# Reinventing Mobility Paradigms: Flying Car Scenarios and Challenges for Urban Mobility
## 4 Experiments
### Simulation Characteristics  
Three test networks having different sizes in terms of baseline modules, i.e. \(3\times 3\), \(6\times 6\) and \(9\times 9\) modules with \(L=1000\) m, have been considered for the experiments. The origin/destination trip demand (i.e., the set of agents moving between \(O\)/\(D\) pairs) has been generated starting from an average value of 300 vehic/h and variation coefficient \(c_{v}=0.4\). The \(O\)/\(D\) values have also been split for several time slices--each one of 5 min for 1 h of simulation--in a random way for each simulation. In more detail, each \(O\)/\(D\) value has been split in 12 sub-values such that their sum is equal to the generated \(O\)/\(D\) value. As described in Section 3, in the UAM scenario, an agent is associated with each flying car. All the agents are assumed to have the same characteristics, which is not unrealistic because flying vehicles have been considered standardized and automated and to avoid collisions, agents, also assisted by the Agency (see Section 3, point d), are compelled to respect some constraints. To perform the experiment, the minimum flight height has been set equal to 40 m, which comes from the hypothesis that the highest building in the given urban context is no more than 25 m and there  
Figure 5: Baseline transportation network module. is a suitable distance of 15 m between the building roof and the lowest agent's height position, which has been considered proper to meet the minimum safety and security issues. Note that, setting different minimum height for skyscraper urban contexts could require some additional conditions on the initial hypothesis here made about uncongested aerial links, i.e. depending on the maximum allowed flight height. However, it is not in the aim of this study to explore these additional conditions. The ground speed follows the empirical relationship \(v=a-b\cdot f\) (see also note at point 3.c in Section 3). As the number of agents on a given link increases, such speed is modified accordingly, and the travel time is computed. For aerial links, the cruise speed has been set in the range [80, 120] Km/h according to the aerial link length and height. As for potential waiting times to access the aerial links, the departing time from the transition node depends on the expected ground travel time to reach such node from the origin and the expected flying time to reach the destination transition node. In particular, the Agency will provide information about the current state of the transportation network to each \(A_{i}\) and, therefore, for each \(A_{i}\):  
1. The expected travel time to go from the origin to the destination node is computed, according to the real number of agents already using the transport links;
2. If there are no other agents at the expected arrival travel time at both the departing and arrival transition node, then \(A_{i}\) moves on the network along the computed path to reach its destination;
3. Otherwise, \(A_{i}\) will wait at the trip origin until a suitable free slot is ready. This condition allows for avoiding:  
1. Waiting times at the departing transition node, which could generate undesirable queues and ground traffic jams near the transition nodes;
2. Unsuitable flying queues at the arrival transition node. The possible waiting time contributes to the total path travel time so that in some traffic conditions only-ground paths might be comparable to, or even better than, \"ground + flying\" paths, particularly for short distances. Finally, when reaching the transition node for accessing the aerial link, for each \(A_{i}\):  
1. The travel time to reach the assigned height depends on the maximum acceleration (\(a_{i,g}=2.5\) m/sec\({}^{2}\)[53]) and the assigned speed (from 80 to 120 km/h according to the distance between transition nodes and the assigned height);
2. The travel time to move along the aerial link depends on the assigned cruise speed;
3.