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.

Parking is a key element of a sustainable urban mobility policy. It plays a fundamental role in travel planning and transport management, as the foremost vector of modal choice, but also as a potential means of freeing up public spaces. In this article we define the smart parking concept, as an application of smart mobility, present a historical analysis of the evolution of smart parking framework and show a statistical analysis of the published patent applications in this field around the world using the ORBIT database. Then, we propose a new smart parking architecture based on multi-agent features. Finally, we introduce the e-Parking system, platform to improve the driver experience of crowded cities. It provides real-time parking prices and offers a reservation and guidance services. In addition, the system assigns an optimal parking for a driver based on the user's requirements that combine proximity to destination, parking cost and dwell time, while ensuring a fair sharing of public space among users and improves traffic conditions. Our approach is based on dynamic pricing policy. Our scheme is suitable for mixed-usage areas, as it considers the presence of reserved and not reserved driver in the same parking area.

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