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Article: Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework
Title | Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework |
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Authors | |
Keywords | mobility mobile phone data Commuting planning support systems land use-transportation interaction models |
Issue Date | 2020 |
Citation | Environment and Planning B: Urban Analytics and City Science, 2020, v. 47, n. 8, p. 1440-1455 How to Cite? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/301786 |
ISSN | 2023 Impact Factor: 2.6 2023 SCImago Journal Rankings: 0.929 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yang, Tianren | - |
dc.date.accessioned | 2021-08-19T02:20:44Z | - |
dc.date.available | 2021-08-19T02:20:44Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Environment and Planning B: Urban Analytics and City Science, 2020, v. 47, n. 8, p. 1440-1455 | - |
dc.identifier.issn | 2399-8083 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301786 | - |
dc.description.abstract | In 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.language | eng | - |
dc.relation.ispartof | Environment and Planning B: Urban Analytics and City Science | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | mobility | - |
dc.subject | mobile phone data | - |
dc.subject | Commuting | - |
dc.subject | planning support systems | - |
dc.subject | land use-transportation interaction models | - |
dc.title | Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1177/2399808320924433 | - |
dc.identifier.scopus | eid_2-s2.0-85084850805 | - |
dc.identifier.volume | 47 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 1440 | - |
dc.identifier.epage | 1455 | - |
dc.identifier.eissn | 2399-8091 | - |
dc.identifier.isi | WOS:000533119500001 | - |