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Need A Boost? A Comparison of Traditional Commuting Models with the XGBoost Model for Predicting Commuting Flows...

by April M Morton, Jesse O Piburn, Nicholas Nagle
Publication Type
Conference Paper
Book Title
10th International Conference on Geographic Information Science (GIScience 2018)
Publication Date
Page Number
51
Volume
114
Publisher Location
Germany
Conference Name
International Conference on Geographic Information Science (GIScience 2018)
Conference Location
Melbourne, Australia, Australia
Conference Sponsor
RMIT University in collaboration with the University of Melbourne
Conference Date
-

Commuting models estimate the number of commuting trips from home to work locations in a given area. Since their infancy, they have been increasingly used in a variety of fields to reduce traffic and pollution, drive infrastructure choices, and solve a variety of other problems. Traditional commuting models, such as gravity and radiation models, typically have a strict structural form and limited number of input variables, which may limit their ability to predict commuting flows as well as machine learning models that might better capture the complex dynamics of the commuting process. To determine whether machine learning models might add value to the field of commuter flow prediction, we compare and discuss the performance of two standard traditional models with the XGBoost machine learning algorithm for predicting home to work commuter flows from a well-known United States commuting dataset. We find that the XGBoost model outperforms the traditional models on three commonly used metrics, indicating that machine learning models may add value to the field of commuter flow prediction.