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On the use of machine learning techniques and discrete choice models in mode choice analysis

Benjdiya Ouissam, Rouky Naoufal, Benmoussa Othmane, Fri Mouhsene

LogForum

2023

Vol. 19, no. 3

331--345

Background: The mode choice stage is a critical aspect that transportation experts rely on to develop a robust transportation system for a particular region. Various techniques are utilized to model mode choice behavior, including Discrete Choice Models (DCMs) and Machine Learning (ML) techniques. However, existing reviews typically focus on either DCMs or ML techniques, and reviews that cover both categories often concentrate on one category while merely mentioning some techniques from the other. This paper aims to address this gap by examining the principal DCMs and ML techniques published over the past four years, differentiating between models based on the granularity level, namely aggregate and disaggregate models. Additionally, a comprehensive discussion is conducted on the accuracy of the different models used in the reviewed articles. Methods: This paper provides a thorough and enhanced analysis of travel mode choice models and analysis techniques used in articles published on "ScienceDirect" from 2020 to 2023. To ensure a comprehensive coverage of the subject, a meticulous search strategy was employed, utilizing targeted keywords. As a result, a total of 38 articles were carefully selected for detailed examination and analysis. Results: The findings of this study highlight the suitability of different modeling approaches for varying levels of analysis. Discrete Choice Models demonstrate effectiveness in aggregate-level analyses, whereas Machine Learning Techniques prove more appropriate for disaggregate-level analyses. Moreover, the study suggests that employing hybrid models can potentially yield a promising solution to attain enhanced prediction accuracy without compromising interpretability. Conclusions: The examination of selected articles revealed several key points. Firstly, there is a concentration of studies on travel mode choice in European countries, China, and the USA, indicating a need for more research in developing countries. Secondly, the reviewed articles often lack in-depth analysis of individual behavior and fail to consider external factors like weather or seasons when employing disaggregate models. Thus, future studies should leverage technological advancements and explore new factors influencing mode choice behavior. Additionally, there is a need for further research on hybrid models that combine Discrete Choice Models (DCMs) with Machine Learning (ML) techniques or deep learning approaches. This research can provide guidance for practitioners unfamiliar with these methods and aid in the design of effective transportation policies. Lastly, considering the variety of models available, it is crucial to understand the extent to which these models can be generalized to different contexts, emphasizing the importance of studying model applicability and generalizability in diverse settings.

Model wyboru środka transportu w dojazdach do i z pracy w Warszawie

Kucharski R., Kulpa T., Szarata A.

Transport Miejski i Regionalny

2016

nr 8

20--25

Artykuł przedstawia model wyboru środka transportu przez podróżnych. Zaproponowano model wyboru dyskretnego: dwumianowy model logitowy, który określa prawdopodobieństwo wyboru w danej sytuacji jednego z dwóch rozważanych środków transportu (nazywanych dalej opcjami): komunikacji zbiorowej (KZ) i indywidualnej (KI). Sytuację, w której dokonywany jest wybór, opisuje odpowiednio zdefiniowana dla każdej z opcji użyteczność obejmująca m.in. dostęp do samochodu, czas przejazdu, liczbę przesiadek, częstotliwość kursowania. W artykule przetestowano kilka postaci modelu i oceniono ich dopasowanie do wyników prawie 7 tysięcy podróży do i z pracy, zbadanych w Warszawskim Badaniu Ruchu 2015 (WBR 2015). Parametry modelu szacowano przy użyciu pakietu do kalibracji modeli dyskretnych BIOGEME (Bierlaire, 2003), który szukał postaci gwarantującej największą zgodność modelu z faktycznymi wyborami podróżnych. Zaproponowano formuły o najwyższej zgodności uzyskanej przy użyciu zmiennych dostępnych w modelu i łatwo prognozowalnych. Efektem jest model, który objaśnia kiedy i dlaczego w dojazdach do pracy w Warszawie wybierana jest komunikacja zbiorowa, a kiedy indywidualna.

In the article the mode-choice model is formulated for the Home-Work-Home trips of Warsaw commuters. Model is calibrated to match the results of the Warsaw Traffic Study (WBR2015) where almost 7 thousands such trips were reported. The Traffic Study revealed the journey diary of the trips conducted the day before. Data included trip purpose, traveler status, car availability, origin and destination and the chosen mode. Which, coupled with the modeled data for the unchosen alternatives, allowed to formulate and calibrate the discrete mode-choice model. We propose several formulations of the binomial logit model and analyzed the fit. We obtained model reasonably explaining the mode-choice behavior when using the context variables (age, car availability), and trip-related variables (travel time, parking, service frequency etc.) To parameterize the model we use BIOGEME, broadly used to estimate the discrete-choice binary models. The article is concluded with the final form of the model, further used in the traffic demand model.