نوع مقاله : مقاله علمی پژوهشی
نویسندگان
1 دانشیار گروه سنجش از دور و سیستم های اطلاعات جغرافیایی، دانشکدة جغرافیا، دانشگاه تهران
2 دانشجوی دکتری سیستم های اطلاعات جغرافیایی، دانشکدة جغرافیا، دانشگاه تهران
3 کارشناس ارشد سنجش از دور و سیستم های اطلاعات جغرافیایی، دانشکدة جغرافیا، دانشگاه تهران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Abstract
Finding the right public parking lot for car parking is one of the major problems for citizens. Drivers spend a lot of time and distance finding the right public parking lot, which increases traffic, air pollution, fuel consumption, driver fatigue and confusion. In this regard, this article attempts to improve the process of parking searches and routing for citizens by providing a mobile GIS-based application. The location-aware program consists of three main modules, including two local parking management modules and comprehensive web-based parking management and a mobile-based parking locator module. The local parking management and comprehensive parking management modules collect information about each parking lot and transfer it to the Parking Finder module. Citizens can get the most appropriate parking and access path by prioritizing any of the criteria found in the Parking Finder module. The program was used to improve the routing of the park in Yazd. Then, a survey of 55 citizens using the system to find parking was conducted to evaluate the proposed plan. Survey results show that 36.4% of participants used the program more than 10 times a month and more in the afternoon (63.6%) and on weekends (20%). It has also reduced the time spent searching (54.5% less than 10 minutes and 16.4% more than 15 minutes) and increased the efficiency of using parking systems in Yazd.
Keywords: parking, mobile GIS, location-aware, routing, Yazd
Introduction
Finding the right public parking lot for car parking is one of the major problems for citizens. Drivers spend a great deal of time and distance finding the right public parking lot, which increases traffic, air pollution, fuel consumption, driver fatigue and confusion. On the other hand, the growing population of cities, especially in tourist areas, has led to the issue of finding a suitable parking spot and finding a lot of attention due to the lack of adequate parking lots and lack of adequate parking information. At the city level, this is facing many problems. Many cities are currently developing location-aware parking systems using ICT. These systems combine telecommunications, geographic information systems (GIS) and global positioning systems (GPS) to make parking information accessible to drivers when needed and the most effective way to navigate the empty spaces of parking lots. Designed as web applications, these systems are available from mobile devices or personal computers (laptops) .The main task of these applications is to select the most suitable parking lot based on the evaluation of available parking lots. To this end, these systems combine a set of criteria that affect the selection of a parking lot with the weight of each parking lot, using Multi-criteria Location Decision Analysis (GIS-MCDA). Introduce the largest parking lot.
Methodology
The proposed citizen-centered parking system consists of three main functional areas: local parking management, comprehensive parking management, and a functional parking lot. The information recorded in the local parking database is transmitted online to the comprehensive parking management system database and is provided through the embedded web services to the parking finder application to provide location and descriptive services.
Results and discussion
In this article, systems are designed and implemented as a mobile based application to service drivers to determine the most appropriate parking and access path. The program is made up of three main application areas, identifying the current location of each user and utilizing mobile device positioning technology, combining user-specific preferences to fit each of the criteria in the application. Provides the most secure parking. After applying weight and determining the importance of each of the existing criteria by the drivers, the proposed program introduces the most suitable parking lot among the available parking lots using WLC Multi-criteria Decision Analysis. The route to the desired parking lot is displayed on the map as text, audio and graph. Showing the shortest route to the parking lot can be very useful for drivers in busy traffic and busy hours, preventing additional routes and increasing traffic.
In order to evaluate the proposed program, 55 users of the questionnaire program were prepared in the form of 5 questions. The first question relates to how often the program is used during a month. 20 participants (36.4%), more than 10 times a month and more in the afternoon (35 people, 63.6%) and weekends (11%, 20%) of this program to find out They have used parking. This indicates that people in Yazd are facing severe parking problems at certain times of the day due to the increased traffic and intra-city traffic, and this program can be very useful and effective in finding suitable parking.
Conclusion
This article proposes a citizen-centered parking tracking system to improve parking search and routing in Yazd city. The innovative program combines instant parking information with drivers' personal preferences using mobile features to provide the most appropriate parking and accessibility. The program consists of three main application sections, two of which are web-based and one mobile-application. The web applications section is used by parking officials and the general manager of parking lots, and information about each parking lot is stored and recorded at any time. The mobile application segment is used by drivers and receives instant parking information online. In addition, this section shows the most suitable parking and the route of access to each driver, by prioritizing the criteria in the program through each driver. Implementation of this proposed city-wide program has shown that time spent searching for parking spaces has been significantly reduced, saving drivers time and money. Using this program also allows drivers to find the best parking in terms of distance, cost and number of vacancies by registering minimal information on their mobile phones. Providing parking location and access can also help drivers in busy city hours. However, in this research, no analysis has been conducted on the level of simplicity and usability of this system by citizens.
کلیدواژه‌ها [English]
11. Alemi, F.; Rodier, C. and Drake, C., 2018, Cruising and on-street parking pricing: A difference-in-difference analysis of measured parking search time and distance in San Francisco. Transportation Research Part A: Policy and Practice, Vol. 111, 187-198.
