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

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.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2]
  1. ORNL
  2. The University of Tennessee, Knoxville
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1468184
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: International Conference on Geographic Information Science (GIScience 2018) - Melbourne, Australia, , Australia - 8/28/2018 8:00:00 AM-8/31/2018 8:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Morton, April M., Piburn, Jesse O., and Nagle, Nicholas. Need A Boost? A Comparison of Traditional Human Commuting Models with the XGBoost Model for Predicting Commuting Flows. United States: N. p., 2018. Web.
Morton, April M., Piburn, Jesse O., & Nagle, Nicholas. Need A Boost? A Comparison of Traditional Human Commuting Models with the XGBoost Model for Predicting Commuting Flows. United States.
Morton, April M., Piburn, Jesse O., and Nagle, Nicholas. Wed . "Need A Boost? A Comparison of Traditional Human Commuting Models with the XGBoost Model for Predicting Commuting Flows". United States. https://www.osti.gov/servlets/purl/1468184.
@article{osti_1468184,
title = {Need A Boost? A Comparison of Traditional Human Commuting Models with the XGBoost Model for Predicting Commuting Flows},
author = {Morton, April M. and Piburn, Jesse O. and Nagle, Nicholas},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Aug 01 00:00:00 EDT 2018},
month = {Wed Aug 01 00:00:00 EDT 2018}
}

Conference:
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