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Volume 37, Issue 1 (2022)
GeoRes 2022, 37(1): 59-68 |
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Received: 2021/10/14 | Accepted: 2021/12/16 | Published: 2022/01/22
Received: 2021/10/14 | Accepted: 2021/12/16 | Published: 2022/01/22
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Akbari M. Analysis of Traffic Equipment in Isfahan Metropolis Using Multimoora Technique. GeoRes 2022; 37 (1) :59-68
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M. Akbari *
Department of Geography and Urban Planning, Faculty of Literature and Humanities, Yasouj University, Yasouj, Iran
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Introduction
Investment in transportation infrastructure is considered a tool to reduce the economic gap between the center and periphery of a country by providing access to underdeveloped areas [Pokharel et al., 2021]. Transportation capability is regarded as a key determinant of sustainable urban development [Ogryzek et al., 2020]. Urban transportation facilitates access to goods, services, and other opportunities, playing a major role in people’s quality of life [Hernández, 2017]. Research on public transport sustainability in cities, particularly in developing countries mostly located in Asia and the Middle East, has been very limited to date [Gruyter et al., 2017]. High-quality sustainable urban transportation requires the implementation of innovative systems and the need to gain public trust [Banister, 2008]. A well-planned and managed transportation system can enhance efficiency and improve the community’s quality of life [Ogryzek et al., 2020].
Since the Industrial Revolution, large cities and metropolises in most developed countries have expanded alongside urbanization and industrialization, which has been accompanied by population growth and economic development in urban areas [Chae et al., 2021]. Industrialization and urbanization have intensified the conflict between environmental protection and economic growth [Dong et al., 2021]. Urbanization is one of the factors affecting economic development, ecosystem health, and human welfare [Wu & Oueslati, 2016].
Urbanization worldwide, including in Iran, while contributing to economic development, has also introduced challenges and problems. Rapid and uncontrolled population growth in Iranian metropolises, along with increased use of motorized vehicles, has made traffic one of the most pressing challenges of contemporary urban life. In large cities, especially in developing countries like Iran, traffic and transportation problems have had negative consequences. Focusing on sustainable urban transport management reduces environmental impacts, increases the efficiency of transportation systems, and improves urban quality of life, with the ultimate goal of minimizing accessibility problems. Therefore, integrated urban transport and traffic management is an undeniable necessity. This study aims to analyze traffic and transportation facilities across the fifteen districts of Isfahan metropolis using the Multi-MOORA technique and its three approaches: ratio system, reference point, and full multiplicative.
Yu et al. [2012] have found that significant spatial variations exist in the effects of transportation infrastructure productivity in China, with the highest output elasticity observed in provinces connected to the central region, indicating the highest economic return on transport investment. Haghshenas & Vaziri [2012] have concluded that cities in developed parts of Asia and Europe performed best due to their emphasis on various forms of non-motorized and public transportation. Lee & Sener [2016] have found that researchers increasingly focus on the link between public health and transportation; transportation programs primarily aim to improve life quality from a physical well-being perspective, while mental and social well-being receive limited attention.
Maparu & Mazumder [2017] have emphasized that transportation infrastructure has long been a key tool for economic development and urbanization, showing a clear relationship between transport infrastructure and economic growth. Hernández [2017] highlightes unequal distribution of transportation facilities in Montevideo, Uruguay. Pereira et al. [2017] have argued that distributive justice concerns in transportation and social exclusion should primarily focus on access as a human capability, meaning that policy assessments must ensure minimum accessibility standards to key destinations.
Vavrek & Bečica [2020] have observed that transport companies in larger Czech cities provide less effective public transport, and economic efficiency declines significantly with population growth. Lope & Dolgun [2020] note inequalities in transport indicators in Melbourne, such as accessible trams. Pokharel et al. [2021] identify transport costs as a key driver of center–periphery patterns in countries, though few studies have tested this in developing nations’ transport networks. Hong et al. [2021] have highlighted the critical role of transport infrastructure in economic growth, noting that unequal distribution is a major cause of economic inequality in China. Akbari [2021] has found that Tehran ranked highest among studied Iranian metropolises in urban transport indicators, while the EDAS technique showed weaker performance for other cities. Lussier Tomaszewski & Boisjoly [2021] have emphasized that accessibility indicators, which measure ease of reaching destinations via specific transport modes, are increasingly used in planning as they support integrated land use and transportation planning.