12. Alkheder, S. A.; Al Rajab, M. M. and Alzoubi, K., 2016, Parking problems in Abu Dhabi, UAE toward an intelligent parking management system “ADIP: Abu Dhabi Intelligent Parking”. Alexandria Engineering Journal, Vol. 55, No. 3, PP. 2679-2687.
13. Anitha, J.; Thoyajakshi, Y.; Ramya, A.; Sravani, V. and Kumar, P., 2017, Intelligent Parking System Using Android Application. International Journal of Pure and Applied Mathematics, Vol. 114, No. 7, PP. 165-174.
14. Bechini, A.; Marcelloni, F. and Segatori, A., 2013, A mobile application leveraging QR-codes to support efficient urban parking. In 2013 Sustainable Internet and ICT for Sustainability (SustainIT), PP. 1-3. IEEE.
15. Boroushaki, S. and Malczewski, J., 2010, ParticipatoryGlS: a web-based collaborative GIS and multicriteria decision analysis. Urisa Journal, Vol. 22, No. 1, PP. 23-32.
16. Cao, J. and Menendez, M., 2018, Quantification of potential cruising time savings through intelligent parking services. Transportation Research Part A: Policy and Practice, Vol. 116. PP. 151-165.
17. Carver, S., 1999, Developing Web-based GIS/MCE: Improving access to data and spatial decision support tools. Multicriteria decision-making and analysis: A geographic information sciences approach. Ashgate, New York, PP. 49-76.
18. Dave, S. M.; Joshi, G. J.; Ravinder, K. and Gore, N., 2019, Data monitoring for the assessment of on-street parking demand in CBD areas of developing countries. Transportation Research Part A: Policy and Practice, Vol. 126, PP. 152-171.
19. Grazioli, A.; Picone, M.; Zanichelli, F. and Amoretti, M., 2013, Collaborative mobile application and advanced services for smart parking. In 2013 IEEE 14th International Conference on Mobile Data Management, Vol. 2, PP. 39-44. IEEE.
20. Griggs, W.; Yu, J. Y.; Wirth, F.; Häusler, F. and Shorten, R., 2016, On the design of campus parking systems with QoS guarantees. IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 5. PP. 1428-1437.
21. Horng, G.-J., 2019, The cooperative on-street parking space searching mechanism in city environments. Computers & Electrical Engineering, Vol. 74, PP. 349-361.
22. Jelokhani-Niaraki, M. and Malczewski, J., 2015, A group multicriteria spatial decision support system for parking site selection problem: A case study. Land Use Policy, Vol. 42. PP. 492-508.
23. Jelokhani-Niaraki, M., 2013, Web 2.0-based collaborative multicriteria spatial decision support system: a case study of human-computer interaction patterns.
24. Jelokhani-Niaraki, M. and Malczewski, J., 2012, A user-centered multicriteria spatial decision analysis model for participatory decision making: An ontology-based approach. Proceedings of GSDI, 13.
25. Kinyanjui, K. E. and Kahonge, A. M., 2013, Mobile Phone–Based Parking System. International Journal of Information Technology, Control and Automation (IJITCA), Vol. 3. No. 1. PP. 23-37.
26. Liu, J.; Chen, R.; Chen, Y.; Pei, L. and Chen, L., 2012, iParking: An intelligent indoor location-based smartphone parking service. Sensors, Vol. 12. No. 11. PP. 14612-14629.
27. Mackowski, D.; Bai, Y. and Ouyang, Y., 2015, Parking space management via dynamic performance-based pricing. Transportation Research Part C: Emerging Technologies, Vol. 59. PP. 66-91.
28. Malczewski, J. and Rinner, C., 2015, Multi-criteria decision analysis in geographic information science: Springer.
29. Millard-Ball, A.; Weinberger, R. R. and Hampshire, R. C., 2014, Is the curb 80% full or 20% empty? Assessing the impacts of San Francisco’s parking pricing experiment. Transportation Research Part A: Policy and Practice, Vol. 63, PP. 76-92.
30. Rhodes, C.; Blewitt, W.; Sharp, C.; Ushaw, G. and Morgan, G., 2014, Smart routing: A novel application of collaborative path-finding to smart parking systems. Paper presented at the CBI.
31. Rodier, C. J. and Shaheen, S. A., 2010, Transit-based smart parking: An evaluation of the San Francisco Bay area field test. Transportation Research Part C: Emerging Technologies, Vol. 18, No. 6, PP. 225-233.
32. Shin, J. H.; Jun, H. B. and Kim, J. G., 2018, Dynamic control of intelligent parking guidance using neural network predictive control. Computers & Industrial Engineering, Vol. 120, PP. 15-30.
33. Shoup, D. C., 2006, Cruising for parking. Transport Policy, Vol. 13, No. 6, PP. 479-486.
34. Tasseron, G.; Martens, K. and van der Heijden, R., 2016, The potential impact of vehicle-to-vehicle communication on on-street parking under heterogeneous conditions. IEEE Intelligent Transportation Systems Magazine, Vol. 8, No. 2, PP. 33-42.
)