scholarly journals Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Xiaowei Li ◽  
Yuting Wang ◽  
Yao Wu ◽  
Jun Chen ◽  
Jibiao Zhou
Keyword(s):  
Comparative Analysis ◽  
High Speed ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Multinomial Logit ◽  
Neural Network Models ◽  
Activity Data ◽  
Travel Mode ◽  
Intercity Travel ◽  
Artificial Neural

This study conducts a comprehensive comparative analysis of regression-based multinomial models and artificial neural network models in intercity travel mode choices. The four intercity travel modes of airplane, high-speed rail (HSR), train, and express bus were used for analysis. Passengers’ activity data over the process of intercity travel were collected to develop the models. The standard multinomial logit (MNL) regression and Bayesian multinomial logit (BMNL) regression were compared with the radial basis function (RBF) and multilayer perceptron (MLP). The results show that MLP performs best in terms of predictive accuracy, followed by BMNL and MNL, and RBF is the least accurate. The performances of all models were examined against changes in data balance, and it was found that rebalancing can improve fitting performance while slightly reducing the predictive performance. This comparative study and its parameter estimation shed new light on the comparison of traditional and emerging models in travel behavior studies, and the findings can be used as heuristic guidance for all stakeholders.

scholarly journals Influence of Weather Conditions on the Intercity Travel Mode Choice: A Case of Xi’an

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaowei Li ◽  
Qiangqiang Ma ◽  
Wenbo Wang ◽  
Baojie Wang
Keyword(s):  
High Speed ◽  
Mode Choice ◽  
Mode Selection ◽  
Weather Conditions ◽  
Individual Characteristics ◽  
Activity Data ◽  
Travel Mode ◽  
Intercity Travel ◽  
Travel Mode Choice ◽  
Weather Characteristics

To explore the influence of weather conditions on the choice of the intercity travel mode of travelers, four modes of traveler transportation were studied in Xi'an, China, in March 2019: airplane, high-speed rail, conventional train, and express bus. The individual characteristics of travelers and intercity travel activity data were obtained, and they were matched with the weather characteristics at the departure time of the travelers. The Bayesian multinomial logit regression was employed to explore the relationship between the travel mode choice and weather characteristics. The results showed that temperature, relative humidity, rainfall, wind, air quality index, and visibility had significant effects on the travel mode selection of travelers, and the addition of these variables could improve the model’s predictive performance. The research results can provide a scientific decision basis for traveler flow transfer and the prediction of traffic modes choice due to the effects of climate change.

Effects of accessibility to the transit stations on intercity travel mode choices in contexts of high speed rail in the Windsor–Quebec corridor in Canada

2015 ◽  
Vol 42 (11) ◽  
pp. 930-939 ◽  
Author(s):  
Billy Wong ◽  
Khandker M. Nurul Habib
Keyword(s):  
Social Media ◽  
Data Collection ◽  
High Speed ◽  
Choice Model ◽  
High Speed Rail ◽  
Travel Mode ◽  
Web Based ◽  
Intercity Travel ◽  
Transit Station ◽  
Data Collection Approach

Main objective of this paper is investigating the role of transit station accessibility on intercity travel mode choices in contexts of a proposed High Speed Rail. The study area is the Quebec–Windsor corridor, which is the most important corridor in Canada and one of the most important corridors in North America. A web-based joint revealed preference – stated preference survey is used to collect data for empirical investigation. To contribute further to travel survey methods, an innovative social media based data collection approach is taken. As opposed to explicit sample frame-based sample selection approach, it applies a reverse procedure of open sample frame-based data collection. The web-based survey is spread through social media groups (that are open in sense that information of all individuals are not known explicitly) and the collected responses are screened to match with population distributions. Results prove the potential of such data collection approach in extracting representative samples of the population of concern. The collected dataset, which has close representation of the population, is used to estimate discrete mode choice model (Nested Logit model) of intercity mode choices. Empirical model reveals that intercity travellers are more concerned about access to and egress from transit stations than the main in-vehicle travel while selecting intercity travel modes. The result of this investigate imply that transit station accessibility should be given careful consideration for the success of any innovative travel mode, e.g., high speed rail.

scholarly journals Exploring Influencing Factors of Intercity Mode Choice from View of Entire Travel Chain

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiaowei Li ◽  
Siyu Zhang ◽  
Yao Wu ◽  
Yuting Wang ◽  
Wenbo Wang
Keyword(s):  
Logit Model ◽  
Unobserved Heterogeneity ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Travel Mode ◽  
Intercity Travel ◽  
Travel Mode Choice ◽  
Mixed Multinomial Logit Model ◽  
Significant Factors

Exploring the influencing factors of intercity travel mode choice can reveal passengers’ travel decision mechanisms and help traffic departments to develop an effective demand management policy. To investigate these factors, a survey was conducted in Xi’an, China, to collect data about passengers’ travel chains, including airplane, high-speed railway (HSR), train, and express bus. A Bayesian mixed multinomial logit model is developed to identify significant factors and explicate unobserved heterogeneity across observations. The effect of significant factors on intercity travel mode choice is quantitatively assessed by the odds ratio (OR) technique. The results show that the Bayesian mixed multinomial logit model outperforms the traditional Bayesian multinomial logit model, indicating that accommodating the unobserved heterogeneity across observations can improve the model fit. The model estimation results show that ticket purchasing method, comfort, punctuality, and access time are random parameters that have heterogeneous effects on intercity travel mode choice.

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