scholarly journals A Bayesian Network Model on the Public Bicycle Choice Behavior of Residents: A Case Study of Xi’an

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Qiuping Wang ◽  
Hao Sun ◽  
Qi Zhang
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Expert Knowledge ◽  
Choice Model ◽  
Mode Choice ◽  
Bayesian Network Model ◽  
Travel Mode ◽  
The Public ◽  
Proposed Model ◽  
Travel Mode Choice

In order to study the main factors affecting the behaviors that city residents make regarding public bicycle choice and to further study the public bicycle user’s personal characteristics and travel characteristics, a travel mode choice model based on a Bayesian network was established. Taking residents of Xi’an as the research object, a K2 algorithm combined with mutual information and expert knowledge was proposed for Bayesian network structure learning. The Bayesian estimation method was used to estimate the parameters of the network, and a Bayesian network model was established to reflect the interactions among the public bicycle choice behaviors along with other major factors. The K-fold cross-validation method was used to validate the model performance, and the hit rate of each travel mode was more than 80%, indicating the precision of the proposed model. Experimental results also present the higher classification accuracy of the proposed model. Therefore, it may be concluded that the resident travel mode choice may be accurately predicted according to the Bayesian network model proposed in our study. Additionally, this model may be employed to analyze and discuss changes in the resident public bicycle choice and to note that they may possibly be influenced by different travelers’ characteristics and trip characteristics.

scholarly journals Exploring the Influence of Attitudes to Walking and Cycling on Commute Mode Choice Using a Hybrid Choice Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Chuan Ding ◽  
Yu Chen ◽  
Jinxiao Duan ◽  
Yingrong Lu ◽  
Jianxun Cui
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Structural Equation ◽  
Choice Model ◽  
Mode Choice ◽  
Influential Factors ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
The Impact ◽  
Hybrid Choice Model

Transport-related problems, such as automobile dependence, traffic congestion, and greenhouse emissions, lead to a great burden on the environment. In developing countries like China, in order to improve the air quality, promoting sustainable travel modes to reduce the automobile usage is gradually recognized as an emerging national concern. Though there are many studies related to the physically active modes (e.g., walking and cycling), the research on the influence of attitudes to active modes on travel behavior is limited, especially in China. To fill up this gap, this paper focuses on examining the impact of attitudes to walking and cycling on commute mode choice. Using the survey data collected in China cities, an integrated discrete choice model and the structural equation model are proposed. By applying the hybrid choice model, not only the role of the latent attitude played in travel mode choice, but also the indirect effects of social factors on travel mode choice are obtained. The comparison indicates that the hybrid choice model outperforms the traditional model. This study is expected to provide a better understanding for urban planners on the influential factors of green travel modes.

scholarly journals Choice Model and Influencing Factor Analysis of Travel Mode for Migrant Workers: Case Study in Xi’an, China

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Hong Chen ◽  
Zuo-xian Gan ◽  
Yu-ting He
Keyword(s):  
Influencing Factors ◽  
Migrant Workers ◽  
Choice Model ◽  
Mode Choice ◽  
Influencing Factor ◽  
Travel Mode ◽  
Factors Affecting ◽  
Travel Choice ◽  
Travel Mode Choice ◽  
Set Up

Based on the basic theory and methods of disaggregate choice model, the influencing factors in travel mode choice for migrant workers are analyzed, according to 1366 data samples of Xi’an migrant workers. Walking, bus, subway, and taxi are taken as the alternative parts of travel modes for migrant workers, and a multinomial logit (MNL) model of travel mode for migrant workers is set up. The validity of the model is verified by the hit rate, and the hit rates of four travel modes are all greater than 80%. Finally, the influence of different factors affecting the choice of travel mode is analyzed in detail, and the inelasticity of each factor is analyzed with the elasticity theory. Influencing factors such as age, education level, and monthly gross income have significant impact on travel choice mode for migrant workers. The elasticity values of education degree are greater than 1, indicating that it on the travel mode choice is of elasticity, while the elasticity values of gender, industry distribution, and travel purpose are less than 1, indicating that these factors on travel mode choice are of inelasticity.

scholarly journals A review of Machine Learning (ML) algorithms used for modeling travel mode choice

DYNA ◽  
2019 ◽  
Vol 86 (211) ◽  
pp. 32-41 ◽  
Author(s):  
Juan D. Pineda-Jaramillo
Keyword(s):  
Machine Learning ◽  
Choice Model ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Support Vector ◽  
Travel Mode ◽  
Wide Range ◽  
Travel Mode Choice ◽  
And Cluster Analysis ◽  
Transportation Research

