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غلامی, یونس, مولوی, ابراهیم, دالوند, هنگامه. (1401). مدل‌سازی ساختاری تأثیر عوامل زمینه‌ای در توسعه حمل‌ونقل فعال در دوران اپیدمی کرونا در شهر دورود. , 10(4), 197-210. doi: 10.22059/jurbangeo.2023.346968.1725
یونس غلامی; ابراهیم مولوی; هنگامه دالوند. "مدل‌سازی ساختاری تأثیر عوامل زمینه‌ای در توسعه حمل‌ونقل فعال در دوران اپیدمی کرونا در شهر دورود". , 10, 4, 1401, 197-210. doi: 10.22059/jurbangeo.2023.346968.1725
غلامی, یونس, مولوی, ابراهیم, دالوند, هنگامه. (1401). 'مدل‌سازی ساختاری تأثیر عوامل زمینه‌ای در توسعه حمل‌ونقل فعال در دوران اپیدمی کرونا در شهر دورود', , 10(4), pp. 197-210. doi: 10.22059/jurbangeo.2023.346968.1725
غلامی, یونس, مولوی, ابراهیم, دالوند, هنگامه. مدل‌سازی ساختاری تأثیر عوامل زمینه‌ای در توسعه حمل‌ونقل فعال در دوران اپیدمی کرونا در شهر دورود. , 1401; 10(4): 197-210. doi: 10.22059/jurbangeo.2023.346968.1725

مدل‌سازی ساختاری تأثیر عوامل زمینه‌ای در توسعه حمل‌ونقل فعال در دوران اپیدمی کرونا در شهر دورود

پژوهش‌های جغرافیای برنامه‌ریزی شهری
مقاله 11، دوره 10، شماره 4، بهمن 1401، صفحه 197-210    اصل مقاله (1.34 M)
نوع مقاله: پژوهشی - کاربردی
شناسه دیجیتال (DOI): 10.22059/jurbangeo.2023.346968.1725
نویسندگان
یونس غلامی* 1؛ ابراهیم مولوی2؛ هنگامه دالوند1
1گروه جغرافیا و گردشگری، دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان، کاشان، ایران
2گروه طراحی شهری، دانشکده هنر و معماری، دانشگاه بوعلی سینا، همدان، ایران
چکیده
چکیده
اپیدمی کرونا یکی از مهم‌ترین مسائل روز دنیاست که حمل‌ونقل شهری را به‌طور قابل‌توجهی تغییر داده است و نقاط ضعف روش‌های حمل‌ونقل فعلی را بیشتر برجسته کرده است. این بحران فرصتی منحصربه‌فرد برای طراحی مجدد برنامه‌های حمل‌ونقل شهری به شیوه‌ای پایدارتر و مقاوم‌تر را فراهم کرده است. هدف اصلی پژوهش حاضر ارزیابی تأثیر عوامل زمینه‌ای در توسعه حمل‌ونقل فعال در دوران اپیدمی کرونا در شهر دورود می‌باشد. این پژوهش از نوع کاربردی و بر اساس روش توصیفی – تحلیلی می‌باشد و با ابزار پرسش‌نامه محقق ساخته تأثیر عوامل زمینه‌ای ازجمله سن و جنس و تحصیلات و... را بر حمل‌ونقل فعال در دوران اپیدمی کرونا با استفاده از مدل‌سازی معادلات ساختاری موردبررسی قرار داده است. جامعه آماری پژوهش شامل 180 نفر از ساکنان شهر دورود می‌باشد که با استفاده از نرم‌افزار موردنیاز و با سطح اطمینان 95 درصد محاسبه‌شده است. تحلیل یافته‌های منتج بیانگر آن است که در میان عوامل موثر بر توسعه حمل‌ونقل فعال در دوران اپیدمی کرونا عوامل اجتماعی و سلامت با بیشترین بار عاملی مؤثرترین عوامل و در میان عوامل زمینه‌ای موثر بر توسعه حمل‌ونقل فعال نیز، سن تأثیرگذارترین و درآمد خانوار کمترین تأثیر را در انتخاب حمل‌ونقل فعال در دوران شیوع اپیدمی کرونا داشته‌اند. نتایج پایانی نشانگر آن است که عوامل زمینه‌ای در دوران شیوع این اپیدمی در شهر دورود اثر بسیار زیادی بر توسعه حمل‌ونقل فعال در این دوران داشته است.
