In this study, the relationship between space mean speed (SMS), flow rate and density of pedestrians was investigated in different pedestrian facilities, including 1 walkway, 2 sidewalks, 2 signalized crosswalks and 2 mid-block crosswalks. First, statistical analysis was performed to investigate the normality of data and correlation of variables. Regression analysis was then applied to determine the relationship between SMS, flow rate, and density of pedestrians. Finally, two prediction models of density were obtained using genetic programming (GP) and group method of data handling (GMDH) models, and k-fold and holdout cross-validation methods were used to evaluate the models. By the use of regression analysis, the mathematical relationships between variables in all facilities were calculated and plotted, and the best relationships were observed in flow rate-density diagrams. Results also indicated that GP had a higher R₂ than GMDH in the prediction of pedestrian density in terms of flow rate and SMS, suggesting that GP was better able to model SMS and pedestrian density. Moreover, the application of k-fold cross-validation method in the models led to better performances compared to the holdout cross-validation method, which shows that the prediction models using k-fold were more reliable. Finally, density relationships in all facilities were obtained in terms of SMS and flow rate.

在这项研究中，研究了不同步行设施的空间平均速度（SMS）、流量和行人密度之间的关系，包括1个人行道、2个人行道、2个信号灯人行横道和2个中间街区人行横道。首先，进行统计分析以研究数据的正态性和变量的相关性。然后应用回归分析来确定SMS之间的关系，流速和行人密度。最后，利用遗传规划（GP）和数据处理组方法（GMDH）模型获得了两个密度预测模型，并使用k-fold和holdout交叉验证方法对模型进行了评估。通过回归分析，计算并绘制了所有设施中变​​量之间的数学关系，并在流量-密度图中观察到最佳关系。结果还表明，在预测行人密度方面，GP在流速和SMS方面的R ₂高于GMDH ，表明GP能够更好地对SMS进行建模。和行人密度。此外，与holdout交叉验证方法相比，在模型中应用k-fold交叉验证方法可以获得更好的性能，这表明使用k-fold的预测模型更可靠。最后，根据SMS和流量获得所有设施中的密度关系。