Modeling of Intercity Travel Mode Choice Behavior for Non-Business Trips within Libya

Gender difference is an important factor in travel mode choice behavior. In this paper, some characteristics of different travelers were found from a survey of Zhenfeng City. Based on the data, this paper developed MNL models about four main travel mode choices (walk, bus, car and motorcycle) of different gender, and six variables were used in the models. Overall, the models represented the gender differences in travel mode choice, and it was influenced by a wide variety of variables, including age, employment status, household income, number of cars, number of motorcycles and travel purpose.

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

Computational Intelligence and Neuroscience ◽

10.1155/2021/9969322 ◽

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.

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Deep Neural Network Design for Modeling Individual-Level Travel Mode Choice Behavior

Sustainability ◽

10.3390/su12187481 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7481

Author(s):

Daisik Nam ◽

Jaewoo Cho

Keyword(s):

Neural Network ◽

Discrete Choice ◽

Deep Neural Network ◽

Choice Behavior ◽

Mode Choice ◽

Choice Models ◽

Discrete Choice Models ◽

Travel Mode ◽

Individual Level ◽

Travel Mode Choice

Individual-level modeling is an essential requirement for effective deployment of smart urban mobility applications. Mode choice behavior is also a core feature in transportation planning models, which are used for analyzing future policies and sustainable plans such as greenhouse gas emissions reduction plans. Specifically, an agent-based model requires an individual level choice behavior, mode choice being one such example. However, traditional utility-based discrete choice models, such as logit models, are limited to aggregated behavior analysis. This paper develops a model employing a deep neural network structure that is applicable to the travel mode choice problem. This paper uses deep learning algorithms to highlight an individual-level mode choice behavior model, which leads us to take into account the inherent characteristics of choice models that all individuals have different choice options, an aspect not considered in the neural network models of the past that have led to poorer performance. Comparative analysis with existing behavior models indicates that the proposed model outperforms traditional discrete choice models in terms of prediction accuracy for both individual and aggregated behavior.

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