# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Coordination of Transportation Fleets of Autonomous Drivers  
A similar problem to the emergency medical service coordination consists of coordinating fleets for transportation in an urban area (e.g., messaging services or taxi fleets). However, in contrast to medical emergency services, here the objective is not only focused on response time, but also on cost  
Figure 13: Average patient delay time of the proposed EMS coordination approach vs. the first-come first-served (FCFS) strategy (%) for the frequency of appearance of 1 patient each 10 time periods. efficiency. Furthermore, a primary characteristic of such systems, at least with the boom of collaborative economy, is that such types of fleets may be open [58] in the sense that private persons may participate in the fleet as autonomous workers, with their own vehicle and at different time intervals. With regard to the coordination of such fleets, the autonomy of the drivers is a crucial characteristic. It implies that the drivers get their income on a per-service basis instead of a monthly salary. This means that, besides accepting a set of basic rules, drivers are more concerned with the actual service they have been assigned from the system and may have more freedom to accept or decline assignment decisions. [PERSON] et al. [59] presented a real-time dispatching strategy based on solving the optimal taxi assignment among idle taxis and pending requests at certain intervals or whenever new events (new customer/available taxi) take place. [PERSON] and [PERSON] [60] analysed how suboptimal individual decisions lead to global inefficiencies. While most existing approaches try to minimise the average waiting time of customers, other works have a different focus. BAMOTR [61] provided a mechanism for fair assignment of drivers, that is, to minimise the differences in income among the taxi drivers. For that, they minimised a combination of taxi income and extra waiting time. [PERSON] et al. [62] proposed an optimal multi-taxi dispatching method with a utility function that combines the total net profits of taxis and waiting time of passengers. [PERSON], and [PERSON] [63] proposed a hybrid path search for fast, efficient, and reliable assignment to minimise the total travel cost with a limited knowledge of the network. In contrast to the previous works, the main characteristic of our approach is the possibility of modifying the assignment when a taxi has been dispatched but has not yet picked up a customer. In this sense, we followed a similar approach to the emergency medical service coordination presented in Section 3.3. One of the few other works in this line is [64], which presented an adaptive scheduling, where reassignment is possible during a time interval until a pick-up order is sent to the taxi and customer. In our case, we do not restrict reassignment to a specific interval. Furthermore, we propose a method that economically compensates the negatively affected taxis in the new schedule such that they do not have a loss in their income. We considered a system that uses some mediator in charge of matching the transportation requests with vehicles of the fleet. On one hand, customers contact the mediator via some telematic means, requesting a transportation service. On the other hand, drivers subscribe to the system, offering their services during specific time intervals where they are available. We assumed a payment structure for transportation services where clients pay a fixed price plus a _fare_ per distance, as described in the following:  
* From the client side, the price of a transportation service \(s\) is determined by the distance of the requested service plus a fixed cost: \[Price(s)=\textit{fcost}+\textit{fare}\cdot d(s),\] where \(d(s)\) denotes the distance from the service origin to its destination, _fcost_ is a fixed cost, _fare_ is a rate a client has to pay per distance unit.