scholarly journals Travel mode choice modeling from cross-sectional survey and panel data: the inclusion of initial nonresponse

2018 ◽  
Vol 32 ◽  
pp. 268-278 ◽  
Author(s):  
M.C. de Haas ◽  
R.G. Hoogendoorn ◽  
C.E. Scheepers ◽  
S. Hoogendoorn-Lanser
Keyword(s):  
Panel Data ◽  
Mode Choice ◽  
Choice Modeling ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Mode Choice Modeling

scholarly journals Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Eui-Jin Kim
Keyword(s):  
Machine Learning ◽  
Choice Behavior ◽  
Travel Demand ◽  
Mode Choice ◽  
Variable Importance ◽  
Choice Modeling ◽  
Travel Mode ◽  
Extreme Gradient Boosting ◽  
Travel Mode Choice ◽  
Mode Choice Modeling

Understanding choice behavior regarding travel mode is essential in forecasting travel demand. Machine learning (ML) approaches have been proposed to model mode choice behavior, and their usefulness for predicting performance has been reported. However, due to the black-box nature of ML, it is difficult to determine a suitable explanation for the relationship between the input and output variables. This paper proposes an interpretable ML approach to improve the interpretability (i.e., the degree of understanding the cause of decisions) of ML concerning travel mode choice modeling. This approach applied to national household travel survey data in Seoul. First, extreme gradient boosting (XGB) was applied to travel mode choice modeling, and the XGB outperformed the other ML models. Variable importance, variable interaction, and accumulated local effects (ALE) were measured to interpret the prediction of the best-performing XGB. The results of variable importance and interaction indicated that the correlated trip- and tour-related variables significantly influence predicting travel mode choice by the main and cross effects between them. Age and number of trips on tour were also shown to be an important variable in choosing travel mode. ALE measured the main effect of variables that have a nonlinear relation to choice probability, which cannot be observed in the conventional multinomial logit model. This information can provide interesting behavioral insights on urban mobility.

Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks

2003 ◽  
Vol 1854 (1) ◽  
pp. 50-61 ◽  
Author(s):  
Chi Xie ◽  
Jinyang Lu ◽  
Emily Parkany
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Mode Choice ◽  
Choice Modeling ◽  
Travel Mode ◽  
Prediction Rate ◽  
Travel Mode Choice ◽  
Mnl Model ◽  
Mode Choice Modeling

Among discrete choice problems, travel mode choice modeling has received the most attention in the travel behavior literature. Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem in which multiple human behavioral patterns reflected by explanatory variables determine the choices between alternatives or classes. The capability and performance of two emerging pattern recognition data mining methods, decision trees (DT) and neural networks (NN), for work travel mode choice modeling were investigated. Models based on these two techniques are specified, estimated, and comparatively evaluated with a traditional multinomial logit (MNL) model. For comparison, a unique three-layer formulation of the MNL model is presented. The similarities and differences of the models' mechanisms and structures are identified, and the mechanisms and structures in the models' specifications and estimations are compared. Two performance measures, individual prediction rate and aggregate prediction rate, which represent the prediction accuracies for individual and mode aggregate levels, respectively, were applied to evaluate and compare the performances of the models. Diary data sets from the San Francisco, California, Bay Area Travel Survey 2000 were used for model estimation and evaluation. The prediction results show that the two data mining models offer comparable but slightly better performances than the MNL model in terms of the modeling results, while the DT model demonstrates the highest estimation efficiency and most explicit interpretability, and the NN model gives a superior prediction performance in most cases.

Travel Mode Choice Modeling Based on Improved Probabilistic Neural Network

2010 ◽  
Author(s):  
Dan Zhao ◽  
Chunfu Shao ◽  
Juan Li ◽  
Chunjiao Dong ◽  
Yinhong Liu
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Mode Choice ◽  
Choice Modeling ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Mode Choice Modeling

Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model

2018 ◽  
Vol 2672 (47) ◽  
pp. 35-45 ◽  
Author(s):  
Fangru Wang ◽  
Catherine L. Ross
Keyword(s):  
Machine Learning ◽  
Mode Choice ◽  
Multinomial Logit ◽  
Choice Modeling ◽  
Gradient Boosting ◽  
Travel Mode ◽  
Extreme Gradient Boosting ◽  
Travel Mode Choice ◽  
Mnl Model ◽  
Mode Choice Modeling

The multinomial logit (MNL) model and its variations have been dominating the travel mode choice modeling field for decades. Advantages of the MNL model include its elegant closed-form mathematical structure and its interpretable model estimation results based on random utility theory, while its main limitation is the strict statistical assumptions. Recent computational advancement has allowed easier application of machine learning models to travel behavior analysis, though research in this field is not thorough or conclusive. In this paper, we explore the application of the extreme gradient boosting (XGB) model to travel mode choice modeling and compare the result with an MNL model, using the Delaware Valley 2012 regional household travel survey data. The XGB model is an ensemble method based on the decision-tree algorithm and it has recently received a great deal of attention and use because of its high machine learning performance. The modeling and predicting results of the XGB model and the MNL model are compared by examining their multi-class predictive errors. We found that the XGB model has overall higher prediction accuracy than the MNL model especially when the dataset is not extremely unbalanced. The MNL model has great explanatory power and it also displays strong consistency between training and testing errors. Multiple trip characteristics, socio-demographic traits, and built-environment variables are found to be significantly associated with people’s mode choices in the region, but mode-specific travel time is found to be the most determinant factor for mode choice.

scholarly journals Modeling Mode Choice Behavior Incorporating Household and Individual Sociodemographics and Travel Attributes Based on Rough Sets Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Long Cheng ◽  
Xuewu Chen ◽  
Ming Wei ◽  
Jingxian Wu ◽  
Xianyao Hou
Keyword(s):  
Rough Sets ◽  
Choice Behavior ◽  
Mode Choice ◽  
Predictive Ability ◽  
Classification Performance ◽  
Choice Modeling ◽  
Travel Mode ◽  
Rough Sets Theory ◽  
Sets Theory ◽  
Mode Choice Modeling

Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem reflected from the explanatory variables of determining the choices between alternatives. The paper applies the knowledge discovery technique of rough sets theory to model travel mode choices incorporating household and individual sociodemographics and travel information, and to identify the significance of each attribute. The study uses the detailed travel diary survey data of Changxing county which contains information on both household and individual travel behaviors for model estimation and evaluation. The knowledge is presented in the form of easily understood IF-THEN statements or rules which reveal how each attribute influences mode choice behavior. These rules are then used to predict travel mode choices from information held about previously unseen individuals and the classification performance is assessed. The rough sets model shows high robustness and good predictive ability. The most significant condition attributes identified to determine travel mode choices are gender, distance, household annual income, and occupation. Comparative evaluation with the MNL model also proves that the rough sets model gives superior prediction accuracy and coverage on travel mode choice modeling.

Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China

2021 ◽  
Vol 93 ◽  
pp. 103070
Author(s):  
Yuqiao Huang ◽  
Linjie Gao ◽  
Anning Ni ◽  
Xiaoning Liu
Keyword(s):  
Mode Choice ◽  
Gps Data ◽  
Travel Mode ◽  
Trip Chain ◽  
Travel Mode Choice
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