Evaluating the multi-scale patterns of jobs-residence balance and commuting time–cost using cellular signaling data: a case study in Shanghai

Evaluating the multi-scale patterns of jobs-residence balance and commuting time–cost using... Transportation https://doi.org/10.1007/s11116-018-9894-3 Evaluating the multi‑scale patterns of jobs‑residence balance and commuting time–cost using cellular signaling data: a case study in Shanghai 1 1 1 1 Longxu Yan  · De Wang  · Shangwu Zhang  · Dongcan Xie © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Assessing jobs-housing balance (JHB) and commuting efficiency is crucial to urban and transport planning. However, the scale dependency problem, meaning that metrics may be biased as they rely on an arbitrary search radius or pre-defined jurisdic - tional division, remains unresolved. This paper proposes to apply clustering method to multi-scale indicators for evaluating aggregate patterns of jobs-housing relationship and commuting time–cost. We based our analysis on individual commuters extracted from cell phone data, a reliable substitute for travel surveys. After the commuter origin-destinations are carefully inferred, we draw two types of curves to represent the multi-scale charac- teristics of jobs-residence relationship and commuting efficiency. One is the multi-scale index curve (MSIC) of jobs-residence ratio that measured within multiple scales. Another is the probability density curve (PDC) of commuting time–cost. Then we applied the affin - ity propagation clustering method to the curves and detected six patterns of MSIC and ten of PDC. By comparing our result with the conventional methods we http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Transportation Springer Journals

Evaluating the multi-scale patterns of jobs-residence balance and commuting time–cost using cellular signaling data: a case study in Shanghai

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Economics; Regional/Spatial Science; Economic Geography; Engineering Economics, Organization, Logistics, Marketing; Innovation/Technology Management
ISSN
0049-4488
eISSN
1572-9435
D.O.I.
10.1007/s11116-018-9894-3
Publisher site
See Article on Publisher Site

Abstract

Transportation https://doi.org/10.1007/s11116-018-9894-3 Evaluating the multi‑scale patterns of jobs‑residence balance and commuting time–cost using cellular signaling data: a case study in Shanghai 1 1 1 1 Longxu Yan  · De Wang  · Shangwu Zhang  · Dongcan Xie © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Assessing jobs-housing balance (JHB) and commuting efficiency is crucial to urban and transport planning. However, the scale dependency problem, meaning that metrics may be biased as they rely on an arbitrary search radius or pre-defined jurisdic - tional division, remains unresolved. This paper proposes to apply clustering method to multi-scale indicators for evaluating aggregate patterns of jobs-housing relationship and commuting time–cost. We based our analysis on individual commuters extracted from cell phone data, a reliable substitute for travel surveys. After the commuter origin-destinations are carefully inferred, we draw two types of curves to represent the multi-scale charac- teristics of jobs-residence relationship and commuting efficiency. One is the multi-scale index curve (MSIC) of jobs-residence ratio that measured within multiple scales. Another is the probability density curve (PDC) of commuting time–cost. Then we applied the affin - ity propagation clustering method to the curves and detected six patterns of MSIC and ten of PDC. By comparing our result with the conventional methods we

Journal

TransportationSpringer Journals

Published: Jun 1, 2018

References

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