# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Emergency Medical Service Coordination
#### 3.3.3 Experimental Results  
We tested the effectiveness of the dynamic reassignment and redeployment approaches in different experiments simulating the operation of SUMMA112, the EMA service provider organisation in the Autonomous Region of Madrid in Spain. We used a simulation tool that allows for a semi-realistic simulation of intervals of times of the normal operation of an EMA service. The tool reproduces the whole process of attending emergency patients, beginning with their appearance and communication with the emergency centre, the schedule of an ambulance, the \"in situ\" attendance, and, finally, the transfer of the patients to hospitals. The simulator operates in a synchronised manner based on a stepwise execution, with a step frequency of 5 s. That is, every 5 s, the activities of all agents are reproduced, leading to a new global state of the system. In the simulations, we are mainly interested in analysing the movements of ambulances and the subsequent arrival times to the patients. The movements are simulated using an external route service to reproduce semi-realistic movements on the actual road network with a velocity adapted to the type of road. External factors, like traffic conditions or others, are ignored. The duration of the phone call between a patient and the emergency centre and the attendance time \"in situ\" is set to 2 and 15 min, respectively. As the area of consideration, we used a rectangle of 125 \(\times\) 133 km that covers the whole region of Madrid. For calculating the probability distribution of upcoming patients, we divided the region into cells of size 1300 \(\times\) 1300 m. A different probability distribution is estimated for each day of the week and each hour from statistical data (patient data from the whole year of 2009). We used 29 hospitals (all located at their real positions) and 29 ambulances with advanced life support (as it was used by SUMMA112 in 2009) and we simulated the operation of the service for 10 different days (24 h periods) with patient data from 2009 (1609 patients in total). The days were chosen to have a representation of high, medium, and low workloads. We only accounted for so-called level 0 patients, (e.g., patients with a live threatening situation). We compared two approaches:  
* SUMMA112: The classical approach (used by SUMMA112). Patients are assigned to the closest ambulances using a fixed FCFS strategy. Furthermore, ambulances are positioned on fixed stations (at the hospitals), waiting for missions. After finishing any mission, the ambulances return to their station. * DRARD: In this case, the dynamic reassignment and redeployment methods are employed. With regard to dynamic re-employment, idle ambulances only move to a new recommended position if it is further away than 500 m. This is to avoid short, continuous movements. Table 1 presents the average arrival times (in minutes) obtained with the two models in simulations for the 10 selected days. As the results show, the use of the DRARD approach provides a considerable improvement (between around 10% and 20%). If we look at all 1609 patients, the average times are 11:45 and 9:54 min, respectively, which implies an improvement of 15.8%. In Figure 9 we present the results of the distribution of arrival times for the different approaches for all 1609 patients of the 10 selected days. The patients in each curve are ordered by increasing arrival time. A clear difference can be observed between the DRARD method, with respect to the current operation model of SUMMA112. The results are clearly better for almost all arrival time ranges. Furthermore, the most important improvements can be observed in the range of higher times. This is a very positive effect because it assures that more patients can be attended to within given response time objectives. For example, out of the 1609 patients, 1163 (72.3%) are reached within 14 min with the SUMMA model, whereas this number increases to 1356 patients (84.3%) with DRARD.