The application of a logistic regression model for predicting preferences of transport system users

Andrzej Brzeziński , Karol Brzeziński , Tomasz Jacek Dybicz , Ł. Szymański


Within the INMOP 3 research project, an attempt was made to solve a number of problems associated with the methodology of modelling travel in urban areas and the application of intermodal models. One of these is the ability to describe the behaviour of transport system users, when it comes to making decisions regarding the selection of means of transport and searching for relationships between travel describing factors and the decisions made in regard of means of transport choice.The paper describes a probabilistic approach to the determination of modal split, and the application of a logistic regression model to determine the impact of variables describing individual and mass transport travels on the probability of selecting specific means of transport. Travels in local model of Warsaw city divided into 9 motivation groups were tested, for which ultimately 8 models were developed, out of which 7 were deemed very well fitted (obtained pseudo R2 was well above 0.2).
Author Andrzej Brzeziński (FCE / IRB)
Andrzej Brzeziński,,
- The Institute of Roads and Bridges
, Karol Brzeziński (FCE / IRB)
Karol Brzeziński,,
- The Institute of Roads and Bridges
, Tomasz Jacek Dybicz (FCE / IRB)
Tomasz Jacek Dybicz,,
- The Institute of Roads and Bridges
, Ł. Szymański
Ł. Szymański,,
Journal seriesArchives of Civil Engineering [Archiwum Inżynierii Lądowej], ISSN 1230-2945, (B 15 pkt)
Issue year2018
Publication size in sheets0.7
Keywords in English transport system; travel modelling; modal split; logistic regression; logit model
ASJC Classification2205 Civil and Structural Engineering
Languageen angielski
[12302945 - Archives of Civil Engineering] The Application of a Logistic Regression Model for Predicting Preferences of Transport System Users.pdf 267.06 KB
Score (nominal)15
ScoreMinisterial score = 15.0, 24-05-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2016 = 0.623
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