Prototype development and test of a server-independent smart exit sign system: An algorithm, a hardware configuration, and its communication reliability

Dynamic Sign Guidance Optimization for Crowd Evacuation considering Flow Equilibrium

“…e available studies on the optimization of exit sign direction are mainly classified into the following three methods. e first method is based on static network shortest route optimization [6,7,10,11], where researchers proposed several exit sign direction setting approaches with shortest route algorithms based on indoor navigation graph networks. e second method is based on dynamic network flow optimization [29][30][31].…”

“…As mentioned above, the route travel cost is the basis of the DPA model, which ensures a well-balanced distribution of pedestrian flow on each route. Existing studies on DSG have proposed various indicators of route travel costs, including distance [1,7], travel time [2,3,6,10], level of service [2,11], hazard level [7], and individual congestion-awareness [39]. Among them, level of service, hazard level, and individual congestion-awareness rely on personal subjective judgments.…”

“…(5) while g < G, do (6) CROSSOVER(P, P c , Y(g), card(R)) ( 7) MUTATION(P, P m , Y(g), card(R)) ( 8) REPRODUCTION(P, Y(g), Y′(g), n ij (k), t ij (k)) (9) g � g + 1 (10) End while (11) For each chromosome in Y(g), do (12) Calculate the fitness value F(X) of the chromosome X (13) obtain the chromosome X with the maximum F(X) from Y(g) (14) End for (15) Return X (16) End procedure ALGORITHM 1: Genetic algorithm for upper-level model.…”

“…(1) Input: P, P c , Y(g), the cardinality of the set R card(R) (2) Output: the offspring population Y′(g) (3) Procedure CROSSOVER(P, P c , Y(g), card(R)) (4) Set temporary population chromosome set T � ∅ (5) For i � 1 to P, do (6) generate random number r ∈ (0, 1) (7) If r < P c (8) en add the i th chromosome in Y(g) to T (9) End if (10) End for (11) For the m th chromosome X m in T (m � 1 to card(T)), do (12) For the n th chromosome X n in T (n � m + 1 to card(T)), do (13)…”

“…However, not much research has been done on the optimization of exit sign directions considering flow equilibrium. Some of the existing studies related to exit sign direction use only path distance as route metric for direction optimization and ignore the crowd distribution, much less the flow equilibrium state [4,[6][7][8][9][10][11]. Another part of the research considered a balanced assignment of evacuation flow using metric that reflects the travel cost of the route, including route flow [12,13], density [2], flow to capacity ration of route [4], and travel time [14].…”