اولویت بندی مولفه های مورد نیازپارکینگ های هوشمند در محیط های شهری (مطالعه موردی شهر مرودشت)

فصلنامه علمی و پژوهشی پژوهش و برنامه ریزی شهری

مقاله 13، دوره 12، شماره 45، مرداد 1400، صفحه 203-218   

نوع مقاله: مقاله پژوهشی

شناسه دیجیتال (DOI): 10.30495/jupm.2021.4152

نویسندگان

معصومه شریف زاده^{* 1}؛ میثم حیدری²؛ امین کشاورزی³

¹کارشناسی ارشد ، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد مرودشت، مرودشت، ایران.

²کارشناسی ارشد شهرسازی- برنامه ریزی شهری، دانشگاه آزاد اسلامی واحد مرودشت، مرودشت، ایران.

³استادیار دانشکده فنی ومهندسی، دانشگاه آزاد اسلامی واحد مرودشت، مرودشت، ایران.

تاریخ دریافت: 22 تیر 1398،  تاریخ بازنگری: 14 دی 1398،  تاریخ پذیرش: 17 بهمن 1398 

چکیده

امروزه تاثیر فناوری اطلاعات و هوش مصنوعی در هیچ یک از جنبه های زندگی بشر قابل کتمان نیست. از آغاز هزاره سوم تاکنون همگرایی فناوریهای نانو، زیست فناوری، اطلاعات و ارتباطات و علوم شناختی در قالب حوزه ای واحد به نام NBIC1و استفاده از آن در مدیریت شهری و هوشمندسازی شهرهای از پیش موجود یا طراحی هوشمند شهرهای نوپدید روند رو به رشدی داشته است. اقتصاد هوشمند، حمل و نقل هوشمند، محیط زیست هوشمند، شهروندان هوشمند، سبک زندگی هوشمند و مدیریت اداری هوشمند از جمله موارد مورد نیاز در یک شهر هوشمند می باشند. در این بین، پارکینگ هوشمند به عنوان یکی از موارد تاثیر گذار در حمل و نقل هوشمند از اهمیت بالایی برخوردار است. پارکینگ هوشمند دارای 6 معیار سخت افزاری، نرم افزاری، سیستم تشخیص، سیستم پرداخت، سنسورها و تابلوهای راهنما می باشد. مجموع این معیارها دارای 32 مولفه است. با توجه به پروژه های هوشمند سازی انجام شده، کمتر پارکینگی را می توان یافت که همه‌ی این مولفه ها را دارا باشد. لذا اولویت بندی این مولفه ها در فرایند هوشمند سازی پارکینگ های شهر مرودشت از نگاه متخصصین این حوزه در این مقاله مورد بررسی قرار گرفته است. این پژوهش از لحاظ روش، نظری و متکی بر تحلیل و توصیف است که تحلیل‌های آن، ترکیبی از تحلیل‌های کیفی و کمّی است که در آن اولویت بندی شاخص های پارکینگ هوشمند با استفاده از آزمون و رتبه بندی فریدمن و با استفاده از نرم افزار spss انجام شده است. نتایج این بررسی نشان می دهد پارکینگ های شهر مرودشت از نظر هوشمند سازی در وضعیت مناسبی قرار ندارند و در بهترین حالت میزان هوشمند سازی پارکینگ های شهر مرودشت 47 در صد می باشد.

کلیدواژه‌ها

شهر هوشمند؛ پارکینگ هوشمند؛ مولفه های هوشمند؛ شهرستان مرودشت؛ آزمون فریدمن

عنوان مقاله [English]

Prioritizing Components of Smart Parking Needs in Urban Areas (Case Study of Marvdasht City)

نویسندگان [English]

Masoomeh Sharifzadeh¹؛ Rasoul Fereidouni²؛ amin keshavarzi³

¹M.Sc., Faculty of Engineering, Islamic Azad University, Marvdasht Branch, Marvdasht, Iran.

²Master of Urban Planning - Urban Planning, Islamic Azad University, Marvdasht Branch, Marvdasht, Iran.

³Assistant Professor, Faculty of Engineering, Islamic Azad University, Marvdasht Branch, Marvdasht, Iran.

چکیده [English]