Many scholars now consider urban quality of life a multidimensional construct encompassing both objective life conditions and subjective satisfaction [Atkinson, 2013]. Transportation is a key component affecting quality of life, and public transport is often viewed as essential for sustainable cities [Miller et al., 2016]. Transportation is a critical aspect of urban sustainability; city road networks carry human activities and have been studied structurally and dynamically for decades [Wang et al., 2018]. Sustainable transport systems focus on planning, policy, and technology to ensure efficient transport of goods and high-quality services [Ogryzek et al., 2020]. While no universally accepted definition of sustainability, sustainable development, or sustainable transportation exists, it is generally agreed that sustainable transport seeks a proper balance among environmental, social, and economic quality for both present and future [Steg & Gifford, 2005].
Transportation advancements are fundamental for economic transformation. Transport systems not only influence economic development but also undergo quantitative and qualitative changes in the process. Urban land-use arrangements are heavily influenced by transport facilities and networks. Therefore, careful urban transport planning can lead to optimal land-use patterns and more desirable urban spaces. The present study aims to analyze traffic and transport facilities across the fifteen districts of Isfahan metropolis.
Methodology
This research is an analytical-comparative study using the Multi-MOORA technique to analyze urban traffic and transport facilities in the fifteen districts of Isfahan metropolis. Data on 14 traffic facility indicators for these districts were collected from the Isfahan Metropolitan Statistical Yearbook (2019) [Statistical Yearbook of Isfahan Metropolitan, 2019]. Indicator weights were calculated using the Shannon entropy method and applied in the Multi-MOORA technique. After data normalization and preparation of a weighted normalized matrix, the indicators were analyzed using the three Multi-MOORA approaches.
The Multi-MOORA technique, introduced by Brauers and Zavadskas in 2010 [Adalı & Işık, 2017; Balezentis et al., 2012], is an extension of the MOORA method developed in 2006. Multi-MOORA adds the full multiplicative approach to the original two approaches (ratio system and reference point). Each approach ranks alternatives, which are then integrated using the dominance theory. Dominance occurs when one alternative outranks the others, determined by the 1-1-1 condition, and overall dominance occurs when two out of three rankings are superior to other alternatives.
Isfahan Metropolis
Isfahan, with a population of 1,961,260, is the third-largest city in Iran, accounting for 2.54% of the country’s total population [Akbari et al., 2018]. Located in Iran’s arid zone, the city faces limitations in urban transport networks, environmental issues, and shortages of cultural spaces, which are exacerbated by population growth, leading to inequality and unhealthy competition for access to urban facilities. Inequitable access to urban services can aggravate core urban issues and social problems [Annamoradnejad et al., 2012].
Based on the 2019 census, Isfahan metropolis had a population of 1,961,260, making it the third-largest city in Iran after Tehran and Mashhad. Among its fifteen districts, District 8 was the most densely populated with 239,765 residents, while District 11 had the smallest population at 58,841.
Findings
Two indicators, staff below diploma level and staff with diploma or associate degree in the Transportation and Traffic Department, were considered negative indicators, whereas the remaining twelve indicators were positive, with higher values indicating better performance. The calculated weights for the indicators were used in the Multi-MOORA model.
In the first approach of Multi-MOORA, the ratio system, indicator weights were applied to the normalized matrix to compute the performance scores for each district. The results showed that District 10 achieved the highest score, while District 11 had the lowest.
In the second approach, the reference point method, reference values for each indicator were calculated. Using these as a baseline, performance scores were obtained. District 8 achieved the highest score in this approach, while Districts 5, 11, 12, and 14 scored the lowest.
In the third approach, the full multiplicative method, District 6 received the highest score. Districts 10, 1, 3, and 6 also performed well, whereas Districts 5, 9, 11, 12, 14, and 15 received the lowest scores.
Finally, the results from the three approaches were integrated using dominance theory to determine the final rankings. According to this method, District 10 ranked first overall, followed by District 8 in second place, and District 3 in third. District 11 ranked last.
Analysis of the relationship between the population of the fifteen districts and their Multi-MOORA rankings showed a weak correlation. The correlation between district population rank and the ratio system rank was 0.361, indicating a weak relationship. The highest correlation observed (0.756) was between the ratio system and the full multiplicative approach, suggesting consistency between these two ranking approaches.
Discussion
In the Multi-MOORA technique, three ranking approaches were applied. The first approach, the ratio system, calculated the performance value (y) for traffic equipment indicators across the fifteen districts of Isfahan metropolis. The results indicated that District 10 achieved the highest score, while District 11 received the lowest.
