Prediction of Travel Mode Choice Behavior Preference under the Impacts of Congestion Pricing Based on ICLV Model

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Yaping Li ◽  
Shuai Sun
Keyword(s):  
Choice Behavior ◽  
Mode Choice ◽  
Congestion Pricing ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Behavior Preference

Applying a random forest method approach to model travel mode choice behavior

2019 ◽  
Vol 14 ◽  
pp. 1-10 ◽  
Author(s):  
Long Cheng ◽  
Xuewu Chen ◽  
Jonas De Vos ◽  
Xinjun Lai ◽  
Frank Witlox
Keyword(s):  
Random Forest ◽  
Choice Behavior ◽  
Mode Choice ◽  
Travel Mode ◽  
Random Forest Method ◽  
Travel Mode Choice ◽  
Method Approach

scholarly journals Research on Passenger’s Travel Mode Choice Behavior Waiting at Bus Station Based on SEM-Logit Integration Model

2018 ◽  
Vol 10 (6) ◽  
pp. 1996 ◽  
Author(s):  
Yan Han ◽  
Wanying Li ◽  
Shanshan Wei ◽  
Tiantian Zhang
Keyword(s):  
Choice Behavior ◽  
Mode Choice ◽  
Integration Model ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Bus Station

Gender Differences in Travel Mode Choice Behavior in Zhenfeng City

2013 ◽  
Vol 361-363 ◽  
pp. 1906-1909
Author(s):  
Xia Liu ◽  
Jian Lu
Keyword(s):  
Gender Differences ◽  
Gender Difference ◽  
Household Income ◽  
Employment Status ◽  
Choice Behavior ◽  
Mode Choice ◽  
Travel Mode ◽  
Travel Mode Choice

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.

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

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.

Modeling travel mode choice behavior with bounded rationality using Markov Logic Networks

2017 ◽  
Vol 11 (6) ◽  
pp. 303-310 ◽  
Author(s):  
Tao Li ◽  
Hongzhi Guan ◽  
Jiaqi Ma ◽  
Guohui Zhang ◽  
Keke Liang
Keyword(s):  
Bounded Rationality ◽  
Choice Behavior ◽  
Mode Choice ◽  
Markov Logic Networks ◽  
Travel Mode ◽  
Markov Logic ◽  
Travel Mode Choice
Sign in / Sign up

Export Citation Format

Share Document