%), moderate traffic congestion (23%), high traffic congestion (19%), and very high traffic congestion (4%)), District 4 (very low traffic congestion (4%), low traffic congestion (16%), moderate traffic congestion (15%), high traffic congestion (30%), and very high traffic congestion (36%)), and District 5 (very low traffic congestion (10%), low traffic congestion (27%), moderate traffic congestion (22%), high traffic congestion (21%), and very high traffic congestion (20%). The results of implementing the SECA model in the Lingo software for various values of W and S and β=5 show that according to Si values, District 4 of Urmia city has the highest traffic congestion potential, followed by Districts 5, 1, 3, and 2, respectively.
 
Conclusion
In general, investigating the 5 districts of Urmia city in the indices of traffic congestion potential indicated how many indices the districts have with the highest traffic congestion potential; District 1 (2 indices), District 2 (2 indices), District 3 (3 indices), District 4 (11 indices), and District 5 (10 indices). Regarding the indices with the lowest traffic congestion potential, the results were as follows:
 District 1 (1 index), District 2 (6 indices), District 3 (11 indices), District 4 (3 indices), and District 5 (4 indices).
 
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.