The reference point method, as the second approach, used calculated reference values for the traffic indicators as a baseline. District 8 achieved the highest score in this approach, while Districts 5, 11, 12, and 14 scored the lowest.
The third approach, the full multiplicative method (U), showed that District 6 obtained the highest score. Districts 10, 3, and 1 also received high scores, whereas Districts 5, 9, 11, 12, 14, and 15 had the lowest scores.
Using dominance theory, the rankings from these three approaches were integrated to determine the final ranking. The results indicated that District 10 had the best overall performance, while District 11 had the worst.
These findings align with Hernández (2017), who reports that transport equipment indicators in Montevideo were neither fairly nor optimally distributed. Their study shows that public transport coverage in Montevideo, the capital and largest port city of Uruguay, effectively serves a large population across the geographic area.
Similarly, Lope & Dolgun (2020) have observed inequalities in transport indicators in Melbourne, particularly regarding accessible trams, where service for people with disabilities was significantly lower than for the general population. They suggest that these inequalities could guide future transport investment.
Akbari (2021) also has found inequalities in transport indicators in Tehran using the AIDAS technique. Tehran scores the highest (1.000) among studied metropolises, while Mashhad scores 0.549 and Isfahan 0.508, showing a smaller gap between Mashhad and Isfahan. Other studied cities show weaker performance.
A key limitation for such studies in Iran is the lack of traffic equipment in most cities, especially small and medium-sized ones. Urban furniture, such as traffic equipment installed on pavements and streets, is essential to ensure order and safety for citizens. Therefore, enhancing traffic infrastructure is crucial for urban management, particularly in medium-sized cities.
In Isfahan, special attention is needed for:
Currently, urban mobility in most Iranian cities is inadequate, and without corrective and preventive measures, it could become a severe crisis. Thus, Iranian cities must adopt sustainable transport management principles as the foundation for future urban transport planning.
Conclusion
Analysis of the differences in scores obtained from the three Multi-MOORA approaches, ratio system, reference point, and full multiplicative, combined through dominance theory, reveals inequalities in traffic and transport equipment across the fifteen districts of Isfahan metropolis.
Acknowledgments: Not reported by the author.
Ethical Permission: Not reported by the author.
Conflict of interest: Not reported by the author.
Author contribution: Mahmoud Akbari is the sole author of the article.
Funding: Not reported by the author.
Investment in transportation infrastructure is considered a tool to reduce the economic gap between the center and periphery of a country by providing access to underdeveloped areas [Pokharel et al., 2021]. Transportation capability is regarded as a key determinant of sustainable urban development [Ogryzek et al., 2020]. Urban transportation facilitates access to goods, services, and other opportunities, playing a major role in people’s quality of life [Hernández, 2017]. Research on public transport sustainability in cities, particularly in developing countries mostly located in Asia and the Middle East, has been very limited to date [Gruyter et al., 2017]. High-quality sustainable urban transportation requires the implementation of innovative systems and the need to gain public trust [Banister, 2008]. A well-planned and managed transportation system can enhance efficiency and improve the community’s quality of life [Ogryzek et al., 2020].
Since the Industrial Revolution, large cities and metropolises in most developed countries have expanded alongside urbanization and industrialization, which has been accompanied by population growth and economic development in urban areas [Chae et al., 2021]. Industrialization and urbanization have intensified the conflict between environmental protection and economic growth [Dong et al., 2021]. Urbanization is one of the factors affecting economic development, ecosystem health, and human welfare [Wu & Oueslati, 2016].
Urbanization worldwide, including in Iran, while contributing to economic development, has also introduced challenges and problems. Rapid and uncontrolled population growth in Iranian metropolises, along with increased use of motorized vehicles, has made traffic one of the most pressing challenges of contemporary urban life. In large cities, especially in developing countries like Iran, traffic and transportation problems have had negative consequences. Focusing on sustainable urban transport management reduces environmental impacts, increases the efficiency of transportation systems, and improves urban quality of life, with the ultimate goal of minimizing accessibility problems. Therefore, integrated urban transport and traffic management is an undeniable necessity. This study aims to analyze traffic and transportation facilities across the fifteen districts of Isfahan metropolis using the Multi-MOORA technique and its three approaches: ratio system, reference point, and full multiplicative.
