## 4 Discussion and Conclusions  
Crowds in public places are diverse in terms of demographics, such as gender, age, culture, and socioeconomic characteristics, such as income level, occupation, travel purpose, and so on. The interactions between individuals in a crowd determine the global movement patterns of the crowd motion. Characteristics of the individual pedestrians could largely influence the microscopic interactions between individuals, and as a result, that will affect the macroscopic and global patterns of motion. The purpose of this study was to determine whether pedestrians' gender influences the microscopic walking dynamics when they walk in a mixed-gender crowd. The trajectory data was obtained from an experiment on single-file movement that was conducted under four different global density levels. Key microscopic variables of walking dynamics, i.e., spacing, speed, and acceleration, which are widely used in microscopic pedestrian behavior models, were derived from these trajectory data. Multiple linear regression was used to model individuals' acceleration and deceleration behaviors as functions of relative speed, spacing, and gender. Eight regression models were obtained for acceleration and deceleration behaviors and for the four considered density levels separately. The gender and the relative speed were significant in all models, which indicated that these two \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Coefficients**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**Sig.**} \\ \cline{3-5}  & & **B** & & **Std. Error** & \\ \hline \multirow{4}{*}{N = 14} & (Constant) & \(0.135\) & \(0.012\) & \(10.910\) & \(0.000\) \\  & DS & \(0.003\) & \(0.010\) & \(0.260\) & \(0.795\) \\  & DV & \(-0.585\) & \(0.032\) & \(-18.269\) & \(0.000\) \\  & gender & \(-0.027\) & \(0.006\) & \(-4.967\) & \(0.000\) \\ \hline \multirow{4}{*}{N = 20} & (Constant) & \(0.182\) & \(0.009\) & \(19.570\) & \(0.000\) \\  & DS & \(0.003\) & \(0.011\) & \(0.270\) & \(0.787\) \\  & DV & \(-0.852\) & \(0.017\) & \(-49.695\) & \(0.000\) \\  & gender & \(-0.064\) & \(0.004\) & \(-17.193\) & \(0.000\) \\ \hline \multirow{4}{*}{N = 24} & (Constant) & \(0.163\) & \(0.005\) & \(32.863\) & \(0.000\) \\  & DS & \(-0.013\) & \(0.006\) & \(-2.025\) & \(0.043\) \\ \cline{1-1}  & DV & \(-0.677\) & \(0.011\) & \(-64.249\) & \(0.000\) \\ \cline{1-1}  & gender & \(-0.020\) & \(0.002\) & \(-10.133\) & \(0.000\) \\ \hline \multirow{4}{*}{N = 30} & (Constant) & \(0.129\) & \(0.002\) & \(57.295\) & \(0.000\) \\ \cline{1-1}  & DS & \(0.000\) & \(0.003\) & \(-0.072\) & \(0.942\) \\ \cline{1-1}  & DV & \(-0.554\) & \(0.007\) & \(-77.549\) & \(0.000\) \\ \cline{1-1}  & gender & \(-0.011\) & \(0.001\) & \(-7.694\) & \(0.000\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Coefficients in the multiple regression models for deceleration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Sum of**} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**Sig.**} \\  & & **Squares** & & & & \\ \hline \multirow{3}{*}{N = 14} & Regression & \(3.099\) & \(3\) & \(1.033\) & \(124.576\) & \(0.000\) \\  & Residual & \(9.584\) & \(1156\) & \(0.008\) & & \\  & Total & \(12.683\) & \(1159\) & & & \\ \hline \multirow{3}{*}{N = 20} & Regression & \(46.374\) & \(3\) & \(15.458\) & \(893.690\) & \(0.000\) \\  & Residual & \(88.974\) & \(5144\) & \(0.017\) & & \\  & Total & \(135.348\) & \(5147\) & & & \\ \hline \multirow{3}{*}{N = 24} & Regression & \(52.049\) & \(3\) & \(17.350\) & \(1394.864\) & \(0.000\) \\  & Residual & \(153.253\) & \(12,321\) & \(0.012\) & & \\ \cline{1-1}  & Total & \(205.302\) & \(12,324\) & & & \\ \hline \multirow{3}{*}{N = 30} & Regression & \(61.715\) & \(3\) & \(20.572\) & \(2027.086\) & \(0.000\) \\ \cline{1-1}  & Residual & \(212.323\) & \(20,922\) & \(0.010\) & & \\ \cline{1-1}  & Total & \(274.038\) & \(20,925\) & & & \\ \hline \hline \end{tabular}
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