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 jurisdictional 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 characteristics 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 affinity 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 showed that the latter’s classification accuracy never exceeds 60% when an arbitrary radius or metric is used. Moreover, we correlated the jobs-residence ratio with the average commuting time–cost and confirmed a positive effect of JHB, which, however, also varies dramatically with different search radius. Additionally, we conducted an overlay analysis of the MSIC and PDC patterns and obtained eight joint-patterns as well as spatial divisions which contributes effectively to our understanding of the commuting issues in Shanghai.
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Yan, L., Wang, D., Zhang, S. et al. Evaluating the multi-scale patterns of jobs-residence balance and commuting time–cost using cellular signaling data: a case study in Shanghai. Transportation 46, 777–792 (2019). https://doi.org/10.1007/s11116-018-9894-3
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DOI: https://doi.org/10.1007/s11116-018-9894-3