Ibution function of your coefficients of influencing variables of 15 was 9 described to clarify the heterogeneity with the 3 components on passenger decision-making utility, as shown in Figure 3a .(a)(b)(c) Figure 3. The marginal probability of estimated coefficients. (a) The marginal probability of estimated coefficient “Dist”; Figure 3. The marginal probability of estimated coefficients. (a) The marginal probability of estimated coefficient “Dist”; (b) The marginal probability of estimated coefficient “Pedestrian (c) The marginal probability of estimated estimated (b) The marginal probability of estimated coefficient “Pedestrian flow”; flow”; (c) The marginal probability of coefficient coefficient “Crowd density”. “Crowd density”.4.3. The Verification of Preferencemarginal probability distribution from the “Dist” coefficient Figure 3a shows that the Heterogeneitywas the this section, we useindicating that theSafranin Protocol skewness coefficient and kernel density In most concentrated, the techniques of estimated coefficient on the “Dist” element showed the lowest the passengers’ preference most of the people estimation to confirm heterogeneity level; that is,heterogeneity.will pick the nearest exit The skewness coefficient [38] will be the characteristic worth that represents the asymmetry degree on the probability distribution density curve relative towards the typical worth. The calculation formula of skewness is as follows: =-(7)Sustainability 2021, 13,9 offor evacuation. Figure 3b,c showed that the marginal probability distribution on the coefficients of “Pedestrian flow” and “Crowd density” have been somewhat dispersed, and their heterogeneity levels had been larger than that of “Dist”, indicating that passengers’ perception of these two influencing elements is reasonably dispersed. four.3. The Verification of Preference Heterogeneity Within this section, we use the procedures of skewness coefficient and kernel density estimation to verify the passengers’ preference heterogeneity. The skewness coefficient [38] would be the characteristic value that represents the asymmetry degree on the probability distribution density curve relative for the typical worth. The calculation formula of skewness is as follows: SK ( X ) = u – M0 (7) (eight) (9)= EX 2 = EX two – exactly where Skew( X ) is the skewness coefficient of influencing factors, X will be the worth of influencing things, is definitely the mean value of influencing variables and two is the variance of influencing factors. When Skew( X ) 0, it signifies that the worth of influencing elements is concentrated inside a little variety, and when Skew( X ) 0, it signifies that the worth of influencing components is concentrated within a massive variety. The higher the absolute value of skewness, the higher the skewness of its information distribution. In Table five, we calculate not merely the imply along with the median, but also the skewness coefficient.Table five. The coefficient of skewness of influence elements. Independent Variable Dist Pedestrian flow Crowd density Skewness Coefficient 0.93 -0.01 0.19 Mean Worth 27.00 three.69 4.01 Median 20.89 three.70 three.The kernel density estimation [39] is usually a method applied to study the SB 271046 Epigenetics qualities of information distribution in the data sample itself, which is a nonparametric approach for estimating the probability density function. Therefore, it has been extremely valued in the field of statistical theory and application. The calculation formula in the kernel density is as follows: f h (x) = 1 n x – xi ( K nh i h =1 (ten)where K (.) could be the kernel function, h is often a smoothing parameter, and h 0, and n is.

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