## 7 Results and Discussion  
The experiments are conducted on scenarios with 30, 60, 90 and 120 evacuees, respectively. [PERSON]'s shortest path algorithm and original distance-oriented goal function-based algorithm are performed for comparison purposes. In the first experiment, we evaluate the performance of three novel metrics, while in the second experiment, we route diverse evacuees with the relevant metric. To exclude the interferences of other factors, the evacuees in the first experiment are homogeneous with an identical velocity and initial health value. In the second experiment, we employ four types of simulated agents with different speeds, initial health and resistance to hazards. The results of ten randomised experiments are presented in Figure 4. The error bars show the highest and lowest percentage of survivors in the ten simulations. In low occupancy rate (30 evacuees), all the CPN-based algorithms reach the performance of [PERSON]'s algorithm, which can be considered as an optimal algorithm, because it has full knowledge of the environment and the level of congestion is low. As the population density increases, congestion occurs more and more frequently and gradually becomes a predominant success factor for evacuation. In this case, CPN-based algorithms outperform [PERSON]'s algorithm for different reasons: CPN with the distance metric overcomes [PERSON]'s algorithm, due to the nature of a decentralized system in which each decision node acts as a sub-system and updates its path recommendation at a different time. This tends to distribute evacuees to different routes and slightly ease congestion. CPN with the time metric and the energy metric embed the congestion ease algorithm, because congestion can induce a time delay and extra energy consumption (braking). CPN with the safety metric outperforms [PERSON]'s algorithm mainly because it directs evacuees to the future safest path and reduces the probability of re-routing. Although CPN with the safety metric does not seek to ease congestion, it predicts the time to reach a certain location regarding the potential congestion that will be encountered. Figure 4 also indicates that CPN with the safety metric performs best in relatively low population densities (30 and 60 evacuees), while the CPN with the time metric has the largest mean percentage of survivors in high occupancy rates (90 and 120 evacuees). Figure 3: The graph-based layout of the building. The black stars represent the exits on the ground floor. Figure 5 shows the average number of congestion situations encountered by civilians in the above evacuation processes. The results imply that CPN with the time metric can effectively alleviate congestion in comparison with other algorithms. This is because CPN with the time metric can disperse evacuees to several channels, while other algorithms tend to insist on one main channel to the exits. Although CPN with the energy metric also adopts the same congestion aware algorithm as CPN with the time metric, it produces more congestion, owning to the energy utilisation on a path largely depending on the length and sinuosity of the path. Hence, electric powered wheelchairs tends to stick to an energy efficiency route, unless a large amount of jamming occurs on the path or the hazard reaches them. CPN with the safety metric causes comparable congestion as the energy metric. This is because this algorithm not only considers a node's current fire intensity, but the situation when it reaches that node. Actually, the safety-oriented goal function is a tradeoff between the number of congestion situations encountered and the probability of being blocked by the hazard and switching to a detour path. Figure 4: The percentage of survivors for diverse path-finding algorithms in scenarios with homogeneous evacuees. The results are the average of 10 randomized simulation runs, and the error bars show the min/max result in any of the 10 simulations. Figure 5: The average number of congestion situations appears in each scenario for different path finding algorithms.