- Tytuł:
- Mode choice analysis of school trips using random forest technique
- Autorzy:
-
D'Cruz, Jinit J.M.
Alex, Anu P.
Manju, V. S. - Powiązania:
- https://bibliotekanauki.pl/articles/2173923.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
city transport
Multinomial Logit model
analysis of the travel model
school trips
peak hour traffic
commuting to schools
transport miejski
analiza trybu przejazdów
przejazdy do szkół
ruch w godzinach szczytu - Opis:
- Mode choice analysis of school trips becomes important due to the fact that these trips contribute to the second largest share of peak hour traffic. This scenario is more relevant in India, which has almost 265 million students enrolled in different accredited urban and rural schools of India, from Class I to XII as per the UDISE report of 2019-20. Thus, it becomes necessary to understand what mode of transport will be mostly used for school trips in order to design an efficient transportation system. Modal attributes and socio-economic characteristics are mostly considered as explanatory variables in travel mode choice models. Multinomial Logit (MNL) model is one of the classic models used in the development of mode choice models. These logistic regression models predict outcomes based on a set of independent variables. With the recent advances in machine learning, transportation problems are getting a wide arena of methods and solutions. Among them the method of ensemble learning is finding a prominent place in contemporary modelling. This study explores the potential of using ensembles of random decision trees in mode choice analysis by Random Forest Technique with a comparative analysis on conventional method. It was observed that Random Forest method outperforms MNL method in predicting the mode choice preference of students. The high accuracy of machine learning models is mainly due to its ability to consider complex nonlinear relationship between socio-economic attributes and travel mode choice. These models can learn and identify pattern characteristics extracted from sample data and form adaptive structures through computational process thereby offering insights into the relationships between variables that random utility models cannot recognize. This study considered activity -travel information, personal data and household characteristics of students as attributes for model development and observed that the age of the student and distance of school from home plays a significant role in deciding the mode choice of school trips.
- Źródło:
-
Archives of Transport; 2022, 62, 2; 39--48
0866-9546
2300-8830 - Pojawia się w:
- Archives of Transport
- Dostawca treści:
- Biblioteka Nauki
Artykuł