## 1 Introduction
Table 1 below shows the KPIs identified in the literature for evaluating processes in LUZs, divided into four categories, according to the authors' criteria:  
The authors highlight that the KPIs most frequently found in the literature are those related to the delivery process, average parking time, average delivery time, the number of deliveries made, occupancy rate of available spaces, the type and capacity of vehicles, and the number of vehicles entering the area. Those related to LUZs include the number of delivery bays required, the size of the loading bays, and the location and availability ofLUZs. In any case, no indicator highlights the numerical quantification of illegal parking in a LUZ, either by private vehicles or commercial vehicles that exceed the time allowed. These KPIs will be the components of the OEE calculation formula. After analysing all the KPIs in the table, none were found that allow the efficiency of a LUZ to be measured from a city governance perspective. Therefore, the objective of this study is to define a new indicator based on the OEE model that enables the evaluation of the use of a LUZ or a set of LUZs over a specific time horizon--hourly and daily in this study. Its implementation will provide a methodology to support decision-making regarding whether the location, size, and reservation hours for loading and unloading operations are appropriate or not. [PERSON] et al. [20] assert that the same space will be less valuable during periods of lower activity. With the application of the proposed model, these periods can be identified. The analysis can also lead to actions that improve the use of the area, which will help city governance make urban coexistence among various users more compatible. [PERSON] et al. [4] discuss the variable size of LUZs but highlight the difficulty of assessing whether their dimensions are appropriate. As demonstrated in the conclusion of this work, illegal occupation by unauthorised vehicles and the excessive time transporters spend unloading are the two factors that most hinder LUZ efficiency. The contribution of this study is to quantify the impact of these problematic practices. \begin{table}
\begin{tabular}{l l} \hline
**KPIs Related to the Delivery Process** & **Source** \\ \hline Average parking duration & [1,2,10,11,15,18,19] \\ Vehicle arrival rate & [5,14,16,18] \\ Average delivery time & [1,4,12–14,20,21] \\ Average waiting time (cruising) & [1,3,5,10,16,17,19] \\ Number of deliveries made & [3,11,12,14,15,20,22,23] \\ \% of deliveries during peak vs. off-peak hours & [12,20] \\ Deliveries not made on time & [24,25] \\ Occupancy rate of places s/total available & [2,5,11,15–21,24,26] \\ Type and capacity of vehicles & [1–3,10,12,13,17,18,26,27] \\ Number of vehicles entering the area & [1–3,10,12,13,17,18,26,27] \\ Day of the week & [18] \\ Time of arrival/departure & [1,16,18,25,26,28] \\ Professional activity (sector) & [1,4,17,25] \\ Type of cargo, weight, and volume & [5,18,25] \\ \hline KPIs related to LUZ & Source \\ \hline Number of delivery bays required & [2,4,5,14–16,20,27,29] \\ Size of loading bays & [2,4,11,14–17,20,27,28] \\ Location and availability of LUZ & [2,4,10–16,24–26,30] \\ Walking distance & [3,10–12,16,20,29,31] \\ Frequency of use of loading areas (rotation) & [5,18,20,26] \\ \hline KPI auxiliaries & Source \\ \hline kg per person consumed in an area & [32] \\ Average emissions per vehicle & [4,11,18] \\ Impact of external events & [24] \\ Parking costs & [3,4,17,24] \\ LUZ demand & [11,13,16,19,22] \\ LUZ parking space reservation & [13,16,17] \\ \hline KPIs related to externalities & Source \\ \hline Number of double-parked vehicles & [2,3,13,15,20,21,24] \\ Location of double-parked vehicles & [2,10,12,16,18,27,33] \\ Illegality rate by vehicle type and duration & [2,4,17,27] \\ Percentage of unauthorised vehicles in LUZ & [27] \\ \hline \end{tabular}
\end{table} 
Table 1: KPIs found in the literature. The article is organised as follows: Section 2 describes the OEE model and its application to the DUM.