Travel Space and Mode Combined Choice Model for Urban Travelers
Linjie Gao
Abstract
Linjie Gao
Abstract
Considering the important relationship between travel space and mode choice,based on the theory of Nested Logit model and its modeling method for disaggregate model,using the data of a revealed preference survey of daily travel of one Chinese big city in 2005,a conbined choice model was built to analyse the travel space and mode choice of urban travelers.The model is a two level NL model which consists of space choice level and mode choice level to connect space choice and mode choice.The model was calibrated and tested by applying STATA9 statistics software.Based on the model,the important factors affecting the travel space and mode choices were revealed.At the end,a mode choice MNL model was built with the same data,and the hit ratio and aggregate result of the presented model were tested and compared with those of multinomial Logit model at the end.The result shows that this combined model can predict travel space and mode choice behavior at a more detailed level to improve the model's accuracy and tility.
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Considering the important relationship between travel space and mode choice,based on the theory of Nested Logit model and its modeling method for disaggregate model,using the data of a revealed preference survey of daily travel of one Chinese big city in 2005,a conbined choice model was built to analyse the travel space and mode choice of urban travelers.The model is a two level NL model which consists of space choice level and mode choice level to connect space choice and mode choice.The model was calibrated and tested by applying STATA9 statistics software.Based on the model,the important factors affecting the travel space and mode choices were revealed.At the end,a mode choice MNL model was built with the same data,and the hit ratio and aggregate result of the presented model were tested and compared with those of multinomial Logit model at the end.The result shows that this combined model can predict travel space and mode choice behavior at a more detailed level to improve the model's accuracy and tility.
Key concepts: Multinomial logistic regression, Mode choice, Mode (computer interface), Space (punctuation), Discrete choice, Econometrics, Nested logit, Choice set