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Iraj BARGEGOL Seyed Mohsen HOSSEINIAN Vahid NAJAFI MOGHADDAM GILANI Mohammad NIKOOKAR Alireza OROUEI 《Frontiers of Structural and Civil Engineering》2022,16(2):250-265
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. 相似文献
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Hamid MIRZAHOSSEIN Milad SASHURPOUR Seyed Mohsen HOSSEINIAN Vahid Najafi Moghaddam GILANI 《Frontiers of Structural and Civil Engineering》2022,16(5):657
The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R2 value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety. 相似文献
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Jorge Ignacio CISNEROS-SALDANA Seyedmohammadhossein HOSSEINIAN Sergiy BUTENKO 《工程管理前沿(英文版)》2018,5(4):533
This study aims to find appropriate locations for wind farms that can maximize the overall energy output while controlling the effects of wind speed variability. High wind speeds are required to obtain the maximum possible power output of a wind farm. However, balancing the wind energy supplies over time by selecting diverse locations is necessary. These issues are addressed using network-based models. Hence, actual wind speed data are utilized to demonstrate the advantages of the proposed approach. 相似文献
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