کلیدواژه‌ها
کرونا؛ حمل‌ونقل فعال؛ عوامل زمینه‌ای؛ شهر دورود؛ مدل‌سازی ساختاری
عنوان مقاله [English]
Structural modelling of the influence of background factors in the development of active transportation during the Corona epidemic in Durood City
نویسندگان [English]
Yones Gholami1؛ Ebrahim Molavi2؛ Hengame Dalvand1
1Department of Geography and Tourism, Faculty of Natural Resources and Earth Sciences, Kashan University, Kashan, Iran
2Department of Urban Design, Faculty of Art and Architecture, Boali Sina University, Hamadan, Iran
چکیده [English]
A B S T R A C T
The Corona epidemic is one of the most important issues in the world today, which has significantly changed urban transportation and highlighted the weaknesses of current transportation methods. This crisis has provided a unique opportunity to redesign urban transportation programs in a more sustainable and resistant way. The main goal of the recent research is to evaluate the impact of contextual factors in the development of active transportation during the Corona epidemic in Durood City. This research is of an applied type and based on the descriptive-analytical method, and with the help of a questionnaire, the researcher investigated the effect of background factors such as age, gender, education, etc. on active transportation during the Corona epidemic. The use of structural equation modelling has been investigated. The statistical population of the research includes 180 residents of Durood City, which was calculated using the required software and with a confidence level of 95%. The analysis of the resulting findings shows that among the factors affecting the development of active transportation during the Corona epidemic, social and health factors with the highest factor load are the most effective factors, and among the background factors affecting the development of active transportation, age is the most influential and Household income had the least influence on the choice of active transportation during the outbreak of the Corona epidemic. The final results indicate that the background factors during the outbreak of this epidemic in Durood City greatly affected the development of active transportation during this period.
Extended Abstract
Introduction
COVID-19 is an infectious disease caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The World Health Organization (WHO) has classified this epidemic as a global epidemic (Wielechowski et al, 2020:1). The COVID-19 epidemic has severely affected many social and economic activities, transportation being one of them (Przybylowski et al, 2021:8). Unprecedented measures such as travel restrictions and gathering restrictions implemented by many countries are one of the most significant impacts the pandemic has had on transportation. And the act to observe social distancing during the COVID-19 pandemic was a sudden restriction that, along with a sharp reduction in travel volume and changes in activity patterns, contributed to sudden changes in the way of transportation in cities around the world (Ciuffini et al, 2021:3). . As a result, rapid and intense changes in people's movement styles (that is, lifestyle about mobility, travel habits, etc.) appear from other aspects of their behaviour in travel. Movement styles are usually resistant to change under normal circumstances, but necessarily during a prolonged and severe pandemic, the unique circumstances introduced by this new pandemic will change many habits and preferences. re-examine themselves and as a result, significant changes are made in people's methods (Shamshiripour et al, 2020:1). Therefore, the relationship between transportation and diseases, as well as how it affects travel behaviour, has been investigated. Following these cases, it is also very important to understand the effects of the pandemic on travel demand (Dingil & Esztergár-Kiss, 2021:1). One of the most important topics of the day is to examine the issues related to the effects of the Corona epidemic on cities. One of these issues is related to the change in the use of transportation in cities, due to the various measures taken by the government to control this epidemic, the amount of transportation during the quarantine period using other means of transportation has decreased and people are facing problems. Moved towards the use of active transport. In this research, using the structural equation model, the effect of factors such as age, gender, education, distance from the city centre, and family income on the components of active transportation during the corona epidemic was investigated, and finally, a model was drawn that the relationship between shows them. and shows the most effective factors.
 
Methodology
 The current research method is descriptive-analytical and applied in terms of purpose. Data collection has been done using the field method and questionnaire tool and its random distribution in the areas of Durood City. The statistical population of this research includes Durood City with a population of 121,638 people. The sample size was calculated using sample power software with a confidence level of 95% and a possible error of 5% from 180 people. To explain and model the effects, structural equation modelling (SEM) was used in Amos software. Cronbach's alpha test was used to measure the reliability of the research tool, which was taken separately for each of the main indicators.
