License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.51
URN: urn:nbn:de:0030-drops-93793
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9379/
Go to the corresponding LIPIcs Volume Portal

Morton, April ; Piburn, Jesse ; Nagle, Nicholas

Need A Boost? A Comparison of Traditional Commuting Models with the XGBoost Model for Predicting Commuting Flows (Short Paper)

pdf-format:
LIPIcs-GISCIENCE-2018-51.pdf (0.4 MB)

Abstract

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.

BibTeX - Entry

@InProceedings{morton_et_al:LIPIcs:2018:9379,
  author =	{April Morton and Jesse Piburn and Nicholas Nagle},
  title =	{{Need A Boostl A Comparison of Traditional Commuting Models with the XGBoost Model for Predicting Commuting Flows (Short Paper)}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{51:1--51:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9379},
  URN =		{urn:nbn:de:0030-drops-93793},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.51},
  annote =	{Keywords: Machine learning, commuting modeling}
}

Keywords: Machine learning, commuting modeling
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI