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

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.

1982 ◽  
Vol 8 (4) ◽  
pp. 370 ◽  
Author(s):  
Richard Barff ◽  
David Mackay ◽  
Richard W. Olshavsky

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.


2019 ◽  
Vol 14 ◽  
pp. 1-10 ◽  
Author(s):  
Long Cheng ◽  
Xuewu Chen ◽  
Jonas De Vos ◽  
Xinjun Lai ◽  
Frank Witlox

2018 ◽  
Vol 10 (6) ◽  
pp. 1996 ◽  
Author(s):  
Yan Han ◽  
Wanying Li ◽  
Shanshan Wei ◽  
Tiantian Zhang

2013 ◽  
Vol 361-363 ◽  
pp. 1906-1909
Author(s):  
Xia Liu ◽  
Jian Lu

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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