پیاده‌‏روی در شهر: استفاده از الگوریتم‏‌های یادگیری ماشین در جهت ارزیابی قابلیت پیاده‌‏مداری در فضای باز عمومی شهر شیراز

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترای شهرسازی، دانشکده معماری و شهرسازی، دانشگاه آزاد اسلامی، واحد اصفهان (خوراسگان)، اصفهان، ایران.

2 استاد گروه معماری، دانشکده معماری و شهرسازی، دانشگاه هنر اصفهان، اصفهان، ایران (نویسنده مسئول).

3 استادیار گروه شهرسازی، دانشکده معماری و شهرسازی، دانشگاه آزاد اسلامی، واحد اصفهان (خوراسگان)، اصفهان، ایران.

10.22034/aaud.2023.362677.2717

چکیده

بررسی ساختار شهری در جهت افزایش فعالیت‌های اجتماعی و پیاده‌روی در جهت افزایش تعاملات و سرزندگی شهری مبحثی است که توسط محققین زیادی بررسی شده است. از این رو توجه به ساختارهایی که از یک طرف پیاده‌روی را در فضاهای عمومی شهر افزایش دهند و از طرفی منجر به افزایش مدت‌زمان پیاده‌روی در فضای عمومی شوند بسیار حائز اهمیت است. زیرا منجر به افزایش کیفیت زندگی، سلامتی شهروندان می‌شود. پیچیده کردن ماهیت توسعه و الگوهای ساختار شهر، ما را نیازمند توجه به ابعاد مختلف در جهت شناخت عوامل تأثیرگذار در افزایش پیاده‌روی شهروند در ساختارهای مختلف شهری می‌کند. از این رو روش تحقیق در این پژوهش استفاده از روش پرسش‌نامه و یادگیری ماشین با استفاده از روش رگرسیون می‌باشد. استفاده از روش یادگیری ماشین به محقق امکان بررسی رابطه‌های پیچیده بین متغیرهای مختلف را می‌دهد. هدف از این تحقیق بررسی روابطه آشکار و پنهان بین عوامل مختلف محیطی تأثیرگذار بر روی پیاده‌روی شهروندان در کلان‌شهر شیراز می‌باشد. نتایج این پژوهش نشان می‌دهد که بیش‌ترین تأثیرگذاری را بر میزان زمان پیاده‌روی در فضای شهری پنج عامل حریم خصوصی فردی، بهبود دسترسی به فضاهای عمومی، خالی از زباله، معماری باکیفیت و اتصال عناصر مختلف و فرصت کافی برای حرکت عابران پیاده بیش‌ترین و کم‌ترین تأثیر را عامل نظارتی نظیر حضور بیش‌تر پلیس، توجه بیش‌تر به ایمنی و امنیت شخصی افراد، سیستم دوربین مداربسته دارند. به صورت کلی به وسیله این 16 عامل تأثیرگذار بر پیاده‌مداری می‌توان بیش‌ترین میزان زمان پیاده‌روی در روز را با تقریب 92 درصد پیش‌بینی نمود. نتایج تحقیق می‌تواند توسط مدیران، طراحان و برنامه‌ریزان شهری در جهت افزایش سرزندگی و فعالیت‌های شهری به‌خصوص پیاده‌روی به‌کار گرفته شود.

کلیدواژه‌ها


عنوان مقاله [English]

Walking in the city: using machine learning algorithms to evaluate the ability to walk in the public open space of Shiraz city

نویسندگان [English]

  • tahere edalat 1
  • Mohammad Masoud 2
  • Golrokh kopaie 3
1 St-phd urbanization Department of Architecture and Urban Planning, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
2 PhD in Urbanism, Associate Professor of the Faculty of Architecture and Urban Planning of Isfahan University of Art
3 Assistant Professor, Faculty of Architecture and Urban Planning, Khorasan Azad University
چکیده [English]

Numerous scholars have examined the issue of examining urban layout in order to encourage social activities and walking to improve interactions and urban life. Therefore, it is crucial to pay close attention to structures that, on the one hand, encourage more walking in the city's public areas and, on the other hand, extend the length of walking in the public space. Because it improves the quality of life and health of the populace. Complicating the nature of growth and city structure patterns, we must pay attention to several aspects in order to identify the elements that influence the increase in citizen walking in diverse urban structures and use more complicated methodologies. Consequently, the questionnaire approach and machine learning employing the regression method will be used in this work. Using machine learning allows the researcher to investigate the intricate interactions between many factors. The objective of this study is to analyses the overt and covert interactions between numerous environmental elements that influence residents' walking in Shiraz's metropolitan area. The findings of this study indicate that the five factors of personal privacy, improving access to public spaces, free of garbage, high-quality architecture and connecting different elements, and sufficient opportunity for pedestrian movement have the greatest influence on the amount of time spent walking in the urban environment. The element with the least influence is supervision, which includes a larger police presence, a greater emphasis on personal safety and security, and a CCTV system. In general, the maximum amount of walking time per day may be predicted with roughly 92% accuracy using these 16 criteria. Managers, designers, and urban planners may apply the findings to promote urban vibrancy and activities, particularly walking.

کلیدواژه‌ها [English]

  • Machine learning
  • regression
  • pedestrian orbit
  • public open space
  • Shiraz city
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