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Bus Dwell Time Modeling Using Gene Expression Programming
This article proposes a gene expression programming (GEP)‐based approach to model and estimate bus dwell time (BDT) as an alternative to the more commonly used multiple linear regression (MLR) model. The proposed model is calibrated and validated using the data collected from 22 bus stops in Auckland and compared against the MLR model based on five different performance measures namely: mean error, mean absolute error, root mean square error, mean absolute percentage error, and R 2 value. The proposed GEP‐based approach shows prospects to estimate BDT more accurately and overcome some of the issues associated with the MLR method, including the restrictions to stick with a predefined model form and the need to satisfy assumptions made on multicollinearity, homoscedasticity, and the normality of random error, which are often difficult to satisfy in real world applications.
Bus Dwell Time Modeling Using Gene Expression Programming
This article proposes a gene expression programming (GEP)‐based approach to model and estimate bus dwell time (BDT) as an alternative to the more commonly used multiple linear regression (MLR) model. The proposed model is calibrated and validated using the data collected from 22 bus stops in Auckland and compared against the MLR model based on five different performance measures namely: mean error, mean absolute error, root mean square error, mean absolute percentage error, and R 2 value. The proposed GEP‐based approach shows prospects to estimate BDT more accurately and overcome some of the issues associated with the MLR method, including the restrictions to stick with a predefined model form and the need to satisfy assumptions made on multicollinearity, homoscedasticity, and the normality of random error, which are often difficult to satisfy in real world applications.
Bus Dwell Time Modeling Using Gene Expression Programming
Rashidi, Soroush (author) / Ranjitkar, Prakash
2015
Article (Journal)
English
BKL:
56.00
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