Classification methods from machine learning are receiving a lot of attention in the transportation modelling community. This is motivated by the access to large databases, and to various success stories reported in this research community. Discrete choice models have been used for decades to model the behaviour of travellers, faced to various types of decisions. In this talk, we will compare the two approaches, and highlight their differences. In particular, we will identify good and less good practices in each community
This article evaluates dynamic driver behaviour models that can be used, in the context of intellige...
As travellers are faced with an increasing portfolio of transportation options, researchers are simi...
Discrete choice models have been in use for decades for mode choice and route choice analysis. These...
Background: A complex travel behaviour among users is intertwined with many factors. Traditionally, ...
Since its inception, the choice modelling field has been dominated by theory-driven modelling approa...
Artificial Intelligence in form of Machine Learning classifiers is increasingly applied for travel c...
For decades, Discrete Choice Models (DCMs) have been used to describe, understand and predict human ...
Every day, decision-makers make choices among finite and discrete sets of alternatives. For example,...
This paper discusses important developments in discrete choice modeling for transportation applicati...
In recent decades, transportation planning researchers have used diverse types of machine learning (...
Research in the field of artificial intelligence systems has been exploring the use of artificial ne...
The continuous progress of machine learning has introduced numerous powerful classifiers that are ex...
The analysis of travel mode choice is an important task in transportation planning and policy making...
Understanding and predicting traveller behaviour remains a complex activity. The set of tools in com...
The continuous progress of machine learning has introduced numerous powerful classifiers that are ex...
This article evaluates dynamic driver behaviour models that can be used, in the context of intellige...
As travellers are faced with an increasing portfolio of transportation options, researchers are simi...
Discrete choice models have been in use for decades for mode choice and route choice analysis. These...
Background: A complex travel behaviour among users is intertwined with many factors. Traditionally, ...
Since its inception, the choice modelling field has been dominated by theory-driven modelling approa...
Artificial Intelligence in form of Machine Learning classifiers is increasingly applied for travel c...
For decades, Discrete Choice Models (DCMs) have been used to describe, understand and predict human ...
Every day, decision-makers make choices among finite and discrete sets of alternatives. For example,...
This paper discusses important developments in discrete choice modeling for transportation applicati...
In recent decades, transportation planning researchers have used diverse types of machine learning (...
Research in the field of artificial intelligence systems has been exploring the use of artificial ne...
The continuous progress of machine learning has introduced numerous powerful classifiers that are ex...
The analysis of travel mode choice is an important task in transportation planning and policy making...
Understanding and predicting traveller behaviour remains a complex activity. The set of tools in com...
The continuous progress of machine learning has introduced numerous powerful classifiers that are ex...
This article evaluates dynamic driver behaviour models that can be used, in the context of intellige...
As travellers are faced with an increasing portfolio of transportation options, researchers are simi...
Discrete choice models have been in use for decades for mode choice and route choice analysis. These...