 
Results and discussion
In this part of the research, the findings have been analyzed and the research hypotheses have been answered. Structural equation modelling has been used for the scientific analysis of this research. The findings of this hypothesis, which shows the influence of background factors in the development of active transportation during the epidemic period, show that the social factor and health with a factor weight of 0.94 have the highest factor load, followed by the social factor with factor A. The economic coefficient of 0.94 ranks second and the economic coefficient of 0.60 has the lowest factor load. Among the relevant factors, age with a factor load of 0.68 is the most effective factor, and distance from the city centre is the second most effective factor, followed by gender and education, and finally, household income is the most effective factor. The coefficient of 0.18 had the least effect on the choice of active transportation during the outbreak of the Corona epidemic.
 
Conclusion
The sudden spread of the corona epidemic in the world has severely affected transportation, these effects have made people use active means of transportation, which can have the greatest effect in controlling this epidemic. And active transmission can be a key strategy to reverse the burden of communicable diseases that require social distancing. In the structural equation model that examines the impact of background factors on the development of active transportation, the results show that among the background factors, age is the most important factor, and with increasing age, the use of active transportation, especially bicycles. is. The ride is less. The second most effective factor in choosing active transportation during the Corona epidemic is the distance from the city centre. This factor indicates active transportation planning in medium and small cities where the residence is less far from the workplace or the city centre. They will be more successful. Gender, the next influential factor in this research, shows that women use bicycles less and walk more. It is the opposite for men. Education and income are also affected in the next categories, which shows that the level of education of individuals or household income can also have important effects on choosing the type of transportation during the epidemic. Finally, background factors have had a significant impact on the spread of Corona in Durood City. Finally, during this pandemic, countries face an opportunity to revise strategic goals to reduce the risk of increasing active transportation infrastructure. The path to sustainability can be improved by temporary measures that enable the increase of active transport, and this epidemic and its medium- and long-term consequences compel us to propose an emerging research program to investigate sustainable and healthy urban mobility with comprehensive guidelines. do. Epidemic diseases in the future, it is necessary to pay attention to the fact that today's policies may have long-term consequences.
 
Funding
There is no funding support.
 
Authors’ Contribution
All of the authors approved the content of the manuscript and agreed on all aspects of the work.
 
Conflict of Interest
The authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.
کلیدواژه‌ها [English]
Corona, Active Transportation, Contextual factors, Durood city, Structural modelling
مراجع
  1. Aguilar-Farias, N., Cortinez-O’Ryan, A., Chandia-Poblete, D., & Heesch, K. C. (2019). Prevalence and correlates of transport cycling in Chile: results from 2014 to 2015 national surveys. Journal of Transport & Health, 14, 100594
  2. Biehl, A., & Stathopoulos, A. (2020). Investigating the interconnectedness of active transportation and public transit usage as a primer for Mobility-as-a-Service adoption and deployment. Journal of Transport & Health, 18, 100897
  3. Buehler, R., & Pucher, J. (2021). COVID-19 Impacts on Cycling, 2019–2020. In Transport Reviews, 41)4(, 393–400.
  4. Christiana, R. W., Bouldin, E. D., & Battista, R. A. (2021). Active living environments mediate rural and non-rural differences in physical activity, active transportation, and screen time among.
  5. Ciuffini, F., Tengattini, S., & Bigazzi, A. Y. (2021). Mitigating Increased Driving after the COVID-19 Pandemic: An Analysis on Mode Share, Travel Demand, and Public Transport Capacity. Transportation Research Record, 03611981211037884.
  6. Connolly, C., Livy, M. R., Qiu, Y., & Klaiber, H. A. (2019). Capitalization of interconnected active transportation infrastructure. Landscape and urban planning, 182, 67-78.
  7. Cusack, M. (2021). Individual, social, and environmental factors associated with active transportation commuting during the COVID-19 pandemic. Journal of Transport & Health, 22, 101089.
  8. Dingil, A. E., & Esztergár-Kiss, D. (2021). The Influence of the Covid-19 Pandemic on Mobility Patterns: The First Wave’s Results. Transportation Letters, 13(5-6), 434-446.
  9. Eldeeb, G., Mohamed, M., & Páez, A. (2021). Built for active travel? Investigating the contextual effects of the built environment on transportation mode choice. Journal of Transport Geography, 96, 103158.
  10. Fyhri, A., Ciccone, A., Papaix, C., & Karlsen, K. (2023). Does Active Transport Lead to Improved Mood and Performance? A Panel Study of Travel Changes During the Covid-19 Lockdown in Norway. A Panel Study of Travel Changes During the Covid-19 Lockdown in Norway, 94, 114-132.