In recent decades, transportation planning researchers have used diverse types of machine learning (ML) algorithms to research a wide range of topics. This review paper starts with a brief explanation of some ML algorithms commonly used for transportation research, specifically Artificial Neural Networks (ANN), Decision Trees (DT), Support Vector Machines (SVM) and Cluster Analysis (CA). Then, these different methodologies used by researchers for modeling travel mode choice are collected and compared with the Multinomial Logit Model (MNL) which is the most commonly-used discrete choice model. Finally, the characterization of ML algorithms is discussed and Random Forest (RF), a variant of Decision Tree algorithms, is presented as the best methodology for modeling travel mode choice.

Travel Mode Choice Modeling: Predictive Efficacy between Machine Learning Models and Discrete Choice Model

2021 ◽  
Vol 15 (1) ◽  
pp. 241-255
Author(s):  
Nur Fahriza Mohd. Ali ◽  
Ahmad Farhan Mohd. Sadullah ◽  
Anwar PP Abdul Majeed ◽  
Mohd Azraai Mohd. Razman ◽  
Muhammad Aizzat Zakaria ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discrete Choice ◽  
Choice Model ◽  
Mode Choice ◽  
Binary Logistic Regression ◽  
Discrete Choice Model ◽  
Learning Models ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Machine Learning Models

Background: A complex travel behaviour among users is intertwined with many factors. Traditionally, the exploration in travel mode choice modeling has been dominated by the Discrete Choice model, nonetheless, owing to the advancement in computational techniques, machine learning has gained traction in understanding travel behavior. Aim: This study aims at predicting users’ travel model choice by means of machine learning models against a conventional Discrete Choice Model, i.e., Binary Logistic Regression. Objective: To investigate the comparison between machine learning models, namely Neural Network, Random Forest, Decision Tree, and Support Vector Machine against the Discrete Choice Model (Binary Logistic Regression) in the prediction of travel mode choice amongst Kuantan City. Methodology: The dataset was collected in Kuantan City, Malaysia, through the Revealed/Stated Preferences (RP/SP) Survey. The data collected was split into a ratio of 80:20 for training and testing before evaluating them between the aforesaid models. The hyperparameters of the models were set to default. The performance of the models is evaluated based on classification accuracy. Results: It was shown in the present study that the Neural Network Model is able to attain a higher prediction accuracy as compared to Binary Logistic Regression (Discrete Choice Model) in classifying mode choice of Kuantan users either to choose public transport or private vehicles as daily transportation. Feature importance technique is crucial for identifying the significant features in modelling travel mode choice. It is demonstrated that the Neural Network Model can yield exceptional classification of mode choice up to 73.4% and 72.4% of training and testing data, respectively, by considering the features identified via the feature importance technique, suggesting the viability of the proposed technique in supporting an informed decision. Conclusion: The findings highlight the strengths and limitations of the Machine Learning Technique as well as the Discrete Choice Model in modeling travel mode choice. It was shown that Machine Learning models have the capability to provide better prediction that could assist the urban transportation planning among policymakers. Meanwhile, it could be also demonstrated that the Discrete Choice Model (Binary Logistic Regression) is helpful in getting a better understanding in expressing the inference relationship between variables for improvising the future transportation system.

Travel Mode Choice Modeling: A Comparison of Bayesian Networks and Neural Networks

2012 ◽  
Vol 209-211 ◽  
pp. 717-723
Author(s):  
Dou Nan Tang ◽  
Min Yang ◽  
Mei Hui Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bayesian Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Prediction Performance ◽  
Bayesian Network Model ◽  
Travel Mode ◽  
Travel Mode Choice

In recent years, Bayesian networks and neural networks have been widely applied to the travel demand prediction area. However, their prediction performance is rarely directly compared. By experimental tests conducted using the same dataset, a Bayesian network model and a neural network model are compared for the travel mode analysis for the first time in this paper. It is found that the fully Bayesian network model tends to overfit the training set when the network itself is considerable complicated. The TAN structure otherwise has a better generalization performance and can achieve a better and more stable prediction performance, for its prediction accuracy 75.4%±0.63%, compared to the BP neural network model ,which prediction accuracy is 72.2%±3.01%. Experiment and statistical tests demonstrate the superiority of Bayesian networks and we propose using Bayesian networks, especially TAN, instead of neural networks in the travel mode choice prediction field.

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