Yu et al. [2012] have found that significant spatial variations exist in the effects of transportation infrastructure productivity in China, with the highest output elasticity observed in provinces connected to the central region, indicating the highest economic return on transport investment. Haghshenas & Vaziri [2012] have concluded that cities in developed parts of Asia and Europe performed best due to their emphasis on various forms of non-motorized and public transportation. Lee & Sener [2016] have found that researchers increasingly focus on the link between public health and transportation; transportation programs primarily aim to improve life quality from a physical well-being perspective, while mental and social well-being receive limited attention.
Maparu & Mazumder [2017] have emphasized that transportation infrastructure has long been a key tool for economic development and urbanization, showing a clear relationship between transport infrastructure and economic growth. Hernández [2017] highlightes unequal distribution of transportation facilities in Montevideo, Uruguay. Pereira et al. [2017] have argued that distributive justice concerns in transportation and social exclusion should primarily focus on access as a human capability, meaning that policy assessments must ensure minimum accessibility standards to key destinations.
Vavrek & Bečica [2020] have observed that transport companies in larger Czech cities provide less effective public transport, and economic efficiency declines significantly with population growth. Lope & Dolgun [2020] note inequalities in transport indicators in Melbourne, such as accessible trams. Pokharel et al. [2021] identify transport costs as a key driver of center–periphery patterns in countries, though few studies have tested this in developing nations’ transport networks. Hong et al. [2021] have highlighted the critical role of transport infrastructure in economic growth, noting that unequal distribution is a major cause of economic inequality in China. Akbari [2021] has found that Tehran ranked highest among studied Iranian metropolises in urban transport indicators, while the EDAS technique showed weaker performance for other cities. Lussier Tomaszewski & Boisjoly [2021] have emphasized that accessibility indicators, which measure ease of reaching destinations via specific transport modes, are increasingly used in planning as they support integrated land use and transportation planning.
Many scholars now consider urban quality of life a multidimensional construct encompassing both objective life conditions and subjective satisfaction [Atkinson, 2013]. Transportation is a key component affecting quality of life, and public transport is often viewed as essential for sustainable cities [Miller et al., 2016]. Transportation is a critical aspect of urban sustainability; city road networks carry human activities and have been studied structurally and dynamically for decades [Wang et al., 2018]. Sustainable transport systems focus on planning, policy, and technology to ensure efficient transport of goods and high-quality services [Ogryzek et al., 2020]. While no universally accepted definition of sustainability, sustainable development, or sustainable transportation exists, it is generally agreed that sustainable transport seeks a proper balance among environmental, social, and economic quality for both present and future [Steg & Gifford, 2005].
Transportation advancements are fundamental for economic transformation. Transport systems not only influence economic development but also undergo quantitative and qualitative changes in the process. Urban land-use arrangements are heavily influenced by transport facilities and networks. Therefore, careful urban transport planning can lead to optimal land-use patterns and more desirable urban spaces. The present study aims to analyze traffic and transport facilities across the fifteen districts of Isfahan metropolis.
Methodology
This research is an analytical-comparative study using the Multi-MOORA technique to analyze urban traffic and transport facilities in the fifteen districts of Isfahan metropolis. Data on 14 traffic facility indicators for these districts were collected from the Isfahan Metropolitan Statistical Yearbook (2019) [Statistical Yearbook of Isfahan Metropolitan, 2019]. Indicator weights were calculated using the Shannon entropy method and applied in the Multi-MOORA technique. After data normalization and preparation of a weighted normalized matrix, the indicators were analyzed using the three Multi-MOORA approaches.
The Multi-MOORA technique, introduced by Brauers and Zavadskas in 2010 [Adalı & Işık, 2017; Balezentis et al., 2012], is an extension of the MOORA method developed in 2006. Multi-MOORA adds the full multiplicative approach to the original two approaches (ratio system and reference point). Each approach ranks alternatives, which are then integrated using the dominance theory. Dominance occurs when one alternative outranks the others, determined by the 1-1-1 condition, and overall dominance occurs when two out of three rankings are superior to other alternatives.
Isfahan Metropolis
Isfahan, with a population of 1,961,260, is the third-largest city in Iran, accounting for 2.54% of the country’s total population [Akbari et al., 2018]. Located in Iran’s arid zone, the city faces limitations in urban transport networks, environmental issues, and shortages of cultural spaces, which are exacerbated by population growth, leading to inequality and unhealthy competition for access to urban facilities. Inequitable access to urban services can aggravate core urban issues and social problems [Annamoradnejad et al., 2012].
Based on the 2019 census, Isfahan metropolis had a population of 1,961,260, making it the third-largest city in Iran after Tehran and Mashhad. Among its fifteen districts, District 8 was the most densely populated with 239,765 residents, while District 11 had the smallest population at 58,841.