  11. Giles-Corti, B., Foster, S., Shilton, T., & Falconer, R. (2010). The co-benefits for health of investing in active transportation. New South Wales public health bulletin, 21(6), 122-127.
  12. Gkiotsalitis, K., & Cats, O. (2021). Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions. Transport Reviews, 41(3), 374-392.
  13. González, E. V., Barranco-Ruiz, Y., & Garzón, P. C. (2022). Active transport to school as a prevention policy in the COVID19 pandemic. Journal of Movement & Health, 19(1), 1-12.
  14. Greaves, S., Beck, M., Cobbold, A., Standen, C., Rissel, C., & Crane, M. (2022). Working from Home, Active Travel, Health and Wellbeing: Consequences of a Pandemic. Active Travel, Health and Wellbeing: Consequences of a Pandemic.
  15. Griffiths, S., Del Rio, D. F., & Sovacool, B. (2021). Policy mixes to achieve sustainable mobility after the COVID-19 crisis. Renewable and Sustainable Energy Reviews, 143, 110919.
  16. Gutiérrez, A., Miravet, D., & Domènech, A. (2020). COVID-19 and urban public transport services: emerging challenges and research agenda. Cities & Health, 5(1), 177-180.
  17. Heydarzadeh, Ehsan Vahghi, Mohammad Reza, (2022) Evaluation of the success of urban sidewalks in gaining citizens' satisfaction, a comparative comparison of sidewalks in Hafzieh and Eram cities in Shiraz. Geographical Research on Urban Planning, 10(1), 63-79. [In Persian]
  18. Housing and urban development. (2009). Durood city development and construction plan, analysis and conclusions from the city's urban development plans and plans, Emco Consulting Engineers Company. [In Persian]
  19. Jafari, A., Both, A., Singh, D., Gunn, L., & Giles-Corti, B. (2022). Building the road network for city-scale active transport simulation models. Simulation Modelling Practice and Theory, 114, 102398.
  20. Jamal, S., Mohiuddin, H., & Paez, A. (2020). How do the perceptions of neighborhood conditions impact active transportation? A study in Rajshahi, Bangladesh. Transportation Research Part D: Transport and Environment, 87, 102525.
  21. Jáuregui, A., Lambert, E. V., Panter, J., Moore, C., & Salvo, D. (2021). Scaling up urban infrastructure for physical activity in the COVID-19 pandemic and beyond. The Lancet, 398(10298), 370-372.
  22. Kim, E. J., Kim, J., & Kim, H. (2020). Neighborhood walkability and active transportation: a correlation study in leisure and shopping purposes. International journal of environmental research and public health, 17(7), 2178.
  23. Li, X., Farrukh, M., Lee, C., Khreis, H., Sarda, S., Sohrabi, S., & Dadashova, B. (2022). COVID-19 impacts on mobility, environment, and health of active transportation users. Cities, 131, 103886.
  24. Mashrur, S. M., Wang, K., & Habib, K. N. (2022). Will COVID-19 be the end for the public transit? Investigating the impacts of public health crisis on transit mode choice. Transportation Research Part A: Policy and Practice, 164, 352-378.
  25. Mogaji, E. (2020). Impact of COVID-19 on transportation in Lagos, Nigeria. Transportation research interdisciplinary perspectives, 6, 100154.
  26. Molvi, Mehrnaz, Hamidi, Arman, Faridi Fashtami, Alia, Aria Pasand, Zahra (2021) Investigating the role of urban sidewalks in improving the indicators of urban vitality and social interactions (Case study: The pedestrian walkway in the center of Rasht). geographical studies Urban Planning, (3), 881-908
  27. Naveen, B. R., & Gurtoo, A. (2022). Public transport strategy and epidemic prevention framework in the Context of Covid-19. Transport policy, 116, 165-174.
  28. Nian, G., Peng, B., Sun, D. J., Ma, W., Peng, B., & Huang, T. (2020). Impact of COVID-19 on Urban Mobility during Post-Epidemic Period in Megacities: From the Perspectives of Taxi Travel and Social Vitality. Sustainability, 12(19), 7954.
  29. Nieuwenhuijsen, M. J., Hahad, O., & Münzel, T. (2022). The COVID-19 pandemic as a starting point to accelerate improvements in health in our cities through better urban and transport planning. Environmental Science and Pollution Research, 29(12), 16783-16785.
  30. Park, K., Singleton, P. A., Brewer, S., & Zuban, J. (2022). Pedestrians and the Built Environment during the COVID-19 Pandemic: Changing Relationships by the Pandemic Phases in Salt Lake County. Utah, USA. Transportation Research Record, 03611981221083606.
