scholarly journals Expression and Validation of Online Bus Headways considering Passenger Crowding

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shengyu Yan ◽  
Jibiao Zhou ◽  
Zhuanzhuan Zhao
Keyword(s):  
Regression Model ◽  
Nonlinear Regression ◽  
Goodness Of Fit ◽  
Sensitive Indicator ◽  
Nonlinear Regression Model ◽  
City Bus ◽  
Bus Scheduling ◽  
Proposed Model ◽  
Xi’An City

Passenger crowding in a city bus is uneven and the most crowded area always appears in the wheelbase of the carriage. The present study aimed to provide a sensitive indicator of the most crowded area to schedule bus headways online using a binocular camera sensor. The algorithm of standee density in the wheelbase area (SDWA) was given by a nonlinear regression model considering standees’ preferences for the standing area, and its goodness of fit and continuity were tested. Considering the characteristics of city bus operation, the proportion of the number of interstops determined from the SDWA was used as a judgment index for passenger crowding. Based on the SDWA algorithm and the judgment index, an online headway model of city buses was proposed, and the feasibility of such a model was verified through a case study in Xi’an city. The proposed model might be beneficial to bus scheduling, seating provision, and bus design.

scholarly journals Dimension Analysis-Based Model for Prediction of Shale Compressive Strength

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangyu Fan ◽  
Fenglin Xu ◽  
Lin Chen ◽  
Qiao Chen ◽  
Zhiwei Liu ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Prediction Error ◽  
Diameter Ratio ◽  
Nonlinear Regression Model ◽  
Average Prediction Error ◽  
Model Average ◽  
Dimension Analysis ◽  
Longmaxi Shale

The compressive strength of shale is a comprehensive index for evaluating the shale strength, which is linked to shale well borehole stability. Based on correlation analysis between factors (confining stress, height/diameter ratio, bedding angle, and porosity) and shale compressive strength (Longmaxi Shale in Sichuan Basin, China), we develop a dimension analysis-based model for prediction of shale compressive strength. A nonlinear-regression model is used for comparison. A multitraining method is used to achieve reliability of model prediction. The results show that, compared to a multi-nonlinear-regression model (average prediction error = 19.5%), the average prediction error of the dimension analysis-based model is 19.2%. More importantly, our dimension analysis-based model needs to determine only one parameter, whereas the multi-nonlinear-regression model needs to determine five. In addition, sensitivity analysis shows that height/diameter ratio has greater sensitivity to compressive strength than other factors.

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