File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework

TitleUnderstanding commuting patterns and changes: Counterfactual analysis in a planning support framework
Authors
Keywordsmobility
mobile phone data
Commuting
planning support systems
land use-transportation interaction models
Issue Date2020
Citation
Environment and Planning B: Urban Analytics and City Science, 2020, v. 47, n. 8, p. 1440-1455 How to Cite?
AbstractIn order to contain commuting distance growth and relieve traffic burden in mega-city regions, it is essential to understand journey-to-work patterns and changes in those patterns. This research develops a planning support model that integrates increasingly available mobile phone data and conventional statistics into a theoretical urban economic framework to reveal and explain commuting changes. Base-year calibration and cross-year validation were conducted first to test the model’s predictive ability. Counterfactual simulations were then applied to help local planners and policymakers understand which factors lead to differences in commuting patterns and how different policies influence various categorical zones (i.e. centre, near suburbs, sub-centres and far suburbs). The case study of Shanghai shows that jobs–housing co-location results in shorter commutes and that policymakers should be more cautious when determining workplace locations as they play a more significant role in mitigating excessive commutes and redistributing travel demand. Furthermore, land use and transport developments should be coordinated across spatial scales to achieve mutually beneficial outcomes for both the city centre and the suburbs. Coupled with empirical evidence explaining commuting changes over time, the proposed model can deliver timely and situation-cogent messages regarding the success or failure of planned policy initiatives.
Persistent Identifierhttp://hdl.handle.net/10722/301786
ISSN
2023 Impact Factor: 2.6
2023 SCImago Journal Rankings: 0.929
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYang, Tianren-
dc.date.accessioned2021-08-19T02:20:44Z-
dc.date.available2021-08-19T02:20:44Z-
dc.date.issued2020-
dc.identifier.citationEnvironment and Planning B: Urban Analytics and City Science, 2020, v. 47, n. 8, p. 1440-1455-
dc.identifier.issn2399-8083-
dc.identifier.urihttp://hdl.handle.net/10722/301786-
dc.description.abstractIn order to contain commuting distance growth and relieve traffic burden in mega-city regions, it is essential to understand journey-to-work patterns and changes in those patterns. This research develops a planning support model that integrates increasingly available mobile phone data and conventional statistics into a theoretical urban economic framework to reveal and explain commuting changes. Base-year calibration and cross-year validation were conducted first to test the model’s predictive ability. Counterfactual simulations were then applied to help local planners and policymakers understand which factors lead to differences in commuting patterns and how different policies influence various categorical zones (i.e. centre, near suburbs, sub-centres and far suburbs). The case study of Shanghai shows that jobs–housing co-location results in shorter commutes and that policymakers should be more cautious when determining workplace locations as they play a more significant role in mitigating excessive commutes and redistributing travel demand. Furthermore, land use and transport developments should be coordinated across spatial scales to achieve mutually beneficial outcomes for both the city centre and the suburbs. Coupled with empirical evidence explaining commuting changes over time, the proposed model can deliver timely and situation-cogent messages regarding the success or failure of planned policy initiatives.-
dc.languageeng-
dc.relation.ispartofEnvironment and Planning B: Urban Analytics and City Science-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectmobility-
dc.subjectmobile phone data-
dc.subjectCommuting-
dc.subjectplanning support systems-
dc.subjectland use-transportation interaction models-
dc.titleUnderstanding commuting patterns and changes: Counterfactual analysis in a planning support framework-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1177/2399808320924433-
dc.identifier.scopuseid_2-s2.0-85084850805-
dc.identifier.volume47-
dc.identifier.issue8-
dc.identifier.spage1440-
dc.identifier.epage1455-
dc.identifier.eissn2399-8091-
dc.identifier.isiWOS:000533119500001-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats