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.
Highlights
Most mega-city regions around the world are suffering from an ongoing increase in both the number of commuting trips and the average passenger kilometres travelled in journeys to work (Aguilera et al, 2009)
The commuting distances derived from the mobile phone data (MPD) and the model both increase with time (Table 3)
It is noteworthy that, while not calibrated, the modelled commute inflow into the centre yields a distance similar to that reflected in the official statistics
Summary
Most mega-city regions around the world are suffering from an ongoing increase in both the number of commuting trips and the average passenger kilometres travelled in journeys to work (Aguilera et al, 2009). Investigating commuting patterns and changes is never an easy task. The causal relationship between changes in urban form and spatial interaction is difficult to quantify due to the complicated mechanisms underlying urban growth (Jun, 2020). This research aims to address these challenges by developing a decision support tool that reveals relationships between commuting pattern changes (bottom-up) and planned urban developments (top-down) using quantifiable evidence. It introduces increasingly available mobile phone data (MPD), as well as conventional statistics, into a theoretical urban economic framework (i.e. recursive spatial equilibrium) to understand causality. The proposed planning support model can be used to conduct counterfactual simulations to identify the key determinants in observed changes and can be further extended to forecast policy outcomes across a variety of hypothetical (‘what-if’) scenarios
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