New Parking Lot Selection Approach Based on the Multi-Criteria Decision Making (MCDM) Methods: Health Criteria

Abstract

To find a parking space, valet parking drivers have to travel a lot, which leads to carbon dioxide (CO2) emissions. In order to reduce these emissions, it is essential to understand a user’s needs and criteria when searching for a parking space. Several selection criteria are considered when allocating a parking space. Recent research on parking space management mentions several parameters that have an impact on the choice of a parking space: namely, the traffic situation, the availability of each parking lot in question, and the cost of parking, etc. In this article, we discuss a new criterion: the physical condition of the driver in the management of parking spaces; the identification of the driver’s bodily fragility. We also propose MCDM as a parking space allocation model that best meets the cost–benefit convention. This reflection leads us to evaluate MCDM methods in the field of intelligent parking management. Therefore, we conducted a comparison between the most recent multi-criteria decision making methods used by researchers, namely, CODA, EDAS, TOPSIS, and WASPAS. The CRITIC method was used in this paper to objectively determine the weight of each criterion. A new approach is proposed to evaluate and select the best MCDM method. Indeed, we propose a method that computes the “average inter-item correlation SW”, a combination of the “average inter-item correlation” and the SW coefficient. This approach allows us to efficiently compute the correlation between a method and the set of methods while favoring the cells with the best ranking. A case study is presented to illustrate the MCDM approach to parking space allocation and evaluation. The proposed system provides drivers with services such as intelligent parking decisions, taking into account the human aspect while reducing energy consumption, driving time, and traffic congestion caused by searching for available parking spaces.

How Does the Built Environment Affect Mechanical Parking Space Planning: A Case Study in Xi’an City

“…Parking cost is the most important variable influencing parking demand, followed by location and road density [1]. There is a non-linear influence of the built environment on the demand and shows an obvious threshold effect [12]. Chen has proposed that the construction index (18.92%) and location (15.23%) are the most important built environment factors influencing parking demand [29].…”

“…The main contributions of this study are as follows: on the one hand, there are few studies on mechanical parking planning. Some studies have analyzed the internal organization, control strategy, site selection and cost, mechanical structure [7][8][9][10][11][12], and the design of the device and intelligent system optimization of the single facilities, while there is a lack of regional planning studies that provide theoretical and practical support for the spatial layout planning of mechanical parking. On the other hand, very few studies have addressed the relationship between car parks and the built environment, especially mechanical parking lots, and there is a lack of analysis of the impact of factors.…”

Implementasi Kebijakan Parkir di Kawasan Central Business District Kota Pekalongan

“…Hal ini harus diimbangi dengan harga progresif rataan parkir yang lebih rendah pada kluster permintaan parkir tinggi antara 5,6%-12,5% (Ornelas et al, 2023). Sistem parkir intelejen dengan aplikasi mobile mampu mengatasi masalah pada transformasi eksistensi infrastruktur pada smart cities (Aditya et al, 2023;Amari et al, 2023;Gunarto et al, 2023;Kujawski & Nürnberg, 2023;Morais, 2023;Poh et al, 2023;Rafique et al, 2023;Zong et al, 2023).…”

Assessing Supply Chain Innovations for Building Resilient Food Supply Chains: An Emerging Economy Perspective

“…The global weights are evaluated by multiplying (Wj) with sub-criteria weights. Decision makers deployed mathematical modeling and sophisticated statistical analysis to enhance the results' accuracy and solve multiple managerial issues [73][74][75][76][77]. Business and management [78][79][80], banking and insurance [81][82][83], computer science [84][85][86], health and medical [81][82][83], engineering [84], energy [85,86] and oil and gas [87][88][89][90], are among the scientific sectors that have employed it.…”