# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Coordination of Transportation Fleets of Autonomous Drivers
#### 3.5.2 Evaluation  
We tested the proposed approach in different experiments simulating the operation of a taxi fleet which basically has the characteristics of the type of fleets we want to address here. We used an operation area of about 9 \(\times\) 9 km, an area that roughly corresponds to the city centre of Madrid, Spain. In the simulations, we randomly generated service requests (customers) who are assigned to available taxis, and we simulated the movement of taxis to pick up a customer, to drive him/her to his/her destination, and then waits for the assignment of a new customer. The simulations are not aimed at reproducing all relevant aspects of the real-world operation of a taxi fleet, but to analyse and compare the proposed coordination strategy (here called DYNRA) to the standard strategies FCFS and NVNR. Thus, we simplified the movements of taxis to straight-line movements with a constant velocity of 17 km/h. This velocity is within the range of the average velocity in the city centre of Madrid. Hence, we do not take into account either the real road network, nor the possibility of different traffic conditions. The general parameters used in the simulation are as follows. We use 1000 taxis (initially distributed randomly in the area with a uniform distribution) and a simulation interval of 5 h. The taxis do not cruise, that is, they only move if they are assigned to a customer. We accomplish different simulation runs with different numbers of customers in order to represent different supply/demand ratios. We generate a fixed number of customers every 15 min (ranging from 250 to 1000 in steps of 125). For each customer, his/her origin (point of appearance) and destination location are randomly chosen such that each trip goes either from the outside of the area to the centre, or vice versa. The origin and destination points are generated using a normal distribution (for centre and outside points). When a taxi arrives at a customer's location, a pick-up time of 30 s is used where the taxi does not move. In the same sense, the simulated drop-off time is 90 s. The system assignment process is accomplished every 5 s, and only if a new client appears or a taxi becomes available again after a previous trip. The payment scheme we used in the experiments is the one that has been used in the city of Madrid in the last years. A taxi trip has a fixed cost _fcost_ = 2.4 euros and _fare_ = 1.05 euros/km. Furthermore, the cost factor is _vocost_ = 0.2 euros/km. This factor roughly corresponds to the actual cost of a vehicle, including petrol, maintenance, as well as other fixed costs. Finally, we applied a factor of \(\gamma=1/0.00085\), which corresponds to the net benefit a taxi receives per metre when transporting a client in the used payment scheme. Each experiment was repeated 10 times with a different random seed, in order to avoid biased results due to a particular distribution of clients. The presented results are averages over those 10 runs. Table 2 presents the average waiting times of the customers for the three methods and the different numbers of generated customers per hour. As it can be observed, for between 2000 and 2500 customers per hour, the FCFS approach starts to be perform really badly. Basically, the system becomes saturated and the rate of serving customers is lower than the rate of appearance of new customers. The other two methods, NVNR and DYNAR, can deal much better with this situation and their saturation point is higher (between 2500 and 3000 customers per hour). There is a clear improvement in the waiting times of these two methods, with respect to FCFS, if there are more than 2000 customers per hour. The dynamic reassignment approach with compensations performs better than the other two methods in all cases. The improvement is rather low if there are fewer customers, but increases with the number of customers up to 101.4 and 2.4 min with respect to FCFS and NVNR, respectively, for 4000 customers a hour.