## 1 Introduction  
The vast majority of cities were not designed to accommodate the distribution of goods by trucks or delivery vans. Managing vehicle mobility on urban streets, while balancing the needs of pedestrians, personal mobility vehicles (PMVs), buses, private cars, and delivery transporters, is a critical factor that municipal authorities must manage. Parking in dense urban areas is a major challenge for last-mile logistics. The scarcity of parking spaces and policies that do not address the needs of commercial vehicles often lead vehicles to park illegally, according to [PERSON] et al. [1] The solution currently in place in cities for loading and unloading of goods to retail stores is to reserve LUZs on the streets, but in most cases, these LUZs are neither located in the right place nor are they of adequate size for the use required by transporters, nor do they have adequate reservation times. [PERSON] et al. [2] argue that optimising the number, location, and use of LUZs can lead to substantial improvements in mobility indicators, particularly in reducing double parking, an externality that disproportionately affects traffic and the efficiency of the urban transport system. Incorrect location, incorrect sizing, and poor choice of reserved times, as well as the time allowed for each unloading, standardised for all areas, create very harmful externalities for urban mobility, such as double parking and illegal occupation of sidewalks, garage doors, crosswalks, or street intersection islands. As suggested by [PERSON] et al. [3], when commercial vehicle drivers cannot find legal parking nearby, they resort to double parking or park in illegal spaces to ensure on-time delivery. [PERSON] et al. [4] indicate that there are no clear criteria for choosing the location, size, and timing of LUZ reservations. This study proposes the application of the OEE model, typically used in industrial and production environments, as a KPI, considering a LUZ as if it were a production element in order to evaluate operational efficiency and identify the factors that reduce productivity in that area. This information can be very useful for city governance, which can draw valuable conclusions about the performance of established areas. [PERSON] et al. [5] propose a methodology for verifying and designing the number of delivery bays needed based on demand but do not indicate how to measure their efficiency. Although [PERSON]\(\ddot{\text{u}}\)[PERSON] et al. [6] assert that, in the context of their research, they believe that decisions about the number, size, and location of loading zones should be made for individual streets rather than for sectors or areas, the application of the OEE model proposed in this article is very versatile and flexible, as it can be applied to a zone or a set of zones and over any time period that can be defined, whether in hours, days, weeks, or months. There is an abundant body of literature on urban distribution of goods (UDG); however, studies focusing specifically on LUZs are scarce. The existing references tend to focus research on access routes to these zones [7; 8; 9] and the walking distance from the LUZ to the supply point [10; 11]. [PERSON] et al. [11] suggest that a maximum distance of 75 m provides a good level of accessibility service for cargo. Other authors, such as [PERSON] et al. [12] and [PERSON]\(\ddot{\text{u}}\)[PERSON] et al. [6], suggest a distance of 50 m, while [PERSON] et al. [13] and [PERSON] [14] suggest up to 100 m. Other studies focus on booking spaces in LUZs [15; 16; 17; 18] to avoid externalities and save on energy consumption when searching for parking. Several articles have linked the OEE model to UDG, providing the foundation for the present article.