Open AccessModelling of Activity-Travel Pattern with Support Vector Machine
Author(s) -
Anu P. Alex
Publication year - 2021
Publication title -
european transport
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.178
H-Index - 20
eISSN - 2283-5520
pISSN - 1825-3997
DOI - 10.48295/et.2021.82.2
Subject(s) - support vector machine , computer science , machine learning , classifier (uml) , artificial intelligence , mode (computer interface) , decision support system , variation (astronomy) , data mining , operating system , physics , astrophysics
Activity based travel demand modelling involves lot of uncertainty due to the complex and varying decision making behaviour of each individual. This study contributes to the literature by assessing the suitability of Support Vector Machine (SVM) in modelling the activity pattern and travel behaviour of workers. Activity and travel behaviour of workers consists of decision outcomes, which can be modelled as classification and regression problems. SVM is a good classifier and regressor with good testing and learning capability, hence the present study used SVM for modelling. It was found that support vector machine models are well performing to predict the activity pattern and travel behaviour of workers. The SVM models developed in the study predicts the temporal variation of mode wise work activity generation. Prediction of temporal mode share of commuters is advantageous to policy makers to experiment the implementation of temporary Travel Demand Management (TDM) actions effectively.