Findings
Two indicators, staff below diploma level and staff with diploma or associate degree in the Transportation and Traffic Department, were considered negative indicators, whereas the remaining twelve indicators were positive, with higher values indicating better performance. The calculated weights for the indicators were used in the Multi-MOORA model.
In the first approach of Multi-MOORA, the ratio system, indicator weights were applied to the normalized matrix to compute the performance scores for each district. The results showed that District 10 achieved the highest score, while District 11 had the lowest.
In the second approach, the reference point method, reference values for each indicator were calculated. Using these as a baseline, performance scores were obtained. District 8 achieved the highest score in this approach, while Districts 5, 11, 12, and 14 scored the lowest.
In the third approach, the full multiplicative method, District 6 received the highest score. Districts 10, 1, 3, and 6 also performed well, whereas Districts 5, 9, 11, 12, 14, and 15 received the lowest scores.
Finally, the results from the three approaches were integrated using dominance theory to determine the final rankings. According to this method, District 10 ranked first overall, followed by District 8 in second place, and District 3 in third. District 11 ranked last.
Analysis of the relationship between the population of the fifteen districts and their Multi-MOORA rankings showed a weak correlation. The correlation between district population rank and the ratio system rank was 0.361, indicating a weak relationship. The highest correlation observed (0.756) was between the ratio system and the full multiplicative approach, suggesting consistency between these two ranking approaches.
Discussion
In the Multi-MOORA technique, three ranking approaches were applied. The first approach, the ratio system, calculated the performance value (y) for traffic equipment indicators across the fifteen districts of Isfahan metropolis. The results indicated that District 10 achieved the highest score, while District 11 received the lowest.
The reference point method, as the second approach, used calculated reference values for the traffic indicators as a baseline. District 8 achieved the highest score in this approach, while Districts 5, 11, 12, and 14 scored the lowest.
The third approach, the full multiplicative method (U), showed that District 6 obtained the highest score. Districts 10, 3, and 1 also received high scores, whereas Districts 5, 9, 11, 12, 14, and 15 had the lowest scores.
Using dominance theory, the rankings from these three approaches were integrated to determine the final ranking. The results indicated that District 10 had the best overall performance, while District 11 had the worst.
These findings align with Hernández (2017), who reports that transport equipment indicators in Montevideo were neither fairly nor optimally distributed. Their study shows that public transport coverage in Montevideo, the capital and largest port city of Uruguay, effectively serves a large population across the geographic area.
Similarly, Lope & Dolgun (2020) have observed inequalities in transport indicators in Melbourne, particularly regarding accessible trams, where service for people with disabilities was significantly lower than for the general population. They suggest that these inequalities could guide future transport investment.
Akbari (2021) also has found inequalities in transport indicators in Tehran using the AIDAS technique. Tehran scores the highest (1.000) among studied metropolises, while Mashhad scores 0.549 and Isfahan 0.508, showing a smaller gap between Mashhad and Isfahan. Other studied cities show weaker performance.
A key limitation for such studies in Iran is the lack of traffic equipment in most cities, especially small and medium-sized ones. Urban furniture, such as traffic equipment installed on pavements and streets, is essential to ensure order and safety for citizens. Therefore, enhancing traffic infrastructure is crucial for urban management, particularly in medium-sized cities.
In Isfahan, special attention is needed for:
- Intersections with traffic lights in Districts 2, 9, 11, 13, 14, and 15.
- Intersections with intelligent systems in Districts 11, 13, and 15.
- Intersections with flashing signals in District 9.
- Intersections with centrally controlled traffic lights in Districts 11 and 12.
- Parameter message signs in Districts 9, 11, 14, and 15.
Currently, urban mobility in most Iranian cities is inadequate, and without corrective and preventive measures, it could become a severe crisis. Thus, Iranian cities must adopt sustainable transport management principles as the foundation for future urban transport planning.
Conclusion
Analysis of the differences in scores obtained from the three Multi-MOORA approaches, ratio system, reference point, and full multiplicative, combined through dominance theory, reveals inequalities in traffic and transport equipment across the fifteen districts of Isfahan metropolis.
Acknowledgments: Not reported by the author.
Ethical Permission: Not reported by the author.
Conflict of interest: Not reported by the author.
Author contribution: Mahmoud Akbari is the sole author of the article.
Funding: Not reported by the author.
Keywords:
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