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The impact of catchment areas in predicting bus journeys

Garn, Wolfgang, Turner, Christopher, Kireulishvili, George and Panagi, Vasiliki (2019) The impact of catchment areas in predicting bus journeys In: The Operational Research Society Annual Conference OR61, 2019-09-03-2019-09-05, Canterbury, Kent, UK.

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Abstract

The catchment area along a bus route is key in predicting bus journeys. In particular, the aggregated number of households within the catchment area are used in the prediction model. The model uses other factors, such as head-way, day-of-week and others. The focus of this study was to classify types of catchment areas and analyse the impact of varying their sizes on the quality of predicting the number of bus passengers. Machine Learning techniques: Random Forest, Neural Networks and C5.0 Decision Trees, were compared regarding solution quality of predictions. The study discusses the sensitivity of catchment area size variations. Bus routes in the county Surrey in the United Kingdom were used to test the quality of the methods. The findings show that the quality of predicting bus journeys depends on the size of the catchment area.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
NameEmailORCID
Garn, WolfgangW.Garn@surrey.ac.uk
Turner, Christopherchristopher.turner@surrey.ac.uk
Kireulishvili, Georgeg.kireulishvili@surrey.ac.uk
Panagi, Vasiliki
Date : 30 June 2019
DOI : 10.13140/RG.2.2.21707.34084/1
Related URLs :
Additional Information : Reference for presentation is 0R61A343
Depositing User : Diane Maxfield
Date Deposited : 24 Sep 2019 10:28
Last Modified : 24 Sep 2019 10:30
URI: http://epubs.surrey.ac.uk/id/eprint/852768

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