A high-precision heuristic model to detect home and work locations from smart card data

Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers. Processing and analyzing these data open new opportunities in urban modeling and travel behavior research. This study seeks to develop an accurate framework f...

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Main Authors: Nilufer Sari Aslam, Tao Cheng, James Cheshire
Format: Article
Language:English
Published: Taylor & Francis Group 2019-01-01
Series:Geo-spatial Information Science
Subjects:
Online Access:http://dx.doi.org/10.1080/10095020.2018.1545884
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spelling doaj-699418b9d30c403fa4f5c8bc4bb15e292020-11-24T23:57:11ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532019-01-0122111110.1080/10095020.2018.15458841545884A high-precision heuristic model to detect home and work locations from smart card dataNilufer Sari Aslam0Tao Cheng1James Cheshire2University College LondonUniversity College LondonUniversity College LondonSmart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers. Processing and analyzing these data open new opportunities in urban modeling and travel behavior research. This study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work locations. The model uses journey counts as an indicator of usage regularity, visit-frequency to identify activity locations for regular commuters, and stay-time for the classification of work and home locations and activities. London is taken as a case study, and the model results were validated against survey data from the London Travel Demand Survey and volunteer survey. Results demonstrate that the proposed model is able to detect meaningful home and work places with high precision. This study offers a new and cost-effective approach to travel behavior and demand research.http://dx.doi.org/10.1080/10095020.2018.1545884Smart card dataactivity location modelingheuristic primary location modelhome and work locationshuman mobility patternurban activity pattern
collection DOAJ
language English
format Article
sources DOAJ
author Nilufer Sari Aslam
Tao Cheng
James Cheshire
spellingShingle Nilufer Sari Aslam
Tao Cheng
James Cheshire
A high-precision heuristic model to detect home and work locations from smart card data
Geo-spatial Information Science
Smart card data
activity location modeling
heuristic primary location model
home and work locations
human mobility pattern
urban activity pattern
author_facet Nilufer Sari Aslam
Tao Cheng
James Cheshire
author_sort Nilufer Sari Aslam
title A high-precision heuristic model to detect home and work locations from smart card data
title_short A high-precision heuristic model to detect home and work locations from smart card data
title_full A high-precision heuristic model to detect home and work locations from smart card data
title_fullStr A high-precision heuristic model to detect home and work locations from smart card data
title_full_unstemmed A high-precision heuristic model to detect home and work locations from smart card data
title_sort high-precision heuristic model to detect home and work locations from smart card data
publisher Taylor & Francis Group
series Geo-spatial Information Science
issn 1009-5020
1993-5153
publishDate 2019-01-01
description Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers. Processing and analyzing these data open new opportunities in urban modeling and travel behavior research. This study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work locations. The model uses journey counts as an indicator of usage regularity, visit-frequency to identify activity locations for regular commuters, and stay-time for the classification of work and home locations and activities. London is taken as a case study, and the model results were validated against survey data from the London Travel Demand Survey and volunteer survey. Results demonstrate that the proposed model is able to detect meaningful home and work places with high precision. This study offers a new and cost-effective approach to travel behavior and demand research.
topic Smart card data
activity location modeling
heuristic primary location model
home and work locations
human mobility pattern
urban activity pattern
url http://dx.doi.org/10.1080/10095020.2018.1545884
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