  31. Passi-Solar, Á., Margozzini, P., Cortinez-O’Ryan, A., Muñoz, J. C., & Mindell, J. S. (2020). Nutritional and metabolic benefits associated with active and public transport: Results from the Chilean National Health Survey, ENS 2016–2017. Journal of Transport & Health, 17, 100819.
  32. Peng, B., Erdoğan, S., Nasri, A. A., & Zou, Z. (2021). Towards a health-conscious transportation planning: a framework for estimating health impacts of active transportation at local level. Journal of Transport & Health, 22, 101231.
  33. Przybylowski, A., Stelmak, S., & Suchanek, M. (2021). Mobility behaviour in view of the impact of. https://doi.org/10.3390/su13010364 the COVID-19 pandemic—public transport users in Gdansk case study. Sustainability, 13(1), 364.
  34. Reche, C., Tobias, A., & Viana, M. (2022). Vehicular Traffic in Urban Areas: Health Burden and Influence of Sustainable Urban Planning and Mobility. Atmosphere, 13(4), 598.
  35. Sá, T. H. D., Rezende, L. F. M. D., Borges, M. C., Nakamura, P. M., Anapolsky, S., Parra, D., ... & Monteiro, C. A. (2018). Prevalence of active transportation among adults in Latin America and the Caribbean: a systematic review of population-based studies. Revista Panamericana de Salud Publica, 41, 1-35.
  36. Said, M., Biehl, A., & Stathopoulos, A. (2021). Interdependence in active mobility adoption: Joint modeling and motivational spillover in walking, cycling and bike-sharing. International Journal of Sustainable Transportation, 16(5) 422-440.
  37. Saneinejad, S., Roorda, M. J., & Kennedy, C. (2012). Modelling the impact of weather conditions on active transportation travel behaviour. Transportation research part D: transport and environment, 17(2), 129-137.
  38. Shamshiripour, A., Rahimi, E., Shabanpour, R., & Mohammadian, A. K. (2020). How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transportation Research Interdisciplinary Perspectives, 7, 100216.
  39. Sharifi, A., & Khavarian-Garmsir, A. R. (2020). The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management. Science of The Total Environment, 749 142391.
  40. Shortall, R., Mouter, N., & Van Wee, B. (2022). COVID-19 passenger transport measures and their impacts. Transport Reviews, 42(4), 441-466.
  41. Statistics Center of Population and Housing Census. (2016). Iran, Mehr 2016, Lorestan Province. [In Persian]
  42. Stockton, E. (2021). Active transportation policy decisions in response to COVID-19: Case studies from four North American cities. https://doi.org/10.17615/FC6J-KS40
  43. Subbarao, S. S., & Kadali, R. (2022). Impact of COVID-19 pandemic lockdown on the public transportation system and strategic plans to improve PT ridership: A review. Innovative Infrastructure Solutions, 7(1), 1-14.
  44. Tirachini, A., & Cats, O. (2020). COVID-19 and public transportation: Current assessment, prospects, and research needs. Journal of Public Transportation, 22(1), 1-12.
  45. Vallejo-Borda, J. A., Giesen, R., Basnak, P., Reyes, J. P., Lira, B. M., Beck, M. J., & de Dios Ortúzar, J. (2022). Characterising public transport shifting to active and private modes in South American capitals during the Covid-19 pandemic. Transportation Research Part A: Policy and Practice, 164, 186-205.
  46. Wielechowski, M., Czech, K., & Grzęda, Ł. (2020). Decline in Mobility: Public Transport in Poland in the time of the COVID-19 Pandemic. Economies, 8(4), 78.
  47. Young, D. R., Cradock, A. L., Eyler, A. A., Fenton, M., Pedroso, M., Sallis, & J. F., (2020). American Heart Association Advocacy Coordinating Committee. Creating built environments that expand active transportation and active living across the United States: a policy statement from the American Heart Association. Circulation, 142(11), 167-183.
  48. Younkin, S. G., Fremont, H. C., & Patz, J. A. (2021). The Health-Oriented Transportation Model: Estimating the health benefits of active transportation. Journal of Transport & Health, 22, 101103.
  49. Zhang, J., & Hayashi, Y. (2022). Research frontier of COVID-19 and passenger transport: A focus on policymaking. Transport Policy, 119, 78-88)
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