scholarly journals Mode Choice Modeling Using Personalized Travel Time and Cost Data

2015 ◽  
Author(s):  
Mahmoud Javanmardi
Keyword(s):  
Travel Time ◽  
Mode Choice ◽  
Choice Modeling ◽  
Cost Data ◽  
Mode Choice Modeling

Development of a New Toll Mode-Choice Modeling System for Florida’s Turnpike Enterprise

2003 ◽  
Vol 1858 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Youssef Dehghani ◽  
Thomas Adler ◽  
Michael W. Doherty ◽  
Randy Fox
Keyword(s):  
Travel Time ◽  
Choice Model ◽  
Mode Choice ◽  
Model Development ◽  
Time Of Day ◽  
Choice Modeling ◽  
Fort Lauderdale ◽  
Trip Purpose ◽  
Mode Choice Model ◽  
Mode Choice Modeling

The Florida Department of Transportation Turnpike Enterprise’s recent toll mode-choice model development activities are described. Because the simple toll travel forecasting analysis methods used were not adequate for reliably addressing contemporary toll study issues, there was a need for toll modeling innovations that address trip makers’ toll route decisions as a mode-choice step sensitive to changes in service levels by time of day, trip purpose, and socioeconomic attributes. Innovations developed for Florida’s turnpike began with data-collection efforts and toll model development for the Central Florida (Orlando) region. This represents the next generation of modeling system. Similar efforts are under way for the Miami–Fort Lauderdale area. The Orlando region toll mode-choice model, which is in its final validation phase, includes a statistically estimated nested mode-choice modeling system with a discrete choice for toll travel. The models were developed for a combination of four periods and four trip purposes, including visitor trips. Other key features are ( a) a pre-mode-choice time-of-day process; ( b) a generalized cost-assignment procedure that uses travel time and costs by time of day (rather than travel time alone); ( c) production of zone-to-zone travel time and costs consistent with travel paths; and ( d) a feedback loop process that uses an iterative successive averaging procedure to estimate travel times.

scholarly journals Current issues in mode choice modeling

2011 ◽  
Vol 38 (4) ◽  
pp. 581-585 ◽  
Author(s):  
Maya Abou-Zeid ◽  
Darren M. Scott
Keyword(s):  
Mode Choice ◽  
Choice Modeling ◽  
Mode Choice Modeling

scholarly journals Travel mode choice modeling from cross-sectional survey and panel data: the inclusion of initial nonresponse

2018 ◽  
Vol 32 ◽  
pp. 268-278 ◽  
Author(s):  
M.C. de Haas ◽  
R.G. Hoogendoorn ◽  
C.E. Scheepers ◽  
S. Hoogendoorn-Lanser
Keyword(s):  
Panel Data ◽  
Mode Choice ◽  
Choice Modeling ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Mode Choice Modeling

Adaptive Zoning for Transport Mode Choice Modeling

2013 ◽  
pp. n/a-n/a ◽  
Author(s):  
Alex Hagen-Zanker ◽  
Ying Jin
Keyword(s):  
Mode Choice ◽  
Choice Modeling ◽  
Transport Mode ◽  
Mode Choice Modeling

Mode choice modeling for long-distance travel

2014 ◽  
Vol 7 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Rolf Moeckel ◽  
Rhett Fussell ◽  
Rick Donnelly
Keyword(s):  
Mode Choice ◽  
Choice Modeling ◽  
Long Distance ◽  
Distance Travel ◽  
Long Distance Travel ◽  
Mode Choice Modeling

scholarly journals Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Eui-Jin Kim
Keyword(s):  
Machine Learning ◽  
Choice Behavior ◽  
Travel Demand ◽  
Mode Choice ◽  
Variable Importance ◽  
Choice Modeling ◽  
Travel Mode ◽  
Extreme Gradient Boosting ◽  
Travel Mode Choice ◽  
Mode Choice Modeling

Understanding choice behavior regarding travel mode is essential in forecasting travel demand. Machine learning (ML) approaches have been proposed to model mode choice behavior, and their usefulness for predicting performance has been reported. However, due to the black-box nature of ML, it is difficult to determine a suitable explanation for the relationship between the input and output variables. This paper proposes an interpretable ML approach to improve the interpretability (i.e., the degree of understanding the cause of decisions) of ML concerning travel mode choice modeling. This approach applied to national household travel survey data in Seoul. First, extreme gradient boosting (XGB) was applied to travel mode choice modeling, and the XGB outperformed the other ML models. Variable importance, variable interaction, and accumulated local effects (ALE) were measured to interpret the prediction of the best-performing XGB. The results of variable importance and interaction indicated that the correlated trip- and tour-related variables significantly influence predicting travel mode choice by the main and cross effects between them. Age and number of trips on tour were also shown to be an important variable in choosing travel mode. ALE measured the main effect of variables that have a nonlinear relation to choice probability, which cannot be observed in the conventional multinomial logit model. This information can provide interesting behavioral insights on urban mobility.

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