Today, the impact of information technology and artificial intelligent can’t be ignored in any aspect of human life. From the beginning of the third millennium until now, convergence of technologies such as Nano technology, Biotech, Information and Communication and Cognitive Sciences into a unified area, which is called NBIC, and using it in urban management and smarting up existing cities or designing new smart cities has been a growing trend. Smart Economy, Smart Transportation, Smart Environment, Smart Citizens, Smart Lifestyle and Smart Office Management are some items that require in a smart city. From these, smart parking, as one the most impressive item in smart transportation, is very important. Smart parking has 6 criterion consist of: hardware, software, detection system, payment system, sensors and stands. The total of these criteria has 32 components. Regarding to the smart projects carried out, less smart parking can be found that has all of these components. Therefore, prioritization of these components in the process of smart parking in Marvdasht city from experts view points has been studied by experts in this paper. This research is theoretical from method viewpoint, and is based on analysis and description, that its analyzation is a combination of quantitative and qualitative analysis that in it prioritization of smart parking indices was performed using Friedman test and implementing using SPSS software. The results of this research reveal that 81 percent of Marvdasht parkings use smart cards, and 80 percent of them use parking management system.   .Also,  the results of this research show that smart parking of Marvdasht city aren’t in a good situation from smarting viewpoint, and in the best cast the amount of smarting in Marvdasht parking is only 47 percent.
Introduction
As a complex and dynamic phenomenon, the city is constantly undergoing physical, social, economic, political and cultural changes over time. Such large-scale developments are due to the widespread growth of the urban population; As after World War II, one of the most important problems in developing countries has been the rapid and heterogeneous growth and development of urbanization. The process of smartening is common in cities around the world in two ways: creating smart cities and making existing cities smarter. Given that most of the existing cities in developing countries such as Iran have the idea of smartening, they are faced with not existing or lack of these infrastructures. The number of vehicles in cities is increasing every year, and this increase is causing congestion and increasing air pollution, especially in the central areas of cities. One of the reasons for the increase in traffic in metropolitan areas is the lack of parking space for drivers in the city. Due to the fact that drivers and vehicles are the main components of traffic, and the time for cars to stop in cities is much longer than their travel time. Traffic control and the existence of a suitable and safe space for parking vehicles are considered as one of the important factors in creating the welfare and comfort of the people. The main questions of this research are:
Is it possible to determine the effective components in smart parking parking using Friedman ranking?
Are the parking lots in Marvdasht in good condition?
What is the most used component in the parking lots of Marvdasht city?
 
Research Method
In terms of method, this research is theoretical and relies on analysis and description, and its analysis is a combination of qualitative and quantitative analysis. It is also, by its nature, a type of applied research. The process was such that after identifying and prioritizing the components of smart parking, based on the experiences of the world's smart projects and the opinion of top thinkers, the compliance of these components for parking lots in Marvdasht city will be evaluated. The collection of information required for this research was initially obtained through documentary and field studies. The work process is such that after identifying the components, criteria and indicators of smart parking lots based on the experiences of smart parking projects and the opinion of top thinkers in this field, these indicators are prioritized for implementation in the smart parking project of Marvdasht city. 32 parameters have been investigated for smart parking. The measurement tool in this study was the Fish for library stage and the questionnaire form for the second stage. The required information of the research variables has been analyzed in both quantitative and qualitative ways. The information obtained from the library-documentary stage was analyzed qualitatively and finally the information obtained from the second questionnaire was analyzed quantitatively. In order to prioritize the smart parking indexes of Marvdasht city, Friedman test and ranking have been used. The SPSS software has been used to perform the mentioned test and ranking. Due to the significant differences in scores related to 32 indicators, the mentioned indicators are prioritized for Marvdasht city with the highest score to the lowest score, respectively.
Results
This study shows that the percentage of parking lots that use smart cards in Marvdasht city is 81% and also over 80% of parking lots in the city use parking management software, 16% of parking lots studied by the system have numberplate detection and none of the existing parking lots are equipped with ground sensors. In 16% of existing parking lots, the cost is automatically calculated by the software. In 80% of cases, the cost of parking is displayed on the screen. Also, the electronic payment of expenses, which is a common method in daily payments today, is only 32%, which is a matter for consideration. Parking space is not booked through smart systems in any of the city's parking lots. Intra-city signs, which show the number of empty parking spaces, are about 30 percent. Also, none of the parking lots in question are equipped with Closed Camera Television (CCTV), which is a matter for consideration. Nearly 33 percent of research parking lots use smart cards. In 16% of cases, space is empty, full, reserved or special for the disabled people are displayed. None of the studied parking lots show the instantaneous counting of parked cars, and none of the parking lots surveyed can compare the photos taken at the time of entry and exit of the car. Finally, 80% of parking lots are equipped with entry and exit traffic lights.
The findings show that parking lot No. 1 (parking lot of Persepolis complex) has nearly 34% of the components of smart parking (components of smart parking studied in this article). Parking No. 2 (hospital parking) has about 46% of the components of smart parking. Parking No. 3 (University Parking) has the least use of smart parking tools, which is about 5%. Parking No. 4 (Municipal Parking) has about 47% of the components of smart parking, which is the highest use of smart parking components among the studied parking lots. Parking No. 5 (Sugar Factory Parking) at 40% and at the end of Parking No. 6 (Amirabad Street Parking) has about 23% of the components of smart parking.
 
Conclusion
In this study, the effective components in smart urban parking lots have been prioritized and the level of smart parking lots in Marvdasht city has been studied. They were classified and then, using the Friedman test, the level of smart parking in Marvdasht was investigated. The results show that the use of smart cards has the highest percentage of use of about 83% among parking lots in Marvdasht. Few results show that parking lots in Marvdasht are not in a good position in terms of intelligence, and the best parking lot is only 47% smart. Also, the parking lot of the huge Persepolis complex with a global status in terms of intelligence has only 5% of the stated criteria.

کلیدواژه‌ها [English]

Smart city, smart parking, smart parking components, Marvdasht city, friedman test

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