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.

中文翻译：

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了解通勤模式和变化：规划支持框架中的反事实分析

为了遏制通勤距离增长并减轻特大城市地区的交通负担，了解上下班模式及其变化至关重要。本研究开发了一个规划支持模型，将越来越多的可用手机数据和传统统计数据整合到一个理论城市经济框架中，以揭示和解释通勤变化。首先进行了基准年校准和跨年验证，以测试模型的预测能力。然后应用反事实模拟来帮助当地规划者和政策制定者了解哪些因素导致通勤模式的差异以及不同的政策如何影响不同的分类区域（即中心、近郊区、次中心和远郊区）。上海的案例研究表明，工作-住房共处导致通勤时间缩短，决策者在确定工作地点时应更加谨慎，因为它们在缓解过度通勤和重新分配出行需求方面发挥着更重要的作用。此外，土地利用和交通发展应跨空间尺度协调，以实现市中心和郊区的互利结果。再加上解释通勤随时间变化的经验证据，所提出的模型可以提供有关计划政策举措成功或失败的及时且符合情况的信息。此外，土地利用和交通发展应跨空间尺度协调，以实现市中心和郊区的互利结果。再加上解释通勤随时间变化的经验证据，所提出的模型可以提供有关计划政策举措成功或失败的及时且符合情况的信息。此外，土地利用和交通发展应跨空间尺度协调，以实现市中心和郊区的互利结果。再加上解释通勤随时间变化的经验证据，所提出的模型可以提供有关计划政策举措成功或失败的及时且符